A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine Learning

Abstract

This work presents an overview of a set of in-house-built software tools intended for state-of-the-art semiconductor device modelling, ranging from numerical simulators to post-processing tools and prediction codes based on statistics and machine learning techniques. First, VENDES is a 3D finite-element based quantum-corrected semi-classical/classical toolbox able to characterise the performance, scalability, and variability of transistors. MLFoMPy is a Python-based tool that post-processes IV characteristics, extracting the most relevant figures of merit and preparing the data for subsequent statistical or machine learning studies. FSM is a variability prediction tool that also pinpoints the most sensitive regions of a device to a specific source of fluctuation. Finally, we also describe machine learning-based prediction tools that were used to obtain full IV curves and specific figures of merit of devices suffering the influence of several sources of variability.

Keywords

• Semiconductor devices
• 3D modelling
• finite element method
• quantum corrections
• variability
• post-processing tools