Improvement of Virtual Diagnostics Performance for Plasma Density in Semiconductor Etch Equipment Using Variational Auto-Encoder

As the critical dimension of transistors has become lower and the stacked layer of semiconductors has become higher, virtual diagnostics to monitor the status of plasma in an etching process has been important because of the reliability of process. In this study, we proposed the model to predict a plasma density of the etch equipment with high accuracy using OES data despite a small number of process conditions. The proposed model could improve the prediction performance of multilayer perceptron by using pre-trained variational auto-encoder as an initializer and had the best performance of several regression methods. At application point of view, it is expected that the model can be used to monitor a plasma density when a wafer is absent from the chamber. Moreover, using the proposed model can more easily understand the status of plasma rather than monitoring thousands of sensor data even without the knowledge about plasma.