Dynamics-inspired feature extraction in semiconductor manufacturing processes

Abstract

Abstract The ability to exploit data-driven process control and decision making frameworks is rapidly becoming critical to success in semiconductor manufacturing. At the same time, advances in manufacturing equipment sensors has seen dramatic increases in sampling rates in recent years, which has led to the ability to capture transients effects in signals with higher fidelity than previously possible. It is known that data-driven process control and decision making methodologies rely on the process of extraction of useful information from raw data signals. To that end, the current manuscript presents a novel methodology for extraction of information from data in the form of a feature set that faithfully and reliably depicts both the transient and stationary portions of the signals. The solution proposed is an automated dynamics-inspired approach that looks to segment a signal into steady state and transient components before summarizing each segment into a set of relevant signatures. The steady state segments are summarized through a set of statistics and each transient is reduced to a set of parameters relating to the underlying system dynamics, such as settling time, rise time, overshoots, etc. The impactful novel information content of the resulting dynamics-inspired feature set is evaluated by application to chamber matching, product defect level prediction and product quality characteristic prediction in etch and deposition processes executed in various tools across several modern 300 mm fabs.