Coventor

Semiconductor process development is no easy task, with each generation of devices more difficult and expensive to create. Traditional cycles of build-and-test development are becoming obsolete, since they are too expensive and time-consuming for the most advanced processes.The High Cost of Process DevelopmentMost chip designers developing new products rely on existing manufacturing processes, but someone had to create those processes to make the designs possible. The goal of process development is to create new semiconductor manufacturing processes that provide high yield while achieving the required device performance. In contrast to new chip design, however, it requires an entirely different set of engineers and skills.The traditional approach to process development involves building multiple test wafers to determine the ideal process for a given device. After one set of wafers is fabricated and analyzed, insights from the previous round help to refine process steps for another round of fabrication. Due to smaller feature sizes, each new process generation is more sensitive to variation. This adds even more complexity because smaller feature sizes and parasitic effects require more measurements and testing as well as additional fabrication. The cycle is repeated many times before the entire process flow can be finalized, making it time- and cost-intensive, especially for the most advanced technology nodes.Testing Virtual Wafers Instead of Real WafersToday, there is an alternative to this slow, expensive way of doing things. Virtual fabrication lets computers simulate all of the processing that occurs when real wafers are built. These virtual models allow semiconductor process engineers to test manufacturing equipment settings with far greater variation than is possible in a physical fab. Designers can simulate the entire process flow, running the equivalent of thousands of wafers in days instead of months. Designers can quickly see graphical animations to visualize process steps, modify process recipes and device geometries, and measure how these changes affect electrical behavior.Improving Yield Using Statistics in Virtual Wafer FabricationBecause of the high volume of data generated, designers are turning to statistical analysis to provide greater confidence in their choice of process settings. Defects and random variations can be modeled in a virtual fab in a way that’s not possible in a real fab, letting developers test the sensitivity of the device structures against the unpredictable aspects of processing.There’s more than one approach to optimizing the process settings used in a new memory or logic fabrication sequence. The simplest one involves taking a single variable and exploring its effects. Critical dimensions (CDs), for example, establish those feature sizes of a device that ensure desired electrical performance. A particular dimension can be swept from low to high values – developers can then measure the effects of that range on device behaviors such as threshold voltage. This allows developers to ensure that the electrical behavior of their device design addresses the range of expected feature sizes and variability. The interactions with intersecting process steps can also be tested for further validation, since these interactions can lead to unanticipated device performance.But, in reality, this approach isn’t sufficient for studying the complex web of interactions between process steps and the resulting structures.A second approach leverages Monte Carlo analysis, randomly varying a wide range of process and device parameters and calculating the resulting device geometry and performance. This data can be used to automatically identify the process and design settings needed to achieve yield and performance goals. It’s an area where simulation shines, providing a useful way to test the interactions between many different processes.Statistical experiments using virtual fabrication illustrate step-by-step methodology to optimize process and design settingsVirtual Fabrication PlatformSEMulator3D is a virtual fabrication platform created by Coventor, a Lam Research company. It allows the definition of all process steps, the modeling of devices, the collection of metrics, electrical and device analysis, the statistical analysis of results, and the visualization of process steps through graphical animation. Today, semiconductor companies use it for both optimizing and scaling leading process nodes and for developing advanced new technologies like GAA (Gate-All-Around) transistors.The ability to do this work virtually is the future of semiconductor process development. Virtual fabrication accelerates new process time-to-market by months, opening up market opportunities worth hundreds of millions of dollars for semiconductor companies.Visualization of process steps of a Gate-All-Around transistor shows 3D construction in SEMulator3D. To learn more about virtual fabrication and how it’s changing the future of semiconductor technology development, download our whitepaper Speeding Up Process Optimization with Virtual Fabrication.Lam Research is a longtime member of MEMS Sensors Industry Group®, (MSIG), a SEMI technology community that connects the MEMS and sensors supply network in established and emerging markets, enabling members to grow and prosper. Visit us today.David M. Fried, Ph.D., is vice president of Computational Products at Lam Research, where he is responsible for the company’s strategic direction and implementation of virtual process solutions, including the Coventor SEMulator3D virtual fabrication 3D process modeling solution. Fried leads the execution of technology strategy for technology platforms, partnerships, and external relationships. His expertise touches upon such areas as Silicon-on-Insulator (SOI), FinFETs, memory scaling, strained silicon, and process variability.Fried is a well-respected technologist in the semiconductor industry, with 60 patents to his credit and a notable 14-year career with IBM, where he was involved in successive process generations from 65-nanometer and lower. His most recent position was 22nm chief technologist for IBM’s Systems and Technology Group. He holds bachelor’s, master’s and doctoral degrees in Electrical Engineering from Cornell University.Republished with permission from Lam Research.

New MEMS-based products are constantly emerging, fueled by the Internet of Things (IoT), autonomous driving, smart manufacturing and healthcare applications. The MEMS pressure sensor market is no exception to this trend1. Its growth has been driven mainly by automotive applications such as tire pressure management system (TPMS) regulations in China, fuel and ignition systems, thermal systems, oil-pressure monitoring, and indoor and outdoor navigation systems. Easy to customize and integrate, miniature, sensitive, accurate and low-power MEMS devices are especially well-suited to the accuracy, power consumption, sensitivity and miniaturization that pressure sensors require.Yet MEMS design also presents some specialized challenges, such as a strong coupling between fabrication technology and design. Complex physical structures that exhibit non-linear behavior, custom packaging requirements, and a final product that requires integration with surrounding CMOS circuitry are just a few examples. What’s more, there is a lack of standardized processes and process validation in MEMS design ecosystems. Pressure Sensor (Courtesy: X-FAB) As with other products based on MEMS technology, designers must increasingly customize pressure sensors for higher performance – sensitivity and linearity, in this case – while decreasing their package size. Designers can accomplish the task by studying sensor performance and manufacturability using computer models prior to fabrication. This can ensure that the sensor meets its required specifications while simultaneously reducing manufacturing cycles and cost.The Power of CollaborationThis is where strong collaboration among EDA providers, MEMS technologists and designers delivers tangible benefits. EDA providers and MEMS foundries can collectively help MEMS designers to incorporate foundry process constraints into their designs.In the semiconductor industry, first-pass successful silicon relies on standardized manufacturing processes, thorough technology characterization, accurate model generation, established simulation and verification, and extensive reuse of proven design blocks. In the MEMS world, where processes and products are developed concurrently, and processes change with every product, is it possible to adopt standardized processes, design methodologies, and tools that enable efficient reuse of existing technology and design knowledge? The challenge lies in maintaining the flexibility to optimize products for a diverse array of requirements. The ideal design platform should ease sharing of technology and design data between the foundry and its customers, enabling two-way collaborative development and allowing foundry technologists to easily perform a feasibility assessment of a customer’s project. This approach offers important benefits, allowing designers to explore and evaluate the suitability of a foundry’s process technology in their unique application. It also supports accurate prediction of device performance prior to fabrication and reduces costly build-and-test cycles. Combining standardized manufacturing processes, MEMS process design kits (PDKs), and a proven design flow are the starting point for development of manufacturing-ready designs.A Real-Life Example using Pressure SensorsAn EDA company, Coventor (a Lam Research company), along with MEMS foundry partner X-FAB, collaborated to develop a PDK that would ensure that manufacturing constraints are automatically considered early in their design process. The design flow is based upon an X-FAB fabrication platform that supports multiple process options for the manufacturing of absolute and relative MEMS pressure sensors. The PDK is a “golden container” for all the process and material characteristics of the silicon membrane and substrate, glass, passivation layers, and piezoresistive components. It enforces material properties and guarantees their correct implementation during the simulation. It also includes a component library containing ready-to-use, 3D parameterized devices (such as membranes and resistors), all pre-designed with foundry-supported materials to support their respective design rules. The components are readily partitioned for optimized meshing and simulation, saving design and simulation time. Figure 1: The elements and design flow of the PDK designed by Coventor and X-FAB. (Courtesy: Coventor)Designers can use components from the library to create a custom design — which might include different membrane shapes and sizes, and resistors of varying shape, size and position — to simulate the impact of different technology variants (such as resistor doping profiles, membrane and substrate thickness, glass material properties, and passivation schemes). This allows them to anticipate the effect of these design changes on sensor sensitivity for varying pressure and temperature regimes.Extensive validation of the pressure sensor design platform is currently underway. So far, the simulations have exhibited very good correlation to actual device measurements across a range of pressure and temperature conditions, including predictions of non-linear behavior for various pressure sensor designs. At the same time, the simulation accounts for mechanical membrane properties and piezoresistivity. With this type of design platform, a foundry can provide guidelines to help customers select both the fab technology and design features that lead to an optimal design solution. Figure 2: Simulation results depicting mechanical displacement in a pressure sensor design (Courtesy: X-FAB) Let’s Face the Next Challenges…A complete design platform for MEMS must eventually include not only MEMS device design, but system integration functions, such as the application-specific integrated circuit (ASIC) design and packaging/assembly of the product. In addition to the design verification that the PDK provides, additional partnerships among foundries, integrated device manufacturers (IDMs), research centers, equipment suppliers, and EDA vendors will help to define requirements and solutions that address every level of design and production. These might include tasks such as describing standardized material properties and process specifications, creating accurate foundry-proven design models, and defining requirements for system-level simulation. In the future, PDK simulations might even include up to tape-out and physical verification. To learn more about this collaborative PDK development work, please click here for the whitepaper.Christine Dufour, MEMS PDK Program Manager, CoventorChristine Dufour is the MEMS PDK program manager at Coventor. She has more than 20 years of experience in the semiconductor industry, leading process design kit development for BiCMOS and CMOS processes at several major semiconductor companies. Ms. Dufour has also worked as a product manager in the RF design environment area. In addition to her extensive experience in MEMS PDK development, she is an expert in all aspects of MEMS design flow and design tool development. Ms. Dufour received an engineering degree at Technological University of Compiegne.For more information on Coventor, a Lam Research Company, visit: https://www.coventor.com/ Viraja Sharma, Development Engineer, MEMS Simulation Design, X-FABViraja Sharma is a development engineer for MEMS Simulation Design at X-FAB. Her work involves the design and simulation of MEMS inertial and pressure sensors. Prior to her tenure at X-FAB, Ms. Sharma performed similar duties for other semiconductor companies. She received her Master of Science degree in Micro and Nano Systems from TU Chemnitz, where she studied MEMS and micro technologies.For more information on X-FAB, visit: https://www.xfab.comCoventor and X-FAB are members of SEMI-MEMS Sensors Industry Group that connects the MEMS and sensors supply network, enabling members to address common industry challenges and explore new markets. 1 Market research firm Yole Développement predicts that MEMS pressure sensors alone will become a $2 billion market by 2023. See: https://yole-i-micronews-com.osu.eu-west 2.outscale.com/uploads/2019/01/YD18018_MEMS_Pressure_Sensor_Market_Yole_Developpement_2018_Sample.pdf

Self-driving cars have been all the rage in both the trade and popular press in recent years. I prefer the term “autonomous vehicles,” which more broadly captures the possibilities, encompassing not only small passenger vehicles but mass transit and industrial vehicles as well. Depending on who’s talking, we will all be riding in fully autonomous vehicles in five to 25 years.The five-year estimates come from startups eager to raise venture capital while the 25-year estimates stem from Tier 1 automotive suppliers who tend to be more conservative in outlook. Regardless of the timeframe, a multitude of investors – national governments, venture capitalists and companies – are dedicating significant capital and effort to make autonomous vehicles a reality.I must admit that I did not fully grasp the enthusiasm for self-driving cars until last year. First, I’ve always enjoyed driving, unless I’m in stop-and-go traffic, so I couldn’t imagine relinquishing the task. Second, I’ve deliberately arranged my life to spend minimal time in my car. However, traffic has become much heavier in my metropolitan area (Boston), and I know that many people in cities around the world face longer commutes and waste more time in gridlock.What is the solution to this problem that is only getting worse? I had an epiphany while walking through Shinigawa Station in Tokyo, one of the busiest train stations in the world. Dense streams of people crisscrossed the station on their individual paths, managing to avoid collisions without the aid of traffic controls. Evidently, humans have an innate collision-avoidance ability that makes traffic controls for pedestrian crowds unnecessary. If autonomous vehicles could achieve the same excellence in collision-avoidance, we could potentially reduce or eliminate traffic controls for vehicular traffic, providing a huge gain in transportation efficiency and relief from gridlock.Sensors as core building blocksNew and improved sensors, many based on micro-electromechanical systems (MEMS) technology, are key to achieving this vision. While MEMS inertial sensors (such as accelerometers and gyros) are already integral to the core safety systems in conventional vehicles, they are also essential to improved self-navigation in autonomous vehicles.The challenge for MEMS suppliers is to deliver inertial sensors that meet the requirements for self-navigation systems, which are different and more demanding than for safety systems.Pinpointing a vehicle’s position requires “dead reckoning” based on inertial sensor signals as a supplement to GPS input. Undesirable drift in the inertial sensor signals due to mechanical quadrature, temperature sensitivity and noise can quickly add up to a large error in position that may result in a collision. To meet the more rigorous requirements for autonomous vehicles, suppliers must design MEMS inertial sensors that are substantially more precise and resistant to drift. This requires design software that is both extremely accurate and fast, as well as increasingly precise and reliable manufacturing capabilities.Other MEMS-based devices, such as micromirrors and micro ultrasound transducers (MUTs), are also promising options for implementing vision and range-finding systems in autonomous vehicles. These sensing systems are needed for building electronic versions of the human collision-avoidance abilities that I witnessed in Shinigawa Station – and it is these systems that autonomous vehicles must emulate.When will self-driving cars become a reality? Aside from the provocative question that got you to read this far, I don’t have a definitive answer. It will undoubtedly occur in phases, ranging from the driver-augmentation systems available in today’s cars to the full autonomy and ubiquity that will allow reduction of traffic controls in 20 years or more. It is clear that the ultimate goals for autonomous vehicles are highly worthwhile, and that achieving those goals will require better-performing and more diverse MEMS sensors. Stephen (Steve) Breit, Ph.D. is Senior Director, MEMS Business, at Coventor, a Lam Research Company. Steve has been responsible for overseeing development and delivery of Coventor’s industry-leading software tools for MEMS design automation since joining Coventor in 2000. Steve holds numerous patents on software systems and methods for MEMS design automation and virtual fabrication. He holds a Ph.D. in Ocean Engineering from MIT and a B.S. in Naval Architecture and Marine Engineering from Webb Institute.For more information, visit: https://www.coventor.com