Machine learning analysis of structural data to predict electronic properties in near-surface InAs quantum wells

TL;DR

This paper develops a machine learning approach to analyze AFM crosshatch patterns in InAs quantum wells, linking pattern features to dislocation density and electron transport properties, thus enabling non-invasive quality assessment.

Contribution

It introduces a novel computer vision and machine learning framework to correlate crosshatch patterns with dislocation density and electron mobility in quantum wells.

Findings

Optimized crosshatch pattern parameters for electron transport.

Machine learning model accurately predicts dislocation density from AFM images.

Dislocation scattering significantly impacts electron mean free path below 200 nm.

Abstract

Semiconductor crosshatch patterns in thin film heterostructures form as a result of strain relaxation processes and dislocation pile-ups during growth of lattice mismatched materials. Due to their connection with the internal misfit dislocation network, these crosshatch patterns are a complex fingerprint of internal strain relaxation and growth anisotropy. Therefore, this mesoscopic fingerprint not only describes the residual strain state of a near-surface quantum well, but also could provide an indicator of the quality of electron transport through the material. Here, we present a method utilizing computer vision and machine learning to analyze AFM crosshatch patterns that exhibits this correlation. Our analysis reveals optimized electron transport for moderate values of $\lambda$ (crosshatch wavelength) and $\epsilon$ (crosshatch height), roughly 1 $\mu$m and 4 nm, respectively, that define the average waveform of the pattern. Simulated 2D AFM crosshatch patterns are used to train a machine learning model to correlate the crosshatch patterns to dislocation density. Furthermore, this model is used to evaluate the experimental AFM images and predict a dislocation density based on the crosshatch waveform. Predicted dislocation density, experimental AFM crosshatch data, and experimental transport characterization are used to train a final model to predict 2D electron gas mean free path. This model shows electron scattering is strongly correlated with elastic effects (e.g. dislocation scattering) below 200 nm $\lambda_{MFP}$.