The need to mitigate climate change is driving a need to electrify our infrastructure, vehicles, and appliances, which can then be charged and powered by renewable energy sources. The most visible and impactful electrification is now under way for electric vehicles (EVs). Beyond the transition to electric engines, several new features and technologies are driving the electrification of vehicles. The number of sensors in a vehicle is skyrocketing, driven by autonomous driving and other safety features, while a modern software-defined vehicle (SDV) is electrifying everything from air-conditioned seats to self-parking technology.

An important technology for EVs and SDVs is power modules. These are super high-voltage devices that convert one form of electricity to another (e.g., AC to DC), which is necessary to convert the vehicle battery energy to a current that can run the vehicles electrical system, including the drive train. These modules demand the highest power loads and are rated at 1000s of voltages – and the design of power devices, which are the fundamental electronic component of the power modules, is crucial, as a bad design can lead to catastrophe events.

Power devices, much more than other types of electrical devices, are designed for specific applications. In comparison, logic transistors can be used in everything from toasters to smartphones. Not only does the architecture of power devices change at higher voltages, different power ratings, or higher switching frequencies as needed, but the material can change as well.

New power requirements need wide-band gap materials

To meet new and future power demands for EVs, electric infrastructure, and other novel electrical systems, wide-band gap (WBG) materials are being developed and introduced. Silicon carbide (SiC) IGBTs are now available and being deployed, while gallium arsenide (GaN) HEMTs are a promising technology that is in the development stage.

Power density vs. switching frequency of power devices based on different materials.

Continuing with our EV example, SiC inverters can generally increase the potential range by approximately 10%, even after accounting for other design considerations. In addition, increasing the drive train voltage from 400V range to 800V can reduce the charging speeds by half. These voltages are only possible to realize with wide-band gap materials like SiC-based power devices. Tesla introduced SiC MOSFETs into its Model S back in 2018. Since then, numerous automotive manufacturers have also adopted SiC in their EVs, including Hyundai and BMW, for example.

GaN still has many design hurdles to overcame to improve reliability and decrease cost – but if it can be made affordable, perhaps the next realization of EVs will allow for charging in seconds with ranges of thousands of miles.

Simulating power devices

Because of the huge number of design parameters, simulation is important in the design of power devices. One crucial part for device design is the calculation of the breakdown voltage – the voltage at which the device can essentially melt, or catch fire, but will never operate again. These simulations need to be highly physics-based and capture the mechanisms by which electrons can be released or absorbed by the crystal lattice of these materials. The increasing band gaps in WBG materials like SiC and GaN increase the breakdown voltage. In addition, these materials have a smaller effective electron mass (i.e., the mass of an electron in a material dictates how fast it will move in an electric field) – which makes the switching frequency in devices based on these WBG materials faster.

A critical area of all electronics design is variability and reliability. Device performance needs to be stable and last a long time. A key factor for variability and reliability is defects in the crystal lattice. These defects, or traps, act as charge centers that can drastically impact how well a device works. Simulation can also help to identify the types of traps, providing a mechanistic understanding of how the traps will impact the device physics. Recently, Synopsys issued a paper using first-principles quantum solutions to characterize specific traps in SiC with QuantumATK.

Going forward, wind energy, solar, home appliances, and even the electric grid itself are going to need new devices with different structures and materials. The future is extremely exciting for power devices, which can be found in our EVs and will soon power a huge range of applications across our society.

The post Enabling New Applications With SiC IGBT And GaN HEMT For Power Module Design appeared first on Semiconductor Engineering.

The need to mitigate climate change is driving a need to electrify our infrastructure, vehicles, and appliances, which can then be charged and powered by renewable energy sources. The most visible and impactful electrification is now under way for electric vehicles (EVs). Beyond the transition to electric engines, several new features and technologies are driving the electrification of vehicles. The number of sensors in a vehicle is skyrocketing, driven by autonomous driving and other safety features, while a modern software-defined vehicle (SDV) is electrifying everything from air-conditioned seats to self-parking technology.

An important technology for EVs and SDVs is power modules. These are super high-voltage devices that convert one form of electricity to another (e.g., AC to DC), which is necessary to convert the vehicle battery energy to a current that can run the vehicles electrical system, including the drive train. These modules demand the highest power loads and are rated at 1000s of voltages – and the design of power devices, which are the fundamental electronic component of the power modules, is crucial, as a bad design can lead to catastrophe events.

Power devices, much more than other types of electrical devices, are designed for specific applications. In comparison, logic transistors can be used in everything from toasters to smartphones. Not only does the architecture of power devices change at higher voltages, different power ratings, or higher switching frequencies as needed, but the material can change as well.

New power requirements need wide-band gap materials

To meet new and future power demands for EVs, electric infrastructure, and other novel electrical systems, wide-band gap (WBG) materials are being developed and introduced. Silicon carbide (SiC) IGBTs are now available and being deployed, while gallium arsenide (GaN) HEMTs are a promising technology that is in the development stage.

Power density vs. switching frequency of power devices based on different materials.

Continuing with our EV example, SiC inverters can generally increase the potential range by approximately 10%, even after accounting for other design considerations. In addition, increasing the drive train voltage from 400V range to 800V can reduce the charging speeds by half. These voltages are only possible to realize with wide-band gap materials like SiC-based power devices. Tesla introduced SiC MOSFETs into its Model S back in 2018. Since then, numerous automotive manufacturers have also adopted SiC in their EVs, including Hyundai and BMW, for example.

GaN still has many design hurdles to overcame to improve reliability and decrease cost – but if it can be made affordable, perhaps the next realization of EVs will allow for charging in seconds with ranges of thousands of miles.

Simulating power devices

Because of the huge number of design parameters, simulation is important in the design of power devices. One crucial part for device design is the calculation of the breakdown voltage – the voltage at which the device can essentially melt, or catch fire, but will never operate again. These simulations need to be highly physics-based and capture the mechanisms by which electrons can be released or absorbed by the crystal lattice of these materials. The increasing band gaps in WBG materials like SiC and GaN increase the breakdown voltage. In addition, these materials have a smaller effective electron mass (i.e., the mass of an electron in a material dictates how fast it will move in an electric field) – which makes the switching frequency in devices based on these WBG materials faster.

A critical area of all electronics design is variability and reliability. Device performance needs to be stable and last a long time. A key factor for variability and reliability is defects in the crystal lattice. These defects, or traps, act as charge centers that can drastically impact how well a device works. Simulation can also help to identify the types of traps, providing a mechanistic understanding of how the traps will impact the device physics. Recently, Synopsys issued a paper using first-principles quantum solutions to characterize specific traps in SiC with QuantumATK.

Going forward, wind energy, solar, home appliances, and even the electric grid itself are going to need new devices with different structures and materials. The future is extremely exciting for power devices, which can be found in our EVs and will soon power a huge range of applications across our society.

The post Enabling New Applications With SiC IGBT And GaN HEMT For Power Module Design appeared first on Semiconductor Engineering.

The challenges before semiconductor fabs are expansive and evolving. As the size of chips shrinks from nanometers to eventually angstroms, the complexity of the manufacturing process increases in response. It can take hundreds of process steps and more than a month to process a single wafer. It can subsequently take more than another month to go through the assembly, testing, and packaging steps necessary to get to the final product.

Artificial Intelligence (AI) can be deployed within a fab to address the complexity and intricacy of semiconductor manufacturing. A fab generates petabytes of data as wafers go through the multitude of process and test operations. This wealth of data also presents a challenge in that it needs to be analyzed and acted on quickly to ensure tight process control, high yield, and avoid process excursions. Beyond navigating the complexity of the manufacturing process, new solutions are necessary to help make the process as efficient as possible and the yield as high as possible to produce the most business value for fabs.

The benefits of AI-enabled analysis tools for IC manufacturers

Traditional techniques to detect issues in the manufacturing process have run out of steam, especially at advanced technology nodes. For example, an engineer must do their own yield analysis to seek out potential problems. Once they identify an issue, they communicate with the defect and process teams to determine the root cause and then troubleshoot it. The defect team will begin work to find some correlation behind the issue and the process team troubleshoot and link it to the root cause.

All these steps take up significant time that could be focused on achieving the highest yield of chips possible, driving costs down and reducing time to market. One of the biggest benefits of enabling AI in analysis tools is that an engineer can quickly recognize and pinpoint an issue in a specific chip to see which process step and/or equipment has caused the issue.

Beyond the fast and accurate process control that AI allows for, there are numerous other benefits that result from the saved time and money, including:

• Predictive applications: Enables fabs to take leap from reactive to predictive process control
• Scalability: Analyzes petabytes of data, connects multiple fabs, and comes cloud-ready
• Efficiency: Allows fab to make better decisions and reduce false alarms

To enable the next generation of manufacturing, Synopsys is enabling AI and Machine Learning (ML) for a comprehensive process control solution.

Actionable insights with AI and ML

Wafer, equipment, design, mask, test, and yield are silos within a fab that can benefit from a comprehensive AI/ML enabled solution. Such a solution can specifically help engineers generate actionable insights into the following:

• Fault detection and classification (FDC)
• Statistical process control (SPC)
• Dynamic fault detection (DFD)
• Defect classification and image analytics
• Defect image analytics
• Decision support system (DSS)

Fast analysis of petabytes of data, from equipment sensors or process parameters, allows manufacturers to quickly identify the root cause of process excursions and take action to maintain yield.

AI and ML in the fab

Synopsys is a provider of software solutions for silicon manufacturing and silicon lifecycle management, including solutions for TCAD, mask solutions, and manufacturing analytics. Its existing solutions are connected to thousands of pieces of equipment over multiple fabs with millions of sensors, analyzing hundreds of petabytes of data. By providing real-time visibility into the manufacturing process, Synopsys enables predictive analytics and optimizes product quality and yield to help give semiconductor fabs a leg up in this competitive landscape.

Synopsys has introduced an AI/ML enabled software offering, Fab.da, to make semiconductor manufacturing efficient. Fab.da is a part of the Synopsys EDA Data Analytics solution, which brings together data analytics and insights from the entire chip lifecycle

It offers a complete data continuum by bringing together these different data types from many different sources into one platform for both advanced and mature node chips. This data continuum allows for high user productivity, maximum data scalability, and increased speed and accuracy in root cause analysis for issues.

Delivering process control solutions to manage complexity at leading-edge fabs, Fab.da can help chip designers and manufacturers drive operational excellence and productivity, providing a competitive edge in today’s manufacturing landscape.

The post Utilizing Artificial Intelligence For Efficient Semiconductor Manufacturing appeared first on Semiconductor Engineering.