In this paper, we present a parameter optimization technique for GaAs/AlGaAs multiple quantum well avalanche photodiodes used for the image capture mechanism in a high-definition system. Even under a flawless environment in a semiconductor manufacturing process, random variation in the process parameters can cause fluctuation in the device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. 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 algorithms to acquire optimized device process parameters. From the optimized technique, we can predict device performance before high-volume manufacturing, and also increase production efficiency.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Condensed Matter Physics
- Surfaces, Coatings and Films
- Electrical and Electronic Engineering