High-Tech

Simulate Electrostatic Discharge on a Virtual Twin

Thu, 14 Nov 2024 17:51:46 GMT

ESD contact discharge is usually tested in the lab by physically connecting a high-voltage probe to a prototype of the device under test (DUT) at a specific location, such as a connector pin. Additionally, a non-contact test is carried out by placing the probe at varying distances from likely discharge points on the DUT. These tests must be repeated for every possible connection & discharge path, making ESD testing time-consuming and expensive. It can also be difficult to understand the root causes of failure from the limited output of an ESD test. Many prototypes can be damaged or destroyed in the testing process. Virtual testing with simulation can accelerate ESD analysis, reducing development time and cost. Both the test equipment and the DUT are created and connected in the virtual environment, with waveforms generated to replicate the real-world tests. 3D visualization of simulation results shows the exact path of currents through the device, helping engineers understand the causes of ESD problems and develop design mitigation strategies. Challenges of ESD Analysis and Mitigation ESD is a significant risk for electronic devices: Sensitive low-voltage electronic components such as integrated circuits (ICs) can easily be damaged by high-voltage pulses. Even if catastrophic damage does not occur, permanent damage may occur, reducing the life of the device or cause unexpected behavior. ESD occurs when a static electric charge is generated on an object and then discharges to earth via an electronic device. This often happens if a human user picks up an electric charge from friction with their clothing, the floor or furniture, and can also come from moving machinery such as vehicles or conveyor belts or from electrostatic induction from other charged objects. ESD test of a smartphone, showing the propagation of currents from discharge at the charging port For ESD to occur, there must be a path between the charged object and the ground through the device. This path can be either direct contact or close proximity contact in which case an arc through the air gap will form. In dry air, static charges can build up more readily, with faster rise times, and higher peak amplitudes, while in humid air, non-contact discharge can form with longer arcs, more slowly rising with lower peak amplitude. The large number of variables in conditions, contact type and arc type mean that analyzing ESD risk requires a long series of physical tests on a significant number of expensive prototypes. Tests have to occur late in the product cycle when detailed prototypes are available. If issues during ESD testing are found, there are a number of steps required that may cause delays and impact the scheduled release date. Steps include understanding the causes of any failure and redesign and rework of the device to mitigate the issue for the next prototype. ESD testing on modern devices can be particularly complicated as additional components are now integrated into a single device, such as a system on chip (SiP). The ESD protection for each individual IC must be harmonized with the system-level ESD protection (SEED). Ensuring safe voltages and currents at all ICs can require trade-offs in terms of component placement, informed by a large number of tests. Benefits of Electrostatic Discharge Simulation Simulation offers a faster alternative to testing. Unified modeling and simulation (MODSIM) makes it easy to turn CAD geometry data into a simulation-ready virtual prototype of the DUT and the test equipment set-up. An excitation at the tip of the virtual generator creates an ESD pulse, and the simulation calculates its propagation through the device. 3D field monitors can visualize the exact path of currents flowing through the device, and virtual field probes can be placed anywhere in the virtual space, including inside the device. Surface currents in an ESD test on an Ethernet system, comparing two different diode placements. Evaluating ESD susceptibility virtually means that problems can be identified and resolved even before construction of the first prototype. This approach enables engineers to get the design right first time, saving on prototyping costs and reducing the risk of project delays caused by issues discovered during testing. The SIMULIA ESD Simulation Solution CST Studio Suite contains state-of-the-art electromagnetic solvers for simulating complex components and systems accurately and efficiently. ESD is inherently a transient phenomenon, and it can be simulated effectively using the 3D Time Domain Solver. The Time Domain Solver also offers highly efficient 3D meshing that is robust enough to deal with poor CAD geometry. Complex geometries are represented accurately and virtual tests can be run rapidly. Design of Experiments (DoE) capabilities allow automated testing of various scenarios with clear visualization and comparison of large datasets. The ESD susceptibility analysis workflow starts with building the DUT’s virtual twin. 3D geometry and PCB or IC layouts from standard CAD and EDA tools can be imported into the virtual environment and converted into simulation-ready models with automatic clean-up and meshing. Pre-defined ESD-specific templates, such as 3D models of ESD generators and ESD pulse excitations, available from the CST component library, help users define their simulation set-up. The ESD generator models have been developed and validated to comply with international standards such as ISO 10605 and can accurately model real-world ESD test set-ups. Users can build representations of standard tests that replicate the set-up found in the lab. ESD simulation representing a test set-up as defined by ISO 10605. Both contact and air-gap ESD generators can be simulated. In the case of an air gap (in other words, where voltage breakdown in the air causes an arc, an electromagnetic simulation is first performed to calculate possible arc paths at different voltages using Paschen’s law, and to generate a SPICE model that represents the non-linear electromagnetic properties of the arc. This then forms the basis for the time domain simulation of the ESD pulse, with true transient co-simulation combining the SPICE circuit model with the 3D simulation model. Close up of the ESD simulation set up, showing the DUT on the left and the ESD gun probe on the right, with an element representing the spark. The simulation produces several ESD KPIs and a 3D visualization of electromagnetic fields and surface currents around the device. Using virtual probes, users can view voltages at any point in the structure throughout the duration of the pulse. Users can generate all the KPIs that a physical test would provide, and some that would be impossible to measure. Conclusion Electrostatic discharge (ESD) can cause errors and device failures and affect the safety and reliability of a device market. Successfully bringing an electronic product to market means meeting legal ESD regulations and ensuring that a device is protected from ESD exposure. Electromagnetic simulation can be used to analyze ESD risk on a device without the cost of building and potentially destroying physical prototypes. A virtual twin contains all relevant data about the product and can be used to accurately represent product behavior in a virtual test using simulation. Engineers can quickly build a virtual twin of their product from the design data thanks to the unified modeling and simulation (MODSIM) approach enabled by Dassault Systèmes tools on the 3DEXPERIENCE platform. With virtual testing, engineers can understand ESD risk from the earliest stages of design and can develop protection and mitigation strategies for individual components or the entire device. Virtual testing helps prevent problems being discovered late in development when rework is expensive and risks delaying the entire project. It also reduces the risk of device failures after launch and the cost of recalls. Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts? The SIMULIA Community is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.

3D EM and Circuit Co-Simulation of a DC-DC Converter with Partially Saturated Magnetic Material

Wed, 02 Oct 2024 13:54:03 GMT

Background The 3D EM and circuit co-simulation of a switching mode power supply, such as a DC-DC converter, involves a 3D model and a circuit model. The 3D model is simulated with CST Microwave Studio (CST MWS) and the components, typically in SPICE format, are connected with the 3D model inside the circuit schematic, CST Design Studio. This approach provides an accurate system response, but the field distribution can’t be correctly modeled using the SPICE. Especially, to model the magnetic field distribution of the inductor that can only be modeled using a 3D inductor model. In addition, when the output current of the DCDC converter increases, the current at the inductor also increases. The further increment of the DC current at the inductor will lead to (partial) magnetic saturation and results in the decrease of the inductance value. 3D EM and Circuit Co-simulation The first step for co-simulation is to import the 3D model of the PCB into CST MWS. The component connections are modeled using discrete ports. Each of the discrete ports is excited and the S-parameter results are available after the 3D simulation. Figure 1 shows the PCB model and the discrete ports. Figure 1. PCB model of DC-DC converter with discrete port connections After that, the circuit components such as R, L, C, diode and transistor are connected in the schematic with the CST MWS block, which contains the PCB parasitic information. The electric behavior of the passive circuit components can be represented either using a SPICE model or a touchstone model. For the active circuit components, a SPICE model is required. The complete connection of the circuit components and the CST MWS block can be seen in Figure 2. Figure 2. Co-simulation circuit schematic of DC-DC boost converter with MWS block As mentioned earlier, to accurately model the field radiation of the power inductor in the simulation, the 3D model of the coil must be considered. The material of the inductor body is modeled using the Debye 1st-order magnetic dispersion model with a static permeability of 125. Figure 3 shows the 3D model of the power inductor inside CST MWS. After that, it is placed on the PCB using the import subproject feature as shown in Figure 4 and then simulated. Figure 3. 3D model of the power inductor Figure 4. 3D power inductor connection to DC-DC boost converter in 3D MWS To visualize the difference in the magnetic field radiation, we compare the magnetic field plot of the circuit modeling of the power inductor with a discrete port to the 3D inductor model (Figure 5). Figure 5. Magnetic field comparison between 3D model and discrete port power inductor model Similarly, we can also observe the magnetic field strength differences using the near-field probe. In contrast to the near-field monitor, the near-field probe provides a wideband result. The probe is placed 10 mm above the PCB. Figure 6 shows an H-field comparison between the 3D inductor model and the circuit-modeled power inductor. Figure 6. H-field probe comparison between 3D model and discrete port power inductor model Measuring the magnetic field strength further away from the PCB shows almost no difference between the two approaches. The blue regions, as illustrated in Figure 5, denote the areas where the magnetic field strength becomes less pronounced as we move further away from the PCB. Modeling of Partially Saturated Magnetic Material In practical applications for boost converters, when power inductors are subjected to high DC input currents, the magnetic material reaches a state of saturation, which leads to a change in its relative permeability. The saturation effect of the magnetic material in the simulation is described with the non-linear behavior of the initial magnetization B-H curve. The B-H curve information can be obtained either from the component vendor or it is described using an analytical formulation. In this blog, we use the material definition with the analytical formulation, which is accessible in CST Studio Suite under VBA Macros –> Materials –> Create Analytical Soft-Magnetic B (H). The interface of this macro is illustrated in Figure 7. This macro is only visible in a low-frequency CST Studio Suite project. Therefore, make sure to switch to the low-frequency project type if your current CST Studio Suite project is of a high-frequency (HF) type. The initial permeability, the saturation magnetization and the tuning parameter values are the main material input definitions, and they are automatically created as parameters and listed in the parameter list window. The tuning parameter value controls the slope of the B-H curve in the saturation region and, by default, the value is two. If a known point of the B-H curve is used, the tuning parameter value is automatically calculated based on it. Figure 7. Analytical soft-magnetic B (H) definition For this particular example, the initial permeability is 125. As no further material information is available, the tuning parameter and saturation magnetization are initially defined with their default value. These two parameters are tuned based on the DC saturation current information from the vendor’s datasheet, which leads to a 20% reduction of the initial inductance value. The inductance value is evaluated using the magneto-static (MS) solver. The MS solver calculates both inductance values, the apparent- and incremental inductance matrix. Because of the non-linearity of the magnetic material, the inductance value is obtained from the incremental inductance matrix. In Figure 8, we illustrate three different spatial distributions of the permeability at the inductor body. First, with low DC current amplitude, without saturation, we can clearly see that the initial permeability is homogenously distributed over the inductor body. As the DC current increases, in this example to approximately 2.8 A, the magnetic material is partially saturated and we can observe the reduction of the permeability mainly at the center of the coil. If we now further increase the DC current, in this case to approximately 8A, the magnetic material saturation increases and the inductance decreases by 50% of its initial value. The permeability inside the coil is now considerably reduced. Figure 8. Relative permeability plot for different saturation cases Simulation Workflow to Consider the Saturation Effect The simulation workflow can be described in the following steps: Modeling of the inductor soft magnetic material with the non-linear behavior BH curve. (See the previous section) Creating a simulation project using “Biased Ferrite-EM coupling” in CST Design Studio. This creates automatically two coupled simulation projects, M-static and EM1 (see Figure 9). The M-Static project calculates the biasing field around the 3D inductor model using the MS solver. The fields are automatically exported to the EM1 project. The EM1 project is a high-frequency project, which consists of:PCB model of a DC-DC converter (must be imported manually) 3D inductor model and fields from the M-Static project. Circuit definition of the converter and transient task simulation for co-simulation. Figure 9. Coupled simulation with biased ferrites in CST Design Studio For the M-static simulation, the DC current is defined as excitation. This DC current corresponds to the input current of the boost converter and it can be approximated with the following equation: η is the converter efficiency, which can be assumed 90%. The input- and output voltage, as well as the output current, are the converter operating parameters. For this example, the boost converter operates with an input voltage of 12 V and delivers an output voltage of 19 V. The output of the converter is connected to a 12-ohm resistor, representing a static load, which results in an output current of around 1.6 A. The switching frequency is fixed at 1.25 MHz with 35% duty cycle. For the high-frequency simulation, EM1 project, the 3d PCB model is imported from the ODB++ layout format. After that, the 3D inductor model is placed on the PCB. The other end of the inductor is connected to the port (in this example number 7). This connection is not necessary but very useful because we can monitor the switching voltage and current through this inductor. The connection of the inductor with the PCB is shown in Figure 10. Figure 10. Connection of the 3D inductor model to the PCB through port 7 To perform the co-simulation, the circuit connections are to be defined in the schematic of the EM1 project. The circuit schematic connection is similar to the one shown in Figure 2 but without the presence of the inductor SPICE model. This is obvious, because the inductor now has been modeled with 3D model. The port number 7 is directly shorted with the GND symbol to establish the electrical connection with the PCB. The probe “Power Inductor” is placed on that connection to record the current and voltage at the inductor. The Figure 11 shows the schematic connection at the pin 7. Figure 11. Schematic connection of the pin 7 with probe defined With the transient task simulation, we can now perform the complete system simulation of the converter. In case the load current increases, we must calculate again the input current with the above equation, and repeat the simulation of the M-static- and EM1 project. Simulation Results The switching current at the power inductor can be monitored in CST Design Studio using a probe, as shown in Figure 11. The increase of the DC current flowing into the inductor leads to magnetic material saturation, which reduces the relative permeability of the magnetic material from its initial value, thus reducing the inductance value. As the inductance value decreases, a higher current ripple at the inductor can also be observed. This can be seen in Figure 12, where the current ripple is compared with the case without saturation. The current ripple is observed for a single period at the switching frequency in steady state. The saturation case is simulated using a 2.8A DC input current. Figure 12. Power inductor current with and without saturation We can see that in the case when the magnetic material has not reached saturation, the observed power inductor current exhibits a ripple with a peak-to-peak of around 265 mA. However, when the magnetic saturation is considered, the observed power inductor current exhibits a ripple with a higher peak-to-peak of approximately 330 mA. To examine whether the current ripple influences the conducted emission result, we can compare the current spectrum at the Line Impedance Stabilization Network (LISN). This is depicted in Figure 13. We can see that there is only 1 dBmA increment in a partially saturated case (only a 20% reduction of the initial inductance value) and approximately 5dBuA increment for a higher saturation case (for example, 50% reduction of the initial inductance value). This concludes that the saturation effect on the power inductor in this converter example has only a small impact on the conducted emission. Nevertheless, it is important to choose the right inductor with proper current rating to avoid saturation. In addition, it’s important to note that if the saturation effect were considered in the EMI filter components, the impact on EMC performance would become more pronounced. Figure 13. Frequency domain currents at the LISN Conclusion In this blog, a co-simulation workflow considering the saturation effect of magnetic material on a boost converter has been shown. The workflow is realized by establishing a coupled simulation between the magneto-static solver and the CST MWS frequency domain solver. In the example, the power inductor is subjected to different DC current amplitudes to show the saturation effect. The current ripple at the inductor increases as the power inductor saturates. A similar workflow can be applied to EMI filter components, where the saturation can show more impact on the EMC performance Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts? The SIMULIA Community is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.

The 3DEXCITE PIONEERS Lodge: A Virtual Hub for Learning, Collaboration, and Fun

Fri, 27 Sep 2024 13:38:44 GMT

Hi, I’m Matts Verkest, currently working as a Industry Process Consultant (IPC) at 3DEXCITE. My journey at 3DS began in 2019, starting as a 3D Artist intern until I officially joined the Production team as 3D Artist in September of that year. The talented team I was a part of primarily focused on creating marketing visuals for automotive companies with 3DEXCITE DELTAGEN. Here, I learned the ropes of creating engaging visuals and collaborating with agencies and clients alike. I also gained experience in supervision, taking the lead, and handling client communication directly. Within my role as a Creative, taking initiative and exploring tools and new ways to develop cutting-edge solutions for our clients was not only encouraged but also sparked my growing interest to make a meaningful impact outside of creating nice eye-candy. As I delved deeper into these projects, I was introduced to the PIONEERS initiative, which opened up new dimensions of the 3DEXPERIENCE platform. This allowed me to engage more actively in the community features and utilize its 3D tools for the first time, also broadening my exposure to diverse clients and industries. Through hands-on experience and navigating the 3DEXPERIENCE platform, I gained valuable insights and expertise in collaborating closely with clients across different sectors. This included conducting workshops and training sessions – both online as well as onsite. This journey ultimately led me to my current role as IPC, with the help of Els Van Langenhove as my former team lead in an intermediate role. Under supervision of Tim Rau, this is where I now operate at the intersection of Sales, Production, and R&D as a Tech-Sales professional within an awesome global team with very talented people from a wide range of backgrounds. In this capacity and environment, I thrive on experimenting, providing feedback, offering advice, coordinating efforts, and sharing knowledge. In other words, challenging the status-quo. Recognizing the importance of spreading knowledge in an engaging manner while continually learning from within and beyond our organization, I started working on an initiative. Unsure on how to build it, I was driven by a passion to foster knowledge sharing, enhance visibility, and promote teamwork on a global scale. I wanted to dismantle barriers between brands and continents, creating a platform that celebrates learning, encourages collaboration, and strengthens connections across our entire organization and beyond. As it turned out, this perfectly aligned with the vision of the PIONEERS initiative. Therefore, when I discussed this with Stefan Radauscher, who leads the PIONEERS initiative, two ideas converged, giving birth to The Lodge – a PIONEERS extension The Lodge opened its doors! Over the past year, we have embarked on an exciting journey with The Lodge, a vibrant community initiative designed to foster learning, collaboration, and fun challenges within our company. As an extension of the 3DEXCITE PIONEERS initiative, The Lodge aims to cultivate innovation and shape the workforce of the future by spreading knowledge and onboarding people onto our diverse portfolio – even across different brands! What is The Lodge? The Lodge serves as a virtual hub where employees from all roles, brands, and experience levels can come together to share knowledge, participate in engaging challenges, and attend insightful webinars. Our goal is to break down silos and create a more interconnected and collaborative company culture. By tapping into teams and offices worldwide, including our experience centers, we are building a community that is well-versed in our company’s extensive and varied global brand portfolio. Monthly Challenges and Webinars Each month, The Lodge hosts a new challenge alongside a series of webinars that are open to everyone. These activities are designed to spread knowledge, promote collaboration, and help employees get to know the wide world of our company better. With each challenge, there’s are also prizes to be won among which as head prize Dinner On the Boss: a dinner up to 100 euros paid by Tom Acland, CEO of 3DEXCITE. We greatly appreciate our CEO’s open support for this initiative, not only by presenting the main prize but also by promoting it through his channels. As a result, you may occasionally see Tom featured in some of the visuals, such as during this BBQ event: Here’s a look at some of the exciting challenges and webinars we’ve hosted since the launch of The Lodge. Note that certain challenges have been extended, which has occasionally adjusted our monthly schedule: Challenge Highlights We aim to offer engaging challenges that align with each month’s theme or events, as well as industry-related trends. These challenges are thoughtfully crafted to be both fun and educational, encouraging participants to explore new skills and ideas. By integrating relevant themes and industry insights, we ensure that our challenges are timely, meaningful, and impactful, fostering growth and innovation within our community. April 2023 – Challenge 001 Artistry VS. Algorithms The goal of the first challenge was to create visually appealing posters that highlight the brand’s portfolio disciplines. These posters needed to effectively communicate the key elements of those disciplines in an engaging and informative manner. However, there was a twist! The challenge was open to both individuals and teams, who could use any software or tools they preferred, including AI. This set up an intriguing competition between traditional (digital) artistry and cutting-edge algorithms. May 2023 – Challenge 002 The Generative AI Explosion Contestants were tasked to design the key visual with AI tools for 3DEXCITE Live’s next event about “The Generative AI Explosion”. To support contestants in this challenge, three training sessions on AI were provided to equip them with the necessary skills and knowledge. September 2023 – Challenge 003 E-MTB X-Render Challenge In this challenge, participants created two stunning renders of an e-mountain bike based on a selected reference image from a curated collection of ten made with an AI model. They used the provided bike model and created two renders using different methods: the 3DEXPERIENCE Platform and any software of their choice (e.g. Unreal Engine, Blender, Deltagen). This comparison highlighted differences between our proprietary software and competitors. It was valuable for our R&D team, providing insights into our software’s strengths and user needs. The challenge included a training session on the 3DEXPERIENCE Platform for rendering techniques and a shared post explaining how the reference images were created using AI with a synthetic data model. And the challenge concluded by hosting a head to head social media contest to decide the winners! March 2024 – Challenge 004 Earth Day Challenge In celebration of Earth Day (April 22), we held a challenge inviting participants to use their creativity and design skills to craft digital creations embodying environmental awareness and sustainability. They created ‘visuals’ addressing specific prompts, using any software, tool, or medium—whether a VR experience, video, or (AI-generated) image. The theme for that year’s Earth Day was Planet Vs. Plastics, which served as the inspiration for the challenge. June 2024 – Challenge 006 Timeless Creations Challenge Our sixth challenge in our series has also officially begun! In anticipation of the World of High Precision event (June 11-14, Geneva, Switzerland), we invite people to create high-quality watch renders inspired by the following themes: Pride Month (June), World Oceans Day (June 8th), International Yoga Day (June 21st), World Music Day (June 21st) and Paris Air Show (June 16-22, Paris, France). July 2024 – Challenge 007 Summer in Space Challenge To celebrate World UFO Day (July 2nd), the Moon Landing Anniversary (July 20th), and the AIAA Aviation and Aeronautics Forum (July 29th – August 2nd), we launched a challenge inviting participants to create a visual depiction of “summer in space.” The task encouraged creativity in showcasing the beauty of celestial landscapes and extraterrestrial adventures, with the option to infuse humor. Whether illustrating present-day space exploration or envisioning future space colonies, participants could submit solo or team entries using any preferred software or tools. Webinar Highlights We strive to offer engaging webinars that complement our monthly challenges and themes. Each webinar is carefully designed to provide valuable insights, practical skills, and inspiration that align with the specific focus of the month. By doing so, we ensure that participants are well-equipped and motivated to tackle the challenges, fostering a deeper understanding and stronger community connection. And with it, we provide a platform for those who wish to present, offering them an opportunity to gain more visibility. Building a Brighter Future Together The Lodge has been a slowly growing success, bringing together employees from all corners of our company to learn, collaborate, and have fun. By participating in the challenges and attending the webinars, employees are not only expanding their knowledge but also contributing to a more innovative and interconnected company culture. As we move forward, we remain committed to making The Lodge an even more dynamic and engaging platform. We invite everyone from Dassault Systèmes to join us in this exciting journey, participate in the upcoming challenges, and take advantage of the wealth of knowledge shared through our webinars. Together, we can build a brighter future for our company and make a lasting impact in our industry. And perhaps one day, The Lodge may open its doors to even more people! Stay tuned for more PIONEERS and the Lodge news. We can’t wait to see what we can achieve together!

Ask an Engineer: What is Machine Learning and Neural Networks?

Thu, 22 Aug 2024 07:00:00 GMT

Originating from brain simulations, neural networks is a subset of machine learning. It’s a theoretical model of how the human brain works. Input and output data are fed to the network, and connections of different strengths are formed between the neurons, in a way similar to a physical brain. Deep learning does not require the programming of human-extracted rules and learns its own representation from the data. This could be game-changing. With continuously growing hardware technology, deep learning could likely change the way of scientific computing. Physics-based simulations have been widely used to guide product design. But depending on the physics and scale of the problem, simulations could take minutes to hours or even days to run. To verify the performance of many design variants and working conditions could be time consuming. I train neural networks to overcome this challenge. The neural networks learn from the physics-based simulations, recognizing the 3D shapes and underlying materials. It then learns to predict the physics of the mechanical scenario with varying designs and conditions. Take the EV design as an example, the design of an EV battery pack has to meet the safety standards upon side impact. With every design change in the battery pack, a physics-based simulation has to be performed to test whether the design meets the safety requirements. When a simulation job is started, the engineer waits for hours to days before he or she can see the simulation results. Now, with a trained machine learning model, the engineer submits a design change and gets to see the results in a few seconds. The results from a machine learning prediction is as rich as a physics-based simulation. We could see how the impact force changes over time and the entire 3D animation of the crash event to understand what would happen to the battery cells upon impact, whether there would be a short circuit due to the battery pack deformation. As another example, an aircraft landing gear has to bear the weight and acceleration of the entire plane during landing. Depending on the landing speed, angle and weather conditions, the landing gear component could be under stress leading to safety concerns. Trained by physics-based simulations, machine learning models could be used to get landing gear stress state within milliseconds as compared to much longer wait times when running actual simulations. As you could see, machine learning models trained from physics-based simulations speed up by the thousands to predict 3D results in space and time. This could enable almost an interactive 3D design environment that turns weeks or months of a design cycle into hours, and potentially change the way products are designed and optimized. In the near future, with almost instantaneous 3D feedbacks from neural networks, car makers could quickly evaluate and optimize their battery pack designs for better safety; doctors could quickly screen and find the matching candidates for a clinical trial; aircraft manufacturers could design landing gears that last for longer; bottle packaging could be made lighter and more sustainable, all at a significantly faster pace. With machine learning, simulations could become smarter and able to solve new challenging problems; simulations performed by experts could be deployed into the hands of designers and clinicians; simulation representations could be learned and simulation knowledge could be accumulated. Detailed 3D information from simulations could become super valuable for machine learning by enriching the sparse experimental observations critical for the training of balanced and reliable AI. I can’t wait to see how simulation and machine learning together change the way of scientific computing. Interested in learning more about this topic? Join us in the SIMULIA Community and visit the Machine Learning wiki. Jing Bi, PhD Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts? The SIMULIA Community is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.

Calling all leaders in business sustainability

Mon, 01 Jul 2024 11:28:04 GMT

Over the past few years, sustainability has become a strategic priority that is driving transformation across all businesses, governments and cities – and right across the globe. It’s easy to see why. The world is grappling with environmental challenges – and there’s increasing demand from consumers for businesses to adopt more sustainable practices. Get it right, and the benefits are huge: responsible businesses not only stand to build more trust with stakeholders, employees and consumers, but they also see a positive impact in terms of engagement, profitability and recruitment. The path toward a smooth energy transition – While each and every business has a responsibility to implement sustainable practices in their everyday operations, there’s even greater expectation placed on the energy sector and large-scale ventures to facilitate the transition to green energy – a crucial process if we are going to cut emissions and be able to keep global warming to no more than 1.5°C as called for in the Paris Agreement. However, the energy transition won’t happen overnight. Recent reports suggests that most heavy industry sectors are struggling to meet even the first-level goals aiming for achievement by 2030. This is a problem. “It’s crucial we create solutions, because a smooth energy transition underpins every single industry,” said Annette Höglund-Dönnes, Renewable Energy and Materials Lead for the Northern Europe at Dassault Systèmes. “Without access to clean energy, we cannot drive the desired decarbonization.” Digital tools are key to success – According to the World Economic Forum, 70% of the sustainability goals in Agenda 2030 can be met by using digital tools to de-risk large capital investments. Similarly, the Swedish industry research and innovation organization Energiforsk recently launched a research program called “Digitalisation in Nuclear Power“, which demonstrates how, as nuclear becomes more prominent across Europe, digital tools will be essential to ensure that projects are delivered in time and on budget. Several European energy giants are prioritizing digitalization and innovation across their energy projects. For example, companies are using digital twin technology as a powerful tool to optimize the design of capital assets, processes and equipment. This is increasing efficiency across the lifecycle from the initial concepts, through to the building, operations and maintenance phases. A call for collaboration – Digital tools are also democratizing data and facilitating better collaboration between key stakeholders within organizations and across the energy value chain. Höglund-Dönnes sees this as a very positive step forward: “Collaboration undergoing a transformation,” she said. A springboard for showcasing success – Keen to foster even further collaboration between businesses, industries and key stakeholders in energy transition, Dassault Systèmes is leveraging its unique position in the market to run its third Leaders in Business Sustainability event in Europe. This year’s event is on September 19th at the historical Münchenbryggeriet in Stockholm, and the all-new format is designed to ensure attendees maximize their knowledge-sharing and enjoy the many networking opportunities. A focus on four key themes – This year, there will be sessions on four key themes: 1. The power plan for a smooth energy transition – the panel discusses the technological advancements, collaboration, and transformation needed to expedite the shift towards decarbonization and new energy. Who does what and how? 2. Supply chain resilience – products change, and new supply chains need to be formed. In a fast-moving environment the key ability is to adopt to new needs and be prepared to meet the demand. Business sustainability relies on resilient supply chains. 3. Sustainable Innovation: Clean transportation and the future of the auto industry. Developing new products, new processes and sometimes new business models is the biggest challenge to stay on track with sustainability imperatives. It also represents the biggest business opportunities for all industries today. In this session, we will discuss how to transform your business by integrating product innovation with sustainable design practices that can help reduce your carbon footprint. 4. How to make the circular economy achievable, scalable and profitable. In this session, Florence Verzelen, Executive Vice President of Industry, Marketing and Sustainability and Philippine de T’Serclaes, Dassault Systemes’ Chief Sustainability Officer at Dassault Systèmes will investigate how, by redefining how you design, manufacture and operate within the circular economy framework, you can not only contribute to a lower carbon footprint but also position your businesses for long-term success. We have invited a selected number of panelists to share their inspiring journeys in each of these sessions. As well as learning from these stories, attendees will be able to participate in focused group sessions on specific sustainability topics. Our team of experts will guide you through areas like systems engineering, 3D modelling and more. For those attendees who like to immerse themselves in new technologies, our Innovative Playground will enable hands-on experiences in the virtual world using the 3DEXPERIENCE platform. Attendees can also connect with industry peers, thought leaders and the brightest drivers of sustainability. Mingle with Dassault Systèmes teams, partners and sponsors and build powerful connections that will drive your business forward. This combination of thought leadership, our playground experience and networking will allow our invited guests to explore the latest new ideas and leave the day feeling inspired and connected. “Leaders from every industry, no matter where they are in their decarbonization journey, will benefit from this event,” said Höglund-Dönnes. “You’ll be able to make new connections, see the latest and greatest new technologies and learn more about how you can help drive sustainability across the value chain to build a brighter future for everyone.” Seats are limited. Register your interest now.

Impedance Simulation of Differential Transmission Lines Considering ESD Protection Diode Components

Thu, 13 Jun 2024 07:00:00 GMT

*The following was co-authored with Reiner Welzer, an Application Engineer at Inova Semiconductors GmbH. He has over 20 years of experience in signal and power integrity and EMC compliant designs of RF and analog signal PCBs. In recent years, he has worked extensively on ESD protection of electronic circuits, especially for automotive. Coupled Microstripline Modeling With the data transmission over a single twisted-pair cable, the connection on the PCB is realized using an edge-coupled surface microstrip conductor. Mostly differential signaling is used for high-speed data transmission. This provides a good electromagnetic compatibility (EMC). Typically, an edge-coupled surface microstrip conductor with a nominal line impedance of 100 Ω is used and located at the TOP layer. Figure 1 shows the dimension of edge-coupled microstrip line used for the simulation, which delivers 100ohm differential line impedance. Figure 1. Differential line model with dimensions For a line impedance calculation, the correct material property and the modeling of the correct solder mask shape are important. As shown in Figure 1, the modeling of the solder mask is realized as a thin skin, covering the microstrip conductor. As the differential line is connected to the cable through a plug, a possible ESD event is very likely to occur at this plug. Such an ESD event generates very high voltage and current peaks within a short time range ( < 1 ns) and it can cause damage to the electronic components. In order to avoid this, ESD protection components should be connected to both conductors of the differential line. Very often TVS (Transient Voltage Suppressor) diodes are used for such applications. They have a very fast response-time and limit the voltage to a certain value within a short time. With a high-speed data rate of 6 Gbps, it is important to use small component package sizes with low parasitic elements. For the analyzed geometry, we were using the ESD protection diode PESD5V0C1BLS-Q from NEXPERIA [1]. This device has a maximum diode capacitance of 0.3 pF and a small package size of 1 mm x 0.6 mm x 0.47 mm. In the simulation, the ESD diodes are defined using “lumped elements”. They are reproduced by their parasitic capacity only. “Lumped elements” are CST internal elements, which can represent R, L, C components. Figure 2 shows the simulation model of the differential line including the ESD diodes. Figure 2. Simulation model of a differential line with the ESD diodes In Figure 2 it can be seen that the landing pads of the ESD components on the differential signal line are wider than the trace. Obviously, the cross-section change at this particular position leads to a line impedance change. Depending on the size of the impedance change, there is an impact to the signal integrity. Pre-Layout TDR Simulation A well-known method to analyze the line impedance along a transmission line is the time domain reflectometry method (TDR). It works similarly to the radar principle, where a pulse is transmitted and the reflected signal is recorded back at the input side. Information about the impedance curve can be obtained by evaluating the reflected signals. CST Studio Suite can also perform TDR simulation using either the time domain- or frequency domain solver. For this pre-layout investigation, we use the frequency domain solver. This is justified because of the relatively simple structure for discretization and its small size compared to the wavelength. As the frequency domain solver delivers only the S-Parameter results, we calculate the TDR results by post-processing the return loss S-Parameter S11. With a Gaussian signal defined as the input signal, the output signal can be reconstructed from the return loss information. We calculate TDR by applying Equation 1. The maximum frequency range defined in the simulation is 8.4 GHz, which corresponds to the 10%-90% rise time of 104 ps. Figure 3. Time integration of Gaussian pulse CST Studio Suite can perform this calculation automatically using the template-based post-processing “TDR calculation from S-Parameter”, as shown in Figure 4. Figure 4. Template Based Post-Processing of TDR calculation from S-Parameter Although the diode capacitance value is considerably small (0.3 pF), it has to be considered when performing the TDR simulation. It reduces the nominal line impedance (100 Ω). Figure 5 illustrates the comparison with – and without considering this capacitance. Figure 5. TDR simulation with- and without diode capacitance Layout Modification A maximum allowed impedance variation of ± 10% is typically set when designing high-speed data lanes. With a 100 Ω reference impedance, we can see from Figure 5 that the curve drops down to 83 Ω. This would fail the lower limit (90 Ω) of the requirements. To fulfill the impedance requirement, a layout optimization around the ESD diode location must be performed. Since the ESD diode capacitance reduces the line impedance in this region, it is necessary to compensate this effect by reducing the capacitance per meter of the transmission line. The most effective method to do this is to cut out the reference plane below the ESD diodes. The optimal size of the recessed ground area can be found by simulation. Figure 6 shows an optimized configuration of the modified reference plane for a certain layer construction after several simulation iterations. Figure 6. Reference plane with cut-out (GND vias are not visible) The corresponding impedance curve improvement can be seen in Figure 7. Figure 7. Impedance comparison with- and without the cut-out at the reference plane Please note that the cut-out in the ground plane may allow crosstalk from and to other switching or interference signals. Therefore, it is recommended not to route any sensitive signal line below this cut-out. In the PCB layout design tool, this can be ensured by creating a restricted/keep-out area. The improvement of the line impedance can also be seen from the return loss S-parameter S11 (Figure 8). An improvement of around 12 dB for the return loss can be observed. This also means an improvement for the signal integrity. Figure 8. Return loss (S11) comparison with- and without the cut-out at the reference plane The better signal integrity behavior of the recessed reference plane can also be demonstrated by comparing the eye-diagram results. The used digital Pseudo Random Bit Sequences (PRBS) have the following properties: PRBS12 Differential voltage level ± 200 mV Rise and fall time of 80 ps and period length corresponds to 3 GHz Figure 9 and Figure 10 show the eye diagram for both layouts. Figure 9. Eye-diagram with cut-out of the reference plane. Figure 10. Eye-diagram without cut-out of the reference plane. Measurement Comparison To confirm the simulation results, a PCB layout with optimized parameters (found by CST Studio Suite simulation) has been created. The TDR impedance measurement has been performed with a differential TDR measurement-system from Sequid. During the measurement, the transmission line was connected via SMA sockets. For the simulation, it is not necessary to consider the connectors because they only increase the computing power and time and have no influence to the line impedance around the ESD diodes. The end of the differential line can be left open. The manufactured PCB prototype for the measurement is a standard FR-4 board with 4-layers and a total thickness of around 1.6 mm. The differential pair dimension differs slightly from the one used in the pre-layout investigation, but the reference plane cut-out dimensions remain the same. Figure 11 shows a section of the PCB layout prototype used for measurement comparison by CST Studio Suite. Figure 11. PCB prototype model inside CST MWS. The comparison of the line impedance results between measurement and simulation is shown in Figure 12. The good agreement to the measurement confirms the simulation with CST MWS. Figure 12. Impedance comparison between measurement and simulation. Conclusion To transmit high-speed data signals, it is important to achieve a smooth line impedance curve for the whole high-speed data lane. The signal integrity requirements are typically ± 10% from the reference impedance. In this blog article, we presented the 3D layout simulation and optimization of such a transmission line using CST Studio Suite. We demonstrated the benefits of using this simulation tool in the pre-layout phase, especially regarding the development time. We showed that it is important to respect the ESD diode component (capacitive information) during the optimization, to achieve more realistic results. Finally, a good agreement between simulation and measurement confirmed the accuracy of the simulation results with CST Studio Suite. References [1] https://www.nexperia.com/products/esd-protection-tvs-filtering-and-signal-conditioning/automotive-esd-protection-and-tvs/automotive-esd-protection/PESD5V0C1BLS-Q.html [2] CST Studio Suite 2024 Online Help. Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts? The SIMULIA Community is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.

Why virtual twins are the dream for semiconductor development

Wed, 22 May 2024 14:46:19 GMT

The Biden administration recently announced that it will disperse $280 million to companies to develop digital twins of semiconductors. The funding comes after a yearslong shortage of chips, of which semiconductors are a significant component and which are necessary for almost every piece of technology we use on a daily basis. The administration’s push for developing a workaround to a persistent problem is innovative in and of itself, but virtual twins can take things a step past where their digital counterparts fall short. Virtual twins replicate physical objects just like digital twins do, but they take it further, simulating the processes and interactions within an entire system or ecosystem. This could be a game-changer for the semiconductor industry and beyond, offering a more holistic approach to understanding and solving complex challenges. By incorporating real-world data in real-time, virtual twins could provide insights into production processes and supply chain dynamics, and even predict future disruptions, offering a more comprehensive solution to the semiconductor shortage and other critical issues facing technology sectors today. Here’s why virtual twins, not digital ones, would be a true game changer for semiconductors (and really everything else). What’s a virtual twin, anyway? Digital twins are 3D representations of physical objects. They’re static and relatively limited in the scope of what you’re able to do with them. You can toggle a 3D rendering, move it around, inspect its elements and conduct a simulation. But such a model is siloed. You can’t understand its interactions with other objects or the effects that certain changes might have on it, like temperature or pressure. It exists in a vacuum, so to speak. “Digital twins are legacy technology,” explained John Maculley, business strategy consultant for high-tech at Dassault Systèmes. That is, they’re useful for some things, but in general, are an outdated choice when it comes to adopting innovative methods to solve persisting problems. Virtual twins, on the other hand, comprise not just a digital model of an object or process, but the entire environment in which it exists. A more sophisticated option, virtual twins can encompass as much as you want them to. That includes not just the soccer ball and a simulation of someone kicking it, but a detailed map of how that soccer ball gets made, from the supply chain that sources its raw materials to the manufacturing methods that are used to create it. The scope is vast when it comes to virtual twins, and, simply put, there’s so much more you can do with them. Virtual twins for semiconductors While it’s clear that virtual twins are more advantageous for sophisticated operations than their digital counterparts, they’re also a superior option for semiconductors specifically. Semiconductors are essentially a building block of electronic technology. They’re responsible for controlling electrical flows in devices like TVs, smartphones and computers, and are what enables these tools to be fast and powerful and, increasingly, small. With so many use cases, they’re a foundational piece of technology in our lives today. Semiconductor manufacturing requires highly specific and intricate processes. The involved equipment must be precise, with no room for deviations in any aspect of the production process. The workers who run the machinery need to be highly skilled and specialized. The labs where the semiconductors are made are extremely expensive to build, operate and maintain. This doesn’t even scratch the surface of what’s involved; in short, however, semiconductors require a high level of detail, skill, investment, sophistication and exactitude. Producing them at scale, which is what the current climate demands, is beyond tricky. Labs and manufacturing facilities where semiconductors are developed could benefit from virtual twins Virtual twins as a differentiator The Biden administration’s funding announcement indicates a hope that a digital, home-grown solution will present a fix for the current problems that the country is experiencing. It’s part of the CHIPS Act, which was initially published in 2022 in an effort to – among other things – reduce the effects felt from supply chain disruptions. Weather, the COVID-19 pandemic, geopolitical shifts and a variety of other factors caused a slowdown in the production of semiconductors and chips. While the supply of these materials went down, their demand didn’t. This trend impacted industries we touch every day, with automotive, consumer electronics and wireless communications chief among them. Virtual twins can prevent similar problems from cropping up again. Regardless of what issues persist, be they meteorological or geopolitical in nature, having the entirety of the semiconductor lifecycle in a virtual setting can support the agility needed to succeed. A virtual twin can house procurement of raw materials specifications, refinement processes, manufacturing set-ups, machinery specifications, shipping and sorting and more. Coupled with AI, virtual twins can sift through data at lightning speed, catching even the most minute changes or errors and addressing them in real-time, a capacity digital twins lack. “Companies that transition from legacy digital twins to virtual twin experiences are going to quickly realize they’ve got an entirely new universe of simulation capabilities and a deeper understanding of the underlying physics driving their product decisions,” Maculley said. “Their AI strategies and roadmaps become clearer, and their ability to react to market changes increases exponentially.” Learning from their own ecosystem and making adjustments as needed, enabling accuracy and efficiency while also expanding the potential for future production are all key differentiators between digital and virtual twins for semiconductors. Implementing cutting-edge technology, rather than relying on legacy systems, can usher in a new era of innovation and advancement that Biden’s funding aims to accomplish. The first steps toward creating a virtual future Recognizing the need for an innovative solution marks a pivotal step towards progress. However, the true challenge lies in pinpointing the exact solution that will steer the United States onto the correct path. By channeling investments into the development of virtual twins rather than settling for mere digital replicas, we can achieve a comprehensive resolution to the complex problems at hand. In February, Dassault Systèmes inked a deal with Purdue University to do exactly that; together, the two sides will offer a hands-on program for students to learn how to leverage virtual twins to explore new solutions for research and development, sustainability and workforce readiness and adaptability in the semiconductor industry. This approach not only enhances our understanding of systems and processes but also significantly improves decision-making and innovation. Furthermore, focusing on semiconductors as a starting point for this transformative shift is both strategic and necessary. Given their critical role in virtually all modern technology, semiconductors are and will remain in high demand. By strengthening our capabilities in semiconductor technology, we not only bolster our technological infrastructure but also ensure our competitiveness in a rapidly evolving global landscape. In essence, a commitment to nurturing advancements in virtual twin technology, coupled with a focus on semiconductor innovation, represents a forward-thinking strategy that promises to secure a prosperous and technologically advanced future for the United States.

[Press Release] Dixon Technologies India Signs MoU with Dassault Systèmes to Optimize its Global Manufacturing Operations

Wed, 17 Apr 2024 03:42:35 GMT

Through this collaboration, Dixon Technologies India will leverage Dassault Systèmes’ DELMIA Apriso applications to optimize manufacturing operations worldwide The collaboration was driven by the need to streamline manufacturing processes, enhance quality control, and drive operational excellence With a focus on innovation, technology, and collaboration, this partnership aims to revolutionize the way products are designed, produced and delivered to markets around the world Dassault Systèmes (Euronext Paris: FR0014003TT8, DSY.PA) today announced it has signed a Memorandum of Understanding (MoU) with Dixon Technologies, a leading global electronics manufacturing services (EMS) provider, to transform its manufacturing landscape thanks to Dassault Systèmes’ DELMIA Apriso applications. This collaboration marks a significant milestone in the evolution of digital manufacturing, bringing together two industry powerhouses with a shared vision for innovation, efficiency, and excellence. This collaboration is driven by the need to streamline production processes, enhance quality control, and drive operational excellence. By harnessing the power of Dassault Systèmes’ DELMIA Apriso applications, Dixon Technologies aims to unlock new levels of agility, flexibility, and sustainability in its manufacturing operations, enabling faster time-to-market and enhanced customer satisfaction. Dixon Technologies India has a proven track record of delivering high-quality electronic products, including consumer electronics, lighting products, mobile phones, and home appliances, to renowned brands worldwide. Through this partnership, Dixon Technologies India will further bolster its commitment to delivering superior products and services, as advanced capabilities of Dassault Systèmes’ DELMIA Apriso applications facilitate real-time visibility, process orchestration, and data-driven insights, all of which are critical for staying ahead of the curve in today’s competitive manufacturing landscape. With a focus on innovation, technology, and collaboration, this partnership is set to revolutionize the way products are designed, produced, and delivered to markets around the world. It is a testament to the power of synergy that leads to a future of mutual growth and success “We are excited about this partnership with Dassault Systèmes, which aligns perfectly with our vision of continuous improvement and innovation,” stated Atul B Lall, Managing Director, Dixon Technologies. “The seamless integration of DELMIA Apriso into our manufacturing processes will enable us to further optimize our operations, drive innovation, and deliver even greater value to our clients.” “At Dassault Systèmes, we believe in the transformative power of innovation. The implementation of Dassault Systèmes’ DELMIA Apriso by Dixon Technologies India, exemplifies our commitment to shaping the future of digital manufacturing. DELMIA Apriso empowers manufacturers like Dixon Technologies to optimize their global manufacturing operations, drive efficiency, and accelerate innovation, all while ensuring the highest quality standards. This collaboration is an illustration of our shared vision of excellence, and we are excited to embark on this journey with Dixon Technologies,” said Deepak NG, Managing Director, Dassault Systèmes India. ### FOR MORE INFORMATION Dassault Systèmes’ 3DEXPERIENCE platform, 3D design software, 3D Digital Mock Up and 3DEXPERIENCE Edu Centers of Excellence: https://www.3ds.com/edu/skills/edu-centers Connect with Dassault Systèmes on Twitter Facebook LinkedIn YouTube Dassault Systèmes Press Contacts India Kriti ASHOK kriti.ashok@3ds.com +91 9741310607

Why the High-Tech Industry Needs Multiphysics Simulation

Tue, 16 Apr 2024 15:00:03 GMT

A wireless charger does exactly what it promises: it charges the battery of compatible electronic devices without using a power cable. Just bring the handset to the base and leave it there. Shrinking Pains – The Challenges Facing the High-Tech Industry The High-Tech industry is built on complexity. Ever more performance and functionality is being packed into small, lightweight devices. “The risk of failure is higher because of the complexity,” says Jonathan Oakley, SIMULIA High-Tech Industry Process Director. “Being able to get that first, prototype to work first time is really important. It’s more important than ever, because it may be difficult to identify the cause of a problem, or even to fix the problem.” Bryan Hurlbut, SIMULIA Global High-Tech Industry Process Expert, notes that wearables from smartwatches to virtual reality (VR) and augmented reality (AR) headsets are putting particular pressure on manufacturers to find innovative solutions to minimize size and weight. Using the example of a smartwatch for skiing, he says “the device knows that I’m on the mountain, and it tells me what my exercise rate is, it communicates with each lift to tell me how many lifts I’ve taken and what my vertical skiing for the day is. All of the different antennas – 5G, Bluetooth, GPS – interact with the environment that you’re in to supply you with data, and packing all that in there and having it all communicate reliably is what’s driving a lot of these challenges.” In particular, Bryan notes “The battery can be one of the largest single consumers of space, so optimization from a physical size and performance perspective is necessary.” (For more information, see “Innovating better batteries for electronic devices”) With so many components in a single device, finding the best trade-off between different KPIs can be a challenge. A change that improves the performance of one subsystem can easily have a negative impact on another, but the interactions between different components and subsystems can be hard to quantify. Despite this, there is a lot of pressure to turnaround optimal designs rapidly to remain competitive and bring products to market faster. David Johns, Global High-Tech Industry Process Expert, adds “There’s also the technical challenge of designing components that can handle the super-high speeds of modern data rates.” In addition, a complex device requires a complex global supply chain. Geopolitical tensions and other unexpected events are pushing up energy, shipping and materials costs, meaning manufacturers must keep costs as low as possible. What is Multiphysics Simulation? Simulation is well-established in the industry as a way to inform design, optimize performance and identify and resolve issues. By simulating a Virtual Twin of the device, engineers can analyze the performance of a design without the costs associated with constructing and testing a physical prototype. There are many different types of simulation covering different aspects of physics. Some of these include: Structural simulation: Structural aspects such as stress, strain, deformation and fracture. Example use case: to ensure the device can withstand drops and impacts. Electromagnetic simulation: Fields and currents within and around the device. Example use case: to optimize antenna placement and reduce interference. Fluid simulation: The behavior of fluids such as air or water, including heat transfer. Example use case: to design air-cooling systems. Vibroacoustic simulation: The vibration behavior of structures and the propagation of sound. Example use case: to identify resonances in components that produce buzzing noise. Many scenarios couple together different fields of physics: Jingsong Wang, SIMULIA Global High-Tech Industry Senior Specialist, gave another example of a vibrating capacitor: “The capacitor vibrates at the resonant frequency of the PCB – it is quiet but you can hear it hum. If we move the resonance a few hertz, the sound stops. So the resonant frequency is crucial to stopping the noise. To model this we have to simulate the electromagnetic and piezoelectric behavior of the capacitor, the resonant modes of the PCBs, and the radiated sound.” The workflow for simulating a vibrating capacitor, showing the need for multiple physical disciplines Even when there is no direct connection between disciplines, trade-offs can often involve balancing the results of several different types of analysis. David pointed to a USB connector as good example: “One critical aspect is the pins and springs that meet when you push the connector parts together. They have important electromagnetic design aspects for signal integrity – TDR, impedance, S-Parameters, and so forth. But there’s also the structural element of the retention spring, and that force needs to be just right to relieve the springs that slide over pins, but provide enough retention. “If you change something from a structural point of view, it might have too much impact on electromagnetics and vice versa. So in that case the physics would not be coupled, but they would independently influence the design and there’d be some trade off.” For more information about the SIMULIA solution for connector design, see “High-speed Connector Design with Modeling and Simulation”. Faster Regulatory Approval with Virtual Certification Simulations of multiple antennas on a 5G phone, as used for virtual SAR certification for the FCC. “Any radiating device has to comply with regulations in each geographical region, and this has to be demonstrated by certification before it can be sold,” Jonathan explains. The traditional way to get certification involves providing measurement data from a prototype to the regulator. However, the test regime for a complex device can be time-consuming and expensive – a beam-forming system as used in typical 5G phone can result in hundreds or thousands of different scenarios to test to ensure that interference and specific absorption rate (SAR) limits are not exceeded in any case. “Regulators now increasingly accept simulation results for virtual certification, replacing measurements from test,” Jonathan adds. “To get realistic results for certification users need to simulate the entire device with all antennas.” To see a real-world example of simulation being used to support virtual certification, see “Simulation for 5G Smartphone FCC Certification”. Get Results at Any Stage of Design with MODSIM Unified modeling and simulation (MODSIM) is a concept where modeling and simulation are combined on a common data model. With MODSIM, designers have access to simulation data even at the concept stage the design, allowing it to be analyzed from the earliest stages of development. “MODSIM allows you to go all the way from the ideation and the requirements through to full virtual prototype,” explains Jonathan. “And of course, it also allows you to do the connected physics, because the physics simulation is working on the same dataset and the same CAD geometry. You can deal with all of the physics at the same time and ensure the optimal trade-off is achieved.” SIMULIA products can be integrated into a MODSIM workflow on the 3DEXPERIENCE platform. This acts as a ‘single source of truth’ for the whole team. David explains that “One of the major challenges is the inefficiency of the development process itself. Companies traditionally have used a very manual process, often emailing files around, and each analyst may develop different versions for their own use. Very quickly it gets out of hand, and there’s often confusion about which version of the model you’re working on, especially with multiple design iterations.” Keeping all model and simulation data in the same place ensures everyone is working from the same files and updates to the model can be automatically propagated to all teams. It also ensures traceability, with a digital thread providing a robust link between the model files and the simulation data. A USB-C connector model on the 3DEXPERIENCE platform, allowing collaborative design and MODSIM Even global teams with complex supply chains across multiple companies can collaborate seamlessly with MODSIM. Access to data can be restricted to protect confidentiality and trade secrets, and encrypted models can be used to share data for integration analysis without revealing sensitive geometry. Conclusion To remain competitive in the fast-moving high tech industry, manufacturers need to bring innovative new products to market faster, with less development cost and risk. Unified modeling and simulation (MODSIM) accelerates development by providing designers and engineers with the data they need to make informed decisions even at the earliest stages of design. Multiphysics simulation can be used to model the behavior of complex systems covering many physical disciplines and help engineers find the best trade-offs between competing design requirements. By implementing MODSIM in their design flows, manufacturers can also streamline the design process to ensure the whole team is working with up-to-date files and any changes to the design are automatically propagated. Files can be shared with partners while maintaining confidentiality and protecting sensitive intellectual property. Traceability between the model geometry and the simulation data allows virtual certification, reducing the need for physical tests and making regulatory compliance faster and cheaper. Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts? The SIMULIA Community is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.

AI Image Recognition: The All Seeing Eye

Mon, 08 Apr 2024 06:08:07 GMT

AI-based image recognition stands at the forefront of technological advancements, revolutionizing the way we interpret and analyze visual information. This cutting-edge field combines the power of artificial intelligence and computer vision to enable machines to interpret, understand, and respond to visual data. From medical diagnostics and autonomous vehicles to retail and security applications, AI image recognition is transforming industries, offering the potential for enhanced efficiency, accuracy, and innovation. In this dynamic landscape, algorithms learn to discern patterns, identify objects, and make decisions, unleashing a myriad of applications that reshape how we interact with and harness the potential of visual information in the digital age. In the medical sector, AI is playing a pivotal role in revolutionizing various aspects of patient care. One significant application lies in diagnostic processes, where machine learning algorithms are employed to analyze medical imaging data, including X-rays and MRIs. This aids healthcare professionals in early and accurate detection of diseases, ultimately improving treatment outcomes. Another critical area where AI excels is in pathology. By assisting pathologists in interpreting complex histopathology slides, AI contributes to more precise and reliable diagnoses. This collaborative approach enhances the overall efficiency of pathology workflows, enabling healthcare providers to deliver timely and targeted interventions. Beyond diagnostics, AI’s predictive analytics models are proving valuable in identifying potential health risks in patients. These models analyze diverse sets of patient data to predict disease progression and anticipate health issues before they become critical. This proactive approach allows healthcare professionals to intervene early, offering more personalized and effective interventions. In essence, AI applications in the medical sector go beyond mere automation; they empower healthcare providers with advanced tools for diagnostic accuracy, streamlined workflows, and the delivery of patient-centric care. As technology continues to evolve, the integration of AI promises to further transform and optimize healthcare delivery. In manufacturing, AI has become a driving force for innovation and efficiency. Quality control processes benefit significantly from AI applications, as automated systems meticulously inspect products for defects on production lines, ensuring the delivery of high-quality outputs. This not only reduces the likelihood of faulty products reaching consumers but also enhances overall product reliability. One of the key contributions of AI in manufacturing is in the realm of predictive maintenance. By leveraging machine learning algorithms, AI can analyze equipment performance data to predict potential issues before they occur. This proactive approach minimizes downtime, optimizes machinery efficiency, and ultimately extends the lifespan of critical manufacturing assets. Collaboration between robotics and AI has transformed manufacturing tasks. Robotic systems, guided by AI algorithms, can perform intricate and precise operations with speed and accuracy. This synergy enhances production capabilities, particularly in industries requiring complex and repetitive tasks. Moreover, AI plays a pivotal role in supply chain management within the manufacturing sector. By optimizing inventory levels and improving demand forecasting, AI contributes to more efficient resource utilization and responsive production planning. This, in turn, enables manufacturers to adapt swiftly to market demands and changes in consumer preferences. In essence, AI applications in manufacturing go beyond automation; they redefine the manufacturing landscape by streamlining processes, elevating product quality, and fostering adaptive and agile production systems. As technology continues to advance, the integration of AI promises to further enhance the productivity and competitiveness of the manufacturing industry. AI revolutionizes environmental monitoring by swiftly identifying changes in ecosystems. In wildlife conservation, it analyzes camera trap images for species identification and health assessments. For deforestation detection, AI processes satellite imagery to monitor and combat illegal logging. Underwater drones equipped with AI track marine species and assess coral reef health. In air quality monitoring, AI analyzes sensor data to identify pollution sources and predict trends. Climate change research benefits from AI processing extensive climate data for impact assessment and predictions. Precision agriculture employs AI to optimize practices through satellite and drone imagery. Noise pollution monitoring uses AI to recognize sound patterns for implementing mitigation measures. Ecosystem health assessments integrate data from various sources, aiding conservation efforts. Overall, AI enhances environmental monitoring for informed decision-making and sustainable practices. Disclaimer: AI tools were used in creating copy and imagery.