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Analysis of Optoelectronic Characterization Data via Bayesian Inference: A Desktop-Scale MCMC Method
Conference proceeding

Analysis of Optoelectronic Characterization Data via Bayesian Inference: A Desktop-Scale MCMC Method

Calvin Fai, Gregory A. Manoukian, Jason B. Baxter, Anthony J.C. Ladd and Charles J. Hages
2023 IEEE 50th Photovoltaic Specialists Conference (PVSC), pp 1-1
11 Jun 2023
DOI: https://doi.org/10.1109/PVSC48320.2023.10359652

Abstract

Doping Markov processes Monte Carlo methods Optical variables measurement Photoluminescence Photovoltaic systems Radiative recombination
The recovery of characteristic absorber parameters such as the carrier mobility, doping concentration, and rate constants of each carrier recombination mechanism from optical characterization measurements has historically been hindered by the complexity of the carrier dynamics. This necessitates the use of simplified, analytically solvable physics models that sacrifice some of the potential information content of the measurement data. In this work, we introduce a desktop-scale Markov Chain Monte Carlo (MCMC) sampler that utilizes simulation of the full carrier physics to recover material parameters with increased accuracy or which were previously inaccessible. From a "power scan" consisting of time-resolved photoluminescence (TRPL) data at varying excitation intensity, we recover the ambipolar carrier mobility, the doping concentration, and rate constants for Auger and bimolecular radiative recombination. We also obtain an effective lifetime for defect-assisted nonradiative recombination, which can be decomposed further into bulk and surface recombination components by introducing additional data from multiple material sample thicknesses. These results reaffirm the potential for simulation-driven statistical analyzers to greatly expand the utility of optical characterization measurements, though herein the need for expensive computational resources is not required.

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