Optimal design using neural network and information analysis in plasma etching

The goal of this article is to use operating data-based approaches for automating the manufacturing of submicron flash memory devices in semiconductor. This novel technique which combines the neural network and information inductive analysis has recently been proposed. It is used in this article to generate the recipe for plasma etching process design. Traditional plasma etching variables such as pressure, gas flows, temperature, rf power, etc., are used to build the neural network for predicting etching rate of polysilicon and field oxide, and the uniformity of field oxide. The information inductive analysis based on the information entropy and fuzzy clustering analysis is then utilized to look for the candidate points in each optimal region whose response surface is constructed based on the neural network model. With only a few runs, the best optimal condition getting close to the design requirements is found. Since the complexity of plasma modeling and design at the equipment level is presently ahead of theoretical method from a fundamental physical standpoint, the proposed method can effectively cope with nonlinear characteristics in the plasma etching process, giving good design directions and taking advantage of traditional statistical approaches. Using the proposed method within four runs and a total of 26 experimental points, the recipe that meets all specifications is found.