Statistical experimental design for MBE process characterization

Abstract

This paper presents a statistically designed experiment for systematic characterization of the molecular beam epitaxy (MBE) process to quantitatively describe the effects of process conditions on the qualities of grown films. This methodology is applied to a five-layer, undoped AlGaAs and InGaAs single quantum well structure grown on a GaAs substrate. Six input factors (time and temperature for oxide removal, substrate temperatures for AlGaAs and InGaAs layer growth, beam equivalent pressure of the As source and quantum well interrupt time) are examined by means of a Resolution IV, 26-2 fractional factorial design requiring sixteen trials. Several responses are characterized, including defect density, x-ray diffraction, and photoluminescence.Results indicate that the manipulation of each of the six factors over the ranges examined are statistically significant and lead to considerable variation in the responses. Following characterization, backpropagation neural networks are trained to model the process responses. The neural process models exhibit very good agreement with experimental results.

Yield prediction models for optimization of high-speed micro-processor manufacturing processes

“…For example, Himmel and May reported 40-70% improvement in experimental error, as well as nearly a 40% improvement in generalization by neural networks as compared to response surface models in the plasma etch application [2]. Neural process modeling schemes have also been applied to many other semiconductor manufacturing processes, including plasma enhanced chemical vapor deposition (PECVD) process [3], molecular beam epitaxy (MBE) process [4], and multichip modules (MCM) packaging [5].…”