Training Spectrum Generation for Machine Learning System for Spectrographic Monitoring

A method of controlling processing of a substrate, comprising:

• measuring a first plurality of training spectra from one or more sample substrates;
• measuring a characterizing value for each training spectra of the plurality of training spectra to generate a plurality of characterizing values with each training spectrum having an associated characterizing value;
• measuring a plurality of dummy spectra during processing of one or more dummy substrates;
• generating a second plurality of training spectra by combining the first plurality of training spectra and the plurality of dummy spectra, there being a greater number of spectra in the second plurality of training spectra than in the first plurality of training spectra, each training spectrum of the second plurality of training spectra having an associated characterizing value;
• training an artificial neural network using the second plurality of training spectra, the artificial neural network having a plurality of input nodes to a plurality of spectral values, an output node to output the characterizing value, and a plurality of hidden nodes connecting the input nodes to the output node;
• receiving, from an in-situ optical monitoring system, a measured spectrum of light reflected from a substrate undergoing processing that modifies a thickness of an outer layer of the substrate;
• generating a measured characterizing value for the measured spectrum by applying spectral values from the measured spectrum into the artificial neural network;
• and
• at least one of halting processing of the substrate or adjusting a processing parameter based on the characterizing value.

A computer program product for controlling processing of a substrate, the computer program product tangibly embodied in a non-transitory computer readable media and comprising instructions for causing a processor to:

• receive a first plurality of training spectra and plurality of characterizing values, each training spectrum of the plurality of training spectra having an associated characterizing value from the plurality of characterizing values;
• receive a plurality of dummy spectra representing spectra obtained from processing of one or more dummy substrates;
• generate a second plurality of training spectra by combining the first plurality of training spectra and the plurality of dummy spectra, there being a greater number of spectra in the second plurality of training spectra than in the first plurality of training spectra, each training spectrum of the second plurality of training spectra having an associated characterizing value;
• train an artificial neural network using the second plurality of training spectra, the artificial neural network having a plurality of input nodes to a plurality of spectral values, an output node to output the characterizing value, and a plurality of hidden nodes connecting the input nodes to the output node;
• receive, from an in-situ optical monitoring system, a measured spectrum of light reflected from a substrate undergoing processing that modifies a thickness of an outer layer of the substrate;
• generate a measured characterizing value for the measured spectrum by applying spectral values from the measured spectrum into the artificial neural network;
• and
• at least one of halt processing of the substrate or adjust a processing parameter based on the characterizing value.