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

Eui Seung Kim, Changhoon Oh, Seogoo Lee, Bongyong Lee, Ilgu Yun

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)563-567
Number of pages5
JournalMicroelectronics Journal
Volume32
Issue number7
DOIs
Publication statusPublished - 2001 Jul

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

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