Process Optimization of the Contact Formation for High Efficiency Solar Cells Using Neural Networks and Genetic Algorithms

신경망과 유전알고리즘을 이용한 고효율 태양전지 접촉형성 공정 최적화

  • 정세원 (명지대학교 정보공학과 & NPTC/명지 IT중점연구소) ;
  • 이성준 (명지대학교 정보공학과 & 명지 IT중점연구소) ;
  • 홍상진 (명지대학교 정보공학과 & 명지 IT중점연구소) ;
  • 한승수 (명지대학교 정보공학과 & 명지 IT중점연구소)
  • Published : 2006.11.30

Abstract

This paper presents modeling and optimization techniques for hish efficiency solar cell process on single-crystalline float zone (FZ) wafers. Among a sequence of multiple steps of fabrication, the followings are the most sensitive steps for the contact formation: 1) Emitter formation by diffusion; 2) Anti-reflection-coating (ARC) with silicon nitride using plasma-enhanced chemical vapor deposition (PECVD); 3) Screen-printing for front and back metalization; and 4) Contact formation by firing. In order to increase the performance of solar cells in terms of efficiency, the contact formation process is modeled and optimized using neural networks and genetic algorithms, respectively. This paper utilizes the design of experiments (DOE) in contact formation to reduce process time and fabrication costs. The experiments were designed by using central composite design which consists of 24 factorial design augmented by 8 axial points with three center points. After contact formation process, the efficiency of the fabricated solar cell is modeled using neural networks. Established efficiency model is then used for the analysis of the process characteristics and process optimization for more efficient solar cell fabrication.

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