Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization

TL;DR

Automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows are proposed that significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Abstract

High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis, key material properties must be measured and characterized to validate discovery and provide feedback to optimization cycles. However, with the boom in development of high-throughput synthesis tools that champion production rates up to $10^4$ samples per hour with flexible form factors, most sample characterization methods are either slow (conventional rates of $10^1$ samples per hour, approximately 1000x slower) or rigid (e.g., designed for standard-size microplates), resulting in a bottleneck that impedes the materials-design process. To overcome this challenge, we propose a set of automated material property characterization (autocharacterization) tools that leverage the adaptive, parallelizable, and scalable nature of computer vision to accelerate the throughput of characterization by 85x compared to the non-automated workflow. We demonstrate a generalizable composition mapping tool for high-throughput synthesized binary material systems as well as two scalable autocharacterization algorithms that (1) autonomously compute the band gap of 200 unique compositions in 6 minutes and (2) autonomously compute the degree of degradation in 200 unique compositions in 20 minutes, generating ultra-high compositional resolution trends of band gap and stability. We demonstrate that the developed band gap and degradation detection autocharacterization methods achieve 98.5% accuracy and 96.9% accuracy, respectively, on the FA$_{1-x}$MA$_{x}$PbI$_3$, $0\leq x \leq 1$ perovskite semiconductor system.

Figures (21)

• Figure 1: a-c, Overview of the synthesis and characterization of perovskite semiconductors. a, High-throughput combinatorial synthesis of FA$_{1-x}$MA$_{x}$PbI$_3$ perovskites attain throughputs of $10^4$ samples/hr. b, Manual characterization of the high-throughput-manufactured materials using UV-Vis spectroscopy and manual determination of band gap and degradation bottlenecks the pipeline down to a throughput of $10^1$ samples/hr. c, Autocharacterization, developed in this paper, of the high-throughput-manufactured material's band gaps and degradation attain throughputs of $10^3$ samples/hr using scalable and parallelizable computer vision measurement. Band gap is determined by automatically segmenting and fitting the material reflectance spectra while the degradation pathway is detected by the material yellowing, in the case depicted above due to a phase change from $\alpha$-FAPbI$_3$ to $\delta$-FAPbI$_3$. The widths of the gray backgrounds visualize the process throughputs.
• Figure 2: a, Raw hyperspectral datacube, $\Omega$, captured using a hyperspectral imager (Resonon, Pika L) of HT-deposited FA$_{1-x}$MA$_{x}$PbI$_3$ perovskites. $(X, Y)$ represents the pixel coordinates, and $R(\lambda)$ represents the reflectance spectra for each pixel. Each sample is deposited onto the glass substrate with a unique composition $0\leq x \leq 1$ and flexible form factor geometry. b, Computer-vision segmented datacube, $\Phi$, that pairs each unique sample's pixels, $(\widehat{X},\widehat{Y})_n \in N$, to its reflectance spectra, $R(\lambda)$. The gray hatched region indicates the discarded background pixels.
• Figure 3: a, High-throughput combinatorial synthesis of one batch of perovskite samples illustrated with its corresponding computer vision-segmented composition map. The print head rasters in a serpentine pattern (black connecting lines) to print a gradient of FA$_{1-x}$MA$_x$PbI$_3$ deposits onto a glass substrate, where the purple labels indicate FA-rich deposits, and the yellow labels indicate MA-rich deposits. Integrating the pump speeds over time determines the proportion of MA, $x$, in the composition. b, XRD peak traces at the (012) crystallographic plane measured at uniformly spaced compositions in the batch print. The peak shifts towards a higher $2\theta$ angle gradually as the proportion of MA increases in the composition. c, XPS traces of the C=N bond peak (red area under the curve) and C-N bond peak (gray area under the curve) measured at uniformly spaced compositions in the batch print. The C=N peak intensity decreases as the proportion of MA increases. Purple-labeled traces are FA-rich while yellow-labeled traces are MA-rich.
• Figure 4: a-c, Automatic band gap computation shown for two unique, computer vision-segmented perovskite deposits. b, The reflectance intensities, $R(\lambda)$, are acquired for each sample from a, the vision-segmented hypercube, $\Phi$. c, The Tauc curves are computed from the median reflectance spectra for each deposit, recursively segmented into line segments, and then iteratively fit with linear regression lines. The best-fit regression line that minimizes the RMSE between the detected Tauc peaks is illustrated by the thick red line, which determines the band gap, $E_g$, from the x-intercept.
• Figure 5: Performance of the autocharacterization of band gap relative to the domain expert-compute band gap for $N=201$ unique perovskite samples across 3 independent trials. The solid black line is the regression fit to the band gap data and the dashed black line is the $y=x$ line. Histogram distributions of both autocharacterization and domain expert band gaps are shown on the right and top of the plot area, respectively. The color of the scatter points corresponds to the proportion of MA, $x$, in the composition FA$_{1-x}$MA$_x$PbI$_3$.