Dr. Kapil Sawlani
Senior Data Scientist & Engineering Manager Lam Research Corporation
Kapil Sawlani is a Senior Data Scientist and Engineering Manager at Lam Research focusing on introduction of novel sensors and use of state-of-the-art AI algorithms for improving process and productivity of Lam equipment. He joined Lam with a Ph.D. in Nuclear Engineering and Radiological Sciences (Plasma physics) from the University of Michigan. In addition, he has dual Master’s in Aerospace and Nuclear Engineering from the University of Michigan and a bachelor’s degree in Mechanical Engineering from the University of Pune, India. In all his work, he used data analytics to drive to conclusions and provide automated solutions to improve efficiency for the process team. He has given several talks and posters demonstrating use cases and value of automation and AI/ML and has over 23 patents in the areas of process, productivity, simulations, and data science applying to the semiconductor industry. He works collaboratively with several faculty members from leading Universities and startups on exploring novel process methods for deposition, new sensor development, use of AI/ML for detection, prediction, and control for improvement of process efficiency and semiconductor equipment design. Recently, he co-authored a book chapter on “Perspectives of Artificial Intelligence for Plasma-Assisted Manufacturing in the Semiconductor Industry".
Presentation Title
Predictive and Autonomous Operations of Semiconductor Equipment with Lam’s Equipment Intelligence®
Presentation Abstract
A semiconductor equipment today generates large volumes of data due to more complex deposition or etch processes, presence of many sensors, constant logging of tool events, and many other data types being introduced by equipment makers. Continuous improvement in computing hardware, modern AI/ML algorithms, and new ways to interact with systems (extended reality, haptic feedback, voice-based or chat-based agents, etc.) enables acceleration of new process and product introduction to market, as well as drive greater productivity for existing tools. While many use cases of implementation of AI/ML have been demonstrated in isolation and shown to increase efficiency, productivity, and/or yield; the integration of these use cases in a framework towards long term objectives have not been fully demonstrated. When these technology implementations are combined, they provide far greater value to enable a smart enterprise.
This talk will discuss examples on how predictive and autonomous operations can happen at an equipment fab level, at a fleet level with multiple tools from the same family, at a tool level containing multiple modules, and at a module level. At each scale, new opportunities present themselves for greater productivity and move us one step closer to the ultimate vision – The Lights Out Fab. As we work towards our destination, we will demonstrate what is being done with Lam’s Equipment Intelligence® to improve speed and productivity using AI/ML and how connected smart systems can be leveraged to provide an integrated end-to-end solution in the context of virtual troubleshooting and recovery in the Semiverse.