Virtual metrology method based on convolutional autoencoder and transfer learning and system thereof

Abstract

A virtual metrology method based on convolutional autoencoder and transfer learning includes performing a data alignment operation, a modeling operation and a calculating operation. The data alignment operation includes performing a data-length adjusting operation onto a plurality of sets of process data. The modeling operation includes classifying paired data and unpaired process data; creating a pre-trained model by using the unpaired process data, and then inputting the paired data to the pre-trained model to create a virtual metrology model based on convolutional autoencoder. The virtual metrology model based on convolutional autoencoder includes at least one convolutional neural network model. In addition, the calculating operation includes executing one of a predicting step and a transfer learning step according to whether the actual metrology data is obtained, thereby calculating one of a phase-one virtual metrology value and a phase-two virtual metrology value.