Parametric manufacturing yield modeling of GaAs/AlGaAs multiple quantum well avalanche photodiodes

摘要

GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APD's) are of interest as an ultra-low noise image capture mechanism for high-definition systems. Since literally millions of these devices must be fabricated for imaging arrays, it is critical to evaluate potential performance variations of individual devices in light of the realities of semiconductor manufacturing. Specifically, even in a defect-free manufacturing environment, random variations in the fabrication process will lead to varying levels of device performance, Accurate device performance prediction requires precise characterization of these variations. This paper presents a systematic methodology for modeling the parametric performance of GaAs MQW APD's. The approach described requires a model of the probability distribution of each of the relevant process variables, as well as a second model to account for the correlation between this measured process data and device performance metrics. The availability of these models enables the computation of the joint probability density function required for predicting performance using the Jacobian transformation method. The resulting density function can then be numerically integrated to determine parametric yield. Since they have demonstrated the capability of highly accurate function approximation and mapping of complex, nonlinear data sets, neural networks are proposed as the preferred tool for generating the models described above. In applying this methodology to MQW APD's, it is shown that using a small number of test devices with varying active diameters, barrier and well widths, and doping concentrations enables prediction of the expected performance variation of APD gain and noise in larger populations of devices. This approach compares favorably with Monte Carlo techniques and allows device yield prediction prior to high volume manufacturing in order to evaluate the impact of both design decisions and process capability.