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].

Thermal Analysis of Flare Containment Structure

SC Solutions delivered to East Bay Municipal Utility District (EBMUD) the results of a thermal analysis of the containment structure of their waste gas burners. EBMUD’s Main Wastewater Treatment Plant (MWWTP) in Oakland, CA operates a digester facility where...

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,...

Reactor-scale Modeling of Silicon Epitaxy

This case study of epitaxial deposition of silicon film on a silicon substrate in a horizontal hot-wall reactor reproduces an earlier modeling study by Habuka et al. [1]. The steady-state finite element (FEM) model incorporates fluid flow, heat transfer, dilute...