Advancing MOSFET Fault Type Detection Through Data-Driven Unsupervised Learning

Abstract

The Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) is a fundamental component in modern electronics, playing a vital role in the amplification and switching of signals within digital circuits. It regulates current flow in response to applied voltage. MOSFETs consist of four terminals: Gate (IG), Drain (ID), Source (IS), and Background or Body (IB). Nevertheless, during the design and manufacturing processes, certain devices are classified as non-functional due to specific abnormalities in their terminals. Identifying the nature of these defects during production can significantly enhance quality control by enabling more accurate detection and correction of faults. This study investigates the use of Artificial Intelligence (AI) and data-driven approaches by applying unsupervised learning techniques to classify the characteristics of faulty MOSFETs. Unsupervised learning enables the analysis of large datasets to examine abnormal devices and distinguish between them based on their distinct properties. By integrating AI-driven diagnostics into the production pipeline, our objective is to establish a system that not only improves yield and operational efficiency but also enhances the reliability of MOSFETs in end-use applications. This approach aims to establish a new standard for precision in quality control semiconductor manufacturing.