Ex Situ Plasma Diagnosis by Recognition of X-Ray Photoelectron Spectroscopy Data Using a Neural Network

摘要

To improve equipment throughput and device yield, faults in plasma equipment should be stringently diagnosed. An ex situ diagnosis technique is presented. This was accomplished by recognizing X-ray photoelectron spectroscopy pattern by using a modular backpropagation neural network (BPNN). Each BPNN comprising a modular network was specific to a variation in a process parameter. For comparison, principal component analysis (PCA) was applied to X-ray photoelectron spectroscopy (XPS) data and with these data another modular network was constructed. A total of 17 XPS patterns were used to construct a modular network. Model performance was evaluated in terms of the recognition and diagnosis accuracies. Model accuracy was also investigated as a function of hidden neuron number or threshold. The optimized model trained with PCA-XPS with 100 percent data variance demonstrated a smaller prediction error compared to XPS and PCA-XPS with 99 percent data variance. Meanwhile, the PCA-XPS model with 100 percent data variance yielded a significant improvement of about 32 percent in fault diagnosis compared to pure XPS model. The improvement was more pronounced under stricter monitoring conditions.