A study in dynamic neural control of semiconductor fabrication processes

This paper describes a generic dynamic control system designed for use in semiconductor fabrication process control. The controller is designed for any batch silicon wafer process that is run on equipment having a high number of variables that are under operator control. These controlled variables include both equipment state variables such as power, temperature, etc. and the repair, replacement, or maintenance of equipment parts, which cause parameter drift of the machine over time. The controller consists of three principal components: 1) an automatically updating database, 2) a neural-network prediction model for the prediction of process quality based on both equipment state variables and parts usage, and 3) an optimization algorithm designed to determine the optimal change of controllable inputs that yield a reduced operation cost, in-control solution. The optimizer suggests a set of least cost and least effort alternatives for the equipment engineer or operator. The controller is a PC-driven software solution that resides outside the equipment and does not mandate implementation of recommendations in order to function correctly. The neural model base continues to learn and improve over time. An example of the dynamic: process control tool performance is presented retrospectively for a plasma etch system. In this study, the neural networks exhibited overall accuracy to within 20% of the observed values of .986, .938, and .87 for the output quality variables of etch rate, standard deviation, and selectivity, respectively, based on a total sample size of 148 records, The control unit was able to accurately detect the need for parts replacements and wet clean operations in 34 of 40 operations. The controller suggested chamber state variable changes which either improved performance of the output quality variables or adjusted the input variable to a lower cost level without impairment of output quality.