This paper presents the modeling methodology of Zn diffusion process utilized for high-speed avalanche photodiode fabrication using neural networks. The modeling scheme can characterize the effects of diffusion process conditions on the performance metrics of diffusion process. Three different InP-based test structures with different doping concentrations in diffused layer are explored. Three input factors (sealing pressure, amount of Zn 3P2 source per volume, and doping concentration of diffused layer) are examined with respect to the two performance metrics (diffusion-rate and Zn doping concentration) by means of D-optimal design experiment. Diffusion rate and Zn doping concentration in diffused layer are characterized by a response model generated by training feed-forward error back-propagation neural networks. It is observed that the neural network based process models developed here exhibit good agreement with experimental results.
Bibliographical noteFunding Information:
The authors would like to thank Y.K. Kim for her aid in performing SIMS measurements. This work was supported by Yonsei University Research Fund of year 2000.
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