Training Spectrum Generation for Machine Learning System for Spectrographic Monitoring

A computer program product for generating spectra for training of a neural network, the computer program product tangibly embodied in a non-transitory computer readable media and comprising instructions for causing a processor to:

• generate a plurality of theoretically generated initial spectra from an optical model;
• send the plurality of theoretically generated initial spectra to a feedforward neural network to generate a plurality of modified theoretically generated spectra;
• send an output of the feedforward neural network and empirically collected spectra to a discriminatory convolutional neural network;
• determine that the discriminatory convolutional neural network does not discriminate between the modified theoretically generated spectra and empirically collected spectra;
• and
• thereafter, generate a plurality of training spectra from the feedforward neural network.

A method of controlling processing of a substrate, comprising:

• generating a plurality of theoretically generated initial spectra from an optical model;
• sending the plurality of theoretically generated initial spectra to a feedforward neural network to generate a plurality of modified theoretically generated spectra;
• sending an output of the feedforward neural network and empirically collected spectra to a discriminatory convolutional neural network;
• determining that the discriminatory convolutional neural network does not discriminate between the plurality of modified theoretically generated spectra and empirically collected spectra;
• thereafter, generating a plurality of training spectra from the feedforward neural network;
• training an artificial neural network using the 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:

• generate a plurality of theoretically generated initial spectra from an optical model;
• send the plurality of theoretically generated initial spectra to a feedforward neural network to generate modified theoretically generated spectra;
• send an output of the feedforward neural network and empirically collected spectra to a discriminatory convolutional neural network;
• determine that the discriminatory convolutional neural network does not discriminate between the modified theoretically generated spectra and empirically collected spectra;
• thereafter, generate a plurality of training spectra from the feedforward neural network;
• train an artificial neural network using the 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.