Modeling and optimization of process parameters for GaAs/AlGaAs multiple quantum well avalanche photodiodes using genetic algorithms

In this paper, we present a parameter optimization technique for GaAs/AlGaA s multiple quantum well avalanche photodiodes used for the image capture me chanism in a high-definition system. Even under a flawless environment in a semiconductor manufacturing process, random variation in the process param eters can cause fluctuation in the device performance. The precise modeling for this variation is thus required for accurate: prediction of device per formance. This paper will first use experimental design and neural networks to model the nonlinear relationship between device process parameters and device performance parameters. The derived model is then put into genetic a lgorithms to acquire optimized device process parameters. From the optimize d technique, we can predict device performance before high-volume manufactu ring, and also increase production efficiency. (C) 2001 Elsevier Science Lt d. All rights reserved.