In-situ monitoring of plasma equipment using spectrophotometric colorimetry and neural network

Plasma plays a critical role in fabricating thin films for manufacturing electronic devices. To improve equipment throughput and device yield, plasma should be strictly monitored. A new technique is presented to monitor process plasma. This is accomplished by using a spectrophotometer and a neural network. The spectrometer was used to calculate color chromaticity coordinates and color temperature. Their sensitivity was examined as a function of radio frequency source and bias powers. All the variables linearly increased or decreased for process-induced faults. For an in-situ monitoring, the color variables were modeled by using an auto-correlated type of a time-series neural network and model prediction was applied to a CUSUM control chart. Unlike conventional models, the neural network trained with a reference pattern was utilized for fault detection. The constructed models demonstrated prediction errors ranging from 0.018 to 0.027%. A presented model-based CUSUM yielded improved fault sensitivity over a raw sensor data-based one. The improvement was more drastic for the bias power-induced faults. The proven high sensitivity can be effectively applied to monitor plasma equipment in real-time.