Abstract
While there have been many studies using machine learning (ML) algorithms to predict process outcomes and device performance in semiconductor manufacturing, the extensively developed technology computer-aided design (TCAD) physical models should play a more significant role in conjunction with ML. While TCAD models have been effective in predicting the trends of experiments, a machine learning statistical model is more capable of predicting the anomalous effects that can be dependent on the chambers, machines, fabrication environment, and specific layouts. In this paper, we use an analytics-statistics mixed training (ASMT) approach using TCAD. Under this method, the TCAD models are incorporated into the machine learning training procedure. The mixed dataset with the experimental and TCAD results improved the prediction in terms of accuracy. With the application of ASMT to the BOSCH process, we show that the mean square error (MSE) can be effectively decreased when the analytics-statistics mixed training (ASMT) scheme is used instead of the classic neural network (NN) used in the baseline study. In this method, statistical induction and analytical deduction can be combined to increase the prediction accuracy of future intelligent semiconductor manufacturing.
Highlights
Machine learning is widely applied to many fields, such as medical imaging [1,2,3,4], financial crises [5,6], biology [7,8,9,10,11], and traffic classification [12,13,14]
With the application of ASMT to the BOSCH process, we show that the mean square error (MSE) can be effectively decreased when the analytics-statistics mixed training (ASMT) scheme is used instead of the classic neural network (NN) used in the baseline study
The effectiveness of machine learning in intelligent semiconductor manufacturing can become more pronounced by incorporating technology computer-aided design (TCAD) analytical models in terms of cost-efficiency, prediction accuracy, and reduced trial-and-error cycles
Summary
Machine learning is widely applied to many fields, such as medical imaging [1,2,3,4], financial crises [5,6], biology [7,8,9,10,11], and traffic classification [12,13,14]. The complexity of the irregular sample space together with the large number of input features makes predicting the semiconductor process a very challenging problem. This problem paves way for the use of machine learning in this area to find optimal solutions. There have been some previous studies on machine learning applied to semiconductor
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