Deep Learning-Assisted Design of All-Dielectric Micropillar Quantum Well Infrared Photodetectors

The integration of micro-nano optical structures has become an essential strategy for overcoming the performance bottlenecks of quantum well infrared photodetectors (QWIPs), specifically by addressing the inherent inability of planar devices to couple with normally incident light due to intersubband transition selection rules. A critical factor in this integration is the precise spectral overlap between an optical mode and the material’s excitation mode. Therefore, achieving precise spectral engineering is indispensable. However, conventional electromagnetic simulations act as forward solvers, calculating optical responses based on given geometric parameters. They cannot directly perform inverse design, which involves deriving optimal geometric parameters directly from a desired optical response. Consequently, structural optimization is severely constrained by time-consuming trial-and-error iterations, which often struggle to find the global optimum in a complex design space. To overcome these limitations, this paper presents a comprehensive theoretical and numerical study proposing a deep learning framework for QWIPs coupled with all-dielectric micropillar structures. By establishing a structure-absorption spectrum dataset via finite difference time domain (FDTD) simulations, we developed a dual-network setup. For the forward prediction, a multilayer perceptron (MLP) maps geometric parameters (side length a and period p) to the absorption spectrum, achieving a computational speedup of seven orders of magnitude over traditional numerical simulations. Concurrently, a convolutional neural network (CNN) is employed for the inverse design, realizing on-demand design of geometric parameters based on target spectra with high reconstruction accuracy. Furthermore, the selected all-dielectric micropillar structures are highly compatible with mainstream semiconductor fabrication processes. This research provides an efficient, automated toolkit for the development of high-performance infrared photodetectors.