Highly accurate, efficient, and fabrication tolerance-aware nanostructure prediction for high-performance optoelectronic devices.

Abstract

Despite extensive efforts to predict optimal nanostructures for enhancing optical devices, a more accurate, efficient, and practical method for nanostructure optimisation is required. In particular, fabrication tolerance is a promising avenue for significantly improving manufacturing efficiency; however, research in this area is limited. In this study, we introduce a practical approach for enhancing the performance of optoelectronic devices using an artificial intelligence (AI)-based nanostructure optimisation strategy. We optimised a support vector regression (SVR) model to capture the complex and nonlinear relationships between the transmittance and nanograting structure variables with the goal of improving optoelectronic devices. Our versatile model accurately predicted the continuous transmittance data with high precision (R² = 0.995) using only 216 training data points. It can also make predictions under untrained conditions, thereby enabling the creation of a transmittance nanostructure contour map (R² = 0.949). This method facilitates the design of nanostructures tailored to specific optical properties and provides valuable insights into fabrication tolerance. Through experimental validation, we identified an optimal nanograting structure with the highest transmittance in the visible-light spectrum. When integrated into optoelectronic devices such as organic light-emitting diodes (OLEDs) and organic solar cells (OSCs), their performance is significantly improved by increasing the light transmittance. Specifically, devices using the fabricated nanograting film exhibited a 17% improvement in external quantum efficiency (EQE) for solution-processed organic light-emitting diodes (SP-OLEDs) and a 10.7% improvement in power-conversion efficiency (PCE) for OSCs.