Physics-informed Machine Learning for Control – DNN in a Dynamic Feedback Loop

This Case Study describes an approach to combining physical principles with Machine Learning (ML) for modeling and control of complex systems. Our approach was developed as part of a DARPA-funded research project. It was applied to oil reservoir management. While this Case Study provides an overview, technical details may be found in a separate publication [1].

SC‐MDD™ Compact Whole‐Wafer Scanner

SC Solutions’ Macro Defect Detection system, SC‐MDD™, is a production‐proven tool that rapidly detects and classifies macro defects for every wafer being processed. SC‐MDD™ includes scanner hardware as well as SC‐WDD™ software which controls the scanning process,...