Senior Director of Semiverse™ Solutions, Lam Research
Dr. Joseph Ervin is the product line head of Semiverse™ Solutions group at Lam Research. Dr. Ervin joined Lam Research in 2017 as a part of Lam’s acquisition of Coventor. Previously, he worked for IBM on semiconductor device and integration development at multiple research and foundry locations, including IBM, ST Microelectronics, the College of Nanoscale Science and Engineering, and at GlobalFoundries. His current position includes managing software product development and deployment for next node semiconductor integration challenges, along with development of unique methods for modeling and solving these issues. He holds a Ph.D. in Device Physics from Arizona State University. He has over 60 issued patents and over 50 publications.
Presentation Title: A Single Digital Twin for Semiconductor Manufacturing? Semiverse™ Solutions and Building the Digital Family
Technology, and semiconductor technology in particular, has been advancing at a rapid and accelerating pace since the invention of the transistor over 75 years ago. We’ve now reached the point that smart manufacturing equipment, powered by advanced chip technology and artificial intelligence, is producing the next generation of computer chips. This has created a virtuous cycle of innovation, where computer chips and machine learning are helping to produce new, more powerful chips, but even faster and better than before.
In this talk, we will examine the use of digital twins and smart manufacturing equipment in the semiconductor industry, and how they are improving manufacturing productivity. We will address the concepts of intelligence and smart equipment, and review how smart fab tools enabled by machine learning (equipment digital twins) are being used to optimize high volume semiconductor manufacturing. In addition, we will discuss the concept of wafer process digital twins (virtual chip fabrication), and how these digital twins can accelerate time to yield. Finally, we will review a case study in process recipe development that demonstrates how human intelligence and experience can be complemented by machine learning to develop optimal process recipes at lower cost. We will conclude the talk by reviewing the challenges of using digital twins and propose solutions to meet those challenges.