Did you know that artificial intelligence (AI) and machine learning (ML) can generate huge business value for semiconductor companies at every step of their operations, from research and chip design to production and sales? According to a recent survey by McKinsey, AI/ML now contributes between $5 billion and $8 billion annually to earnings before interest and taxes at semiconductor companies. And this is just the tip of the iceberg. Within the next two to three years, AI/ML could potentially generate between $35 billion and $40 billion in value annually.
But what exactly are AI and ML, and how do they work in semiconductor design? AI is a broad term that describes a machine or software application’s ability to reason, learn, and act in a manner similar to human cognition. In essence, AI makes it possible for machines to think. ML is a subset of AI that uses advanced algorithms to process massive amounts of data and learn from experience. ML can help machines improve their performance and accuracy over time without explicit programming.
Semiconductor design is a complex and challenging process that involves creating integrated circuits (ICs) that perform specific functions on a chip. Semiconductor design requires high levels of creativity, innovation, and precision, as well as rigorous testing and verification. As the demand for faster, smaller, cheaper, and more powerful chips increases, semiconductor designers face many challenges, such as:
- Increasing research and design costs for each new technology node
- Shortening product life cycles and time to market
- Optimizing portfolios and efficiency during the research and chip-design phase
- Eliminating defects, accelerating yield ramp-up, and decreasing costs
- Enhancing memory systems, data movement, and power efficiency
- Exploring new materials and devices for ultra-wide-bandgap semiconductors
This is where AI/ML can make a difference. By applying AI/ML techniques to various aspects of semiconductor design, such as circuit optimization, layout generation, verification, device modeling, simulation, characterization, material discovery, synthesis, fabrication, etc., semiconductor designers can achieve better results in terms of accuracy, efficiency, scalability, robustness, etc. AI/ML can also help semiconductor designers discover new possibilities and solutions that may not be obvious or feasible with conventional methods.
In this blog post, we will explore some of the applications and benefits of AI/ML in semiconductor design. We will also discuss some of the challenges and limitations of AI/ML in this domain. We hope that this post will inspire you to learn more about this exciting topic and how it can transform the semiconductor industry.
1. How AI/ML can help optimize portfolios and improve efficiency during the research and chip-design phase
One of the key applications of AI/ML in semiconductor design is to help semiconductor companies optimize their portfolios and improve efficiency during the research and chip-design phase. This phase involves exploring new ideas, developing new architectures, testing new concepts, and verifying new designs. It is a critical and costly phase that requires a lot of creativity, innovation, and precision.
AI/ML can help semiconductor companies in this phase by:
- Providing data-driven insights and recommendations for portfolio optimization. AI/ML can analyze market trends, customer needs, competitor strategies, technology roadmaps, and other relevant data to help semiconductor companies identify the most promising opportunities and allocate resources accordingly. AI/ML can also help semiconductor companies monitor and evaluate their portfolio performance and adjust their plans as needed.
- Automating and accelerating the design process. AI/ML can automate and speed up many tasks in the design process, such as circuit optimization, layout generation, verification, etc. AI/ML can also help designers explore more design options and find optimal solutions faster and more accurately. AI/ML can also help designers avoid or fix errors, bugs, or defects that may cause delays or failures in the design process.
- Enhancing collaboration and communication among designers. AI/ML can facilitate collaboration and communication among designers by providing common platforms, tools, and languages for data sharing, analysis, visualization, feedback, etc. AI/ML can also help designers learn from each other’s experiences and best practices and leverage collective intelligence.
By using AI/ML in the research and chip-design phase, semiconductor companies can achieve several benefits, such as:
- Improving their competitive edge by launching innovative products faster and more frequently
- Reducing their research and design costs by eliminating waste and inefficiency
- Increasing their customer satisfaction by delivering high-quality products that meet or exceed their expectations
- Enhancing their reputation and brand value by demonstrating their leadership and excellence in technology
Some examples of AI/ML use cases for optimizing portfolios and improving efficiency during the research and chip-design phase are:
- Intel uses AI/ML to optimize its product portfolio by analyzing market data, customer feedback, technology trends, etc. Intel also uses AI/ML to automate its design verification process by generating test cases, detecting errors, providing feedback, etc.
- Nvidia uses AI/ML to accelerate its chip design process by automating tasks such as layout generation, routing optimization, power analysis, etc. Nvidia also uses AI/ML to enhance its design quality by detecting defects, improving performance, reducing power consumption, etc.
- IBM uses AI/ML to facilitate its design collaboration by providing a cloud-based platform that enables data sharing, analysis, visualization, etc. IBM also uses AI/ML to leverage its design expertise by creating a knowledge base that captures best practices, lessons learned, etc.
2. How AI/ML can help eliminate defects, accelerate yield ramp-up, and decrease costs
Another important application of AI/ML in semiconductor design is to help semiconductor companies eliminate defects, accelerate yield ramp-up, and decrease costs. This application is especially relevant for the production phase, where semiconductor companies need to ensure the high quality and reliability of their products while minimizing waste and inefficiency.
AI/ML can help semiconductor companies in this phase by:
- Detecting and classifying defects automatically and accurately. AI/ML can use computer vision to analyze images of chips or wafers and identify any defects or anomalies that may affect their performance or functionality. AI/ML can also classify the defects according to their type, severity, location, cause, etc. This can help semiconductor companies reduce manual inspection efforts, improve quality control, and prevent faulty products from reaching customers.
- Optimizing and controlling process parameters. AI/ML can use data analytics to monitor and adjust the process parameters that affect the quality and yield of chips or wafers, such as temperature, pressure, voltage, etc. AI/ML can also use predictive modeling to anticipate and prevent potential issues or failures that may occur during the production process. This can help semiconductor companies improve process stability, efficiency, and consistency.
- Enhancing yield learning and ramp-up. AI/ML can use machine learning to learn from historical data and feedback and improve the production process over time. AI/ML can also use data mining to discover patterns and correlations among various factors that influence the yield of chips or wafers, such as design features, process steps, equipment settings, environmental conditions, etc. This can help semiconductor companies identify and eliminate yield limiters, optimize their product portfolio, and accelerate their time to market.
By using AI/ML in the production phase, semiconductor companies can achieve several benefits, such as:
- Increasing their product quality and reliability by reducing defects and errors
- Reducing their production costs by eliminating waste and inefficiency
- Increasing their product yield by maximizing output and minimizing losses
- Increasing their customer satisfaction by delivering products that meet or exceed their expectations
- Enhancing their reputation and brand value by demonstrating their excellence in quality and reliability
Some examples of AI/ML use cases for eliminating defects, accelerating yield ramp-up, and decreasing costs are:
- Intel uses AI/ML to detect and classify defects in its chips by using deep learning models that analyze images captured by optical microscopes. Intel also uses AI/ML to optimize its process parameters by using reinforcement learning algorithms that learn from real-time data.
- Google Cloud uses AI/ML to improve manufacturing quality control by providing a solution called Visual Inspection AI that uses computer vision to automatically detect product defects. Visual Inspection AI can also provide insights and recommendations for improving the production process.
- Applied Materials uses AI/ML to enhance yield learning and ramp-up by providing a solution called SmartFactory Yield Management that uses data analytics to identify yield limiters and suggest corrective actions. SmartFactory Yield Management can also provide a solution called SmartFactory Defect Management that uses machine learning to automate defect classification.
3. How AI/ML can help enhance memory systems, data movement, and power efficiency
A third application of AI/ML in semiconductor design is to help semiconductor companies enhance memory systems, data movement, and power efficiency. This application is especially relevant for the performance and functionality of chips or wafers, especially for AI applications that require large amounts of data processing and storage.
AI/ML can help semiconductor companies in this application by:
- Improving memory capacity and bandwidth. AI/ML can use novel memory technologies, such as resistive random-access memory (RRAM), to store large AI models in a small area footprint and consume very little power. AI/ML can also use advanced memory architectures, such as high bandwidth memory (HBM), to increase the data transfer rate between memory and processing units.
- Reducing data movement and latency. AI/ML can use compute-in-memory (CIM) techniques to perform AI computing directly within memory rather than in separate processing units. This can eliminate the data movement bottleneck and reduce the latency and energy consumption of AI processing.
- Optimizing power consumption and management. AI/ML can use power-aware algorithms and techniques to adjust the power supply and demand according to the workload and performance requirements of AI applications. AI/ML can also use predictive modeling and control to anticipate and prevent power issues or failures that may affect the quality and reliability of chips or wafers.
By using AI/ML in this application, semiconductor companies can achieve several benefits, such as:
- Increasing their product performance and functionality by enabling faster and more complex AI applications
- Reducing their product size and cost by using smaller and cheaper memory technologies
- Increasing their product lifetime and reliability by using less power and avoiding power-related problems
- Increasing their customer satisfaction by delivering products that meet or exceed their performance and functionality expectations
- Enhancing their reputation and brand value by demonstrating their leadership and excellence in technology
Some examples of AI/ML use cases for enhancing memory systems, data movement, and power efficiency are:
- Stanford University engineers developed a novel RRAM chip that does the AI processing within the memory itself, thereby eliminating the separation between the compute and memory units. Their CIM chip, called NeuRRAM, is about the size of a fingertip and does more work with limited battery power than what current chips can do.
- Google Cloud developed a solution called Visual Inspection AI that uses HBM to increase the bandwidth between memory and processing units. Visual Inspection AI uses computer vision to automatically detect product defects. HBM enables faster data transfer and higher performance for image analysis.
- IBM developed a solution called PowerAI that uses power-aware algorithms and techniques to optimize the power consumption and management of AI applications. PowerAI adjusts the power supply and demand according to the workload and performance requirements of AI applications. PowerAI also uses predictive modeling and control to prevent power issues or failures.
4. How AI/ML can help explore new materials and devices for ultra-wide-bandgap semiconductors
A fourth application of AI/ML in semiconductor design is to help semiconductor companies explore new materials and devices for ultra-wide-bandgap (UWBG) semiconductors. UWBG semiconductors are a new class of materials that have bandgap energies much greater than the 3.4 eV of GaN or 3.2 eV of SiC. They have many potential advantages over their narrower-bandgap counterparts in high-power and deep-UV applications, as well as in harsh environments.
AI/ML can help semiconductor companies in this application by:
- Discovering and designing new UWBG materials and devices. AI/ML can use data-driven methods to search for new UWBG materials and devices that have desirable properties and performance. AI/ML can also use physics-based methods to simulate and optimize the structure and behavior of new UWBG materials and devices.
- Characterizing and testing new UWBG materials and devices. AI/ML can use machine learning methods to analyze and interpret the experimental data obtained from new UWBG materials and devices. AI/ML can also use deep learning methods to extract features and patterns from the data that can reveal the underlying mechanisms and phenomena of new UWBG materials and devices.
- Developing and validating new models and theories for new UWBG materials and devices. AI/ML can use machine learning methods to develop and validate new models and theories that can explain and predict the behavior of new UWBG materials and devices. AI/ML can also use reinforcement learning methods to learn from trial-and-error experiments and improve the models and theories over time.
By using AI/ML in this application, semiconductor companies can achieve several benefits, such as:
- Expanding their knowledge and innovation frontier by discovering and designing new UWBG materials and devices
- Reducing their research and development costs by accelerating and automating the characterization and testing of new UWBG materials and devices
- Increasing their scientific understanding and confidence by developing and validating new models and theories for new UWBG materials and devices
- Increasing their competitive edge by launching novel products based on new UWBG materials and devices
Some examples of AI/ML use cases for exploring new materials and devices for UWBG semiconductors are:
- Stanford University engineers used a novel RRAM chip that does the AI processing within the memory itself to explore new UWBG materials such as BN, diamond, β -Ga 2 O 3, etc.. They used CIM techniques to perform AI computing directly within memory rather than in separate processing units.
- A research team from China used data-driven methods to search for new UWBG oxide semiconductors that have high breakdown electric fields. They used machine learning methods to screen a large number of candidate oxides based on their electronic structures.
- A research team from Japan used physics-based methods to simulate and optimize the performance of new UWBG devices based on β -Ga 2 O 3 . They used density functional theory (DFT) methods to calculate the band structures, carrier concentrations, mobility, etc. of β -Ga 2 O 3 devices.
Conclusion
In this blog post, we have explored some of the applications and benefits of AI/ML in semiconductor design. We have seen how AI/ML can help semiconductor companies optimize their portfolios and improve efficiency during the research and chip-design phase, eliminate defects, accelerate yield ramp-up, and decrease costs, enhance memory systems, data movement, and power efficiency, and explore new materials and devices for UWBG semiconductors. We have also discussed some of the challenges and limitations of AI/ML in this domain. We hope that this post has inspired you to learn more about this exciting topic and how it can transform the semiconductor industry. If you have any thoughts, questions, or feedback, please contact us. Thank you for reading!
