Plasma Diagnosis by Using Atomic Force Microscopy and Neural Network

Atomic Force Microscopy와 신경망을 이용한 플라즈마 진단

A new diagnosis model was constructed by combining atomic force microscopy (AFM), wavelet, and neural network. Plasma faults were characterized by filtering AFM-measured etch surface roughness with wavelet. The presented technique was evaluated with the data collected during the etching of silicon oxynitride thin film. A total of 17 etch experiments were conducted. Applying wavelet to AFM, surface roughness was detailed into vertical, horizon%at, and diagonal components. For each component, neural network recognition models were constructed and evaluated. Comparisons revealed that the vertical component-based model yielded about 30% improvement in the recognition accuracy over others. The presented technique was evaluated with the data collected during the etching of silicon oxynitride thin film. A total of 17 etch experiments were conducted