Abstract: Methods and systems for using a time-series of spectra to identify endpoint of a multi-step semiconductor fabrication processes such as multi-step deposition and multi-step etch processes. One method includes accessing a virtual carpet (e.g., a machine learning model) that is formed from a time-series of spectra for the multi-step processes collected during a training operation. During production, in-situ time-series of spectra are compared to the virtual carpet as part of end pointing of multi-step fabrication processes.

Abstract: Various embodiments herein relate to systems and methods for adaptive model training. In some embodiments, a computer program product for adaptive model training is provided, the computer program product comprising a non-transitory computer readable medium on which is provided computer-executable instructions for: receiving, from a plurality of process chambers, ex situ data associated with wafers fabricated using the process chambers and in situ measurements, wherein a first machine learning model is used to predict the ex situ data using the in situ measurements; calculating a metric indicating an error associated with the first machine learning model; determining whether to update the first machine learning model; and generating a second machine learning model using the ex situ data and the in situ measurements.