Method for Predicting Pre-Failure States of Microelectronic Manufacturing Equipment Based on Digital Twin and Intelligent Analysis of Process Parameters

Abstract

The paper proposes a method for predicting pre-failure states of microelectronic manufacturing equipment based on Digital Twin technology and intelligent analysis of process parameters, providing equipment health monitoring, anomaly detection, and simulation of equipment degradation scenarios. The method employs machine learning models and time-series analysis to predict the probability of a pre-failure state, estimate the Remaining Useful Life (RUL), and calculate a dynamic risk index. The integration of prediction results with a decision support system and a continuous learning loop creates the prerequisites for a transition from reactive maintenance to proactive prediction and prevention of equipment failures.