Neural Network Recognition of Scanning Electron Microscope Image for Plasma Diagnosis

To improve equipment throughput and device yield, a malfunction in plasma equipment should be accurately diagnosed. An ex-situ diagnosis model was constructed by applying neural network to scanning electron microscope (SEM) image of plasma-etched patterns. The experimental data were collected from a plasma etching of tungsten thin films. Faults in plasma were generated by simulating a variation in process parameters. Feature vectors were obtained by applying direct and wavelet techniques to SEM images. The wavelet techniques generated three feature vectors composed of detailed components. The diagnosis models constructed were evaluated in terms of the recognition and diagnosis accuracies. The direct technique yielded much smaller recognition accuracy with respect to the wavelet technique. The improvement was about 82%. For the diagnosis accuracy, the improvement was about 30%. All these results demonstrate that the direct method is a more effective feature extraction in constructing a SEM-based neural network diagnosis model.