Machine Learning Based on Digital Image Colorimetry Driven In Situ, Noncontact Plasma Etch Depth Prediction

Abstract

This study presents a noncontact, in situ framework for etch depth prediction in plasma etching using machine learning (ML) and digital image colorimetry (DIC). While conventional ex situ methods offer accuracy, they suffer from delays and contamination risks. To overcome these, two approaches are explored. First, etch depth is initially obtained through ellipsometry mapping and used to train an artificial neural network (ANN) based on process parameters (e.g., plasma power, pressure, and gas flow), achieving significantly lower mean squared error (MSE) than a linear baseline. This is extended with a Bayesian neural network (BNN) to capture uncertainty in the predictions. Second, it is demonstrated that red, green, and blue data from DIC alone can effectively predict etch depth without relying on process parameters. Together, these findings establish ML-DIC integration as a real-time, low-cost, and noninvasive alternative for plasma process monitoring.