Plasma diagnosis by recognizing in situ data using a modular backpropagation network

Accurate and fast diagnosis of a plasma equipment is crucial to maintain device yield and throughput. A methodology is presented to identify plasma faults. Particular emphasis is placed on the use of in situ diagnostic data in conjunction with a modular backpropagation neural network (BPNN). The experimental data were collected from a real-time impedance match monitor system. Two match positions were used as the signature of an anomaly in equipment plasma. Fault patterns were experimentally generated with a variation in process factors, including radio frequency source power, pressure, O2 and Ar flow rates. A total of 30 experiments were conducted and subsequently divided into 20 training and 10 test data. Prediction accuracy and fault sensitivity were measured as a function of training factors, hidden neuron and initial weight distribution. The sensitivity was further evaluated from the standpoint of a single and modular network. Modular network consisted of four single networks, specific to variations in process factors. Adjusting initial weight distribution improved fault sensitivity in either network. Compared to single network, modular network greatly improved fault sensitivity irrespective of thresholds.