High Performance SiGe HBT Performance Variability Learning by Utilizing Neural Networks and Technology Computer Aided Design

Abstract

Improvements in uniformity of electrical device performance is often not taken into major consideration until a certain maturity level of a technology is reached. In this work, use of technology computer aided design (TCAD) and process compact models (PCM) developed from neural networks demonstrate their utility in process-parameter variation understanding in earlier stages of technology development. A third generation High Performance Silicon Germanium Heterojunction Bipolar Transistor (SiGe HBT) was modelled and simulated as the device of focus in this study.