In this letter, we demonstrated the possibility of predicting full transistor current-voltage (IV)
and capacitance-voltage (CV) curves using machines trained by Technology Computer …

This review investigates the possibility of using Machine Learning as a replacement for
numerical TCAD device simulation. As the chip design is getting complex to incorporate …

In this article, using experimental data, we demonstrate that the technology computer-aided
design (TCAD) is a very cost-effective tool to generate the data to build machine learning …

A machine learning (ML) model by combing two autoencoders and one linear regression
model is proposed to avoid overfitting and to improve the accuracy of Technology Computer …

This work, for the first time, experimentally demonstrates a TCAD-Machine Learning (TCAD-
ML) framework to assist the analysis of device-to-device variation and operating (ambient) …

The sensitivity of semiconductor devices to any microscopic perturbation is increasing with
the continuous shrinking of device technology. Even the small fluctuations have become …

In this paper, we propose and study the use of noise to avoid the overfitting issue in
Technology Computer-Aided Design-augmented machine learning (TCAD-ML). TCAD-ML …

In this article, the possibilities of accurate prediction of wide range of parameters and
optimizing the same through machine learning (ML) approach have been demonstrated for …

The selection of design structure parameters is a critical step for meeting the performance
requirement for silicon on insulator (SOI) lateral power devices, especially when multiple …

This paper presents a novel approach to enable real-time device simulation and
optimization. State-of-the-art algorithms which can describe semiconductor domain are …