Physics-Aware Inverse Design for Nanowire Single-Photon Avalanche Detectors via Deep Learning

TL;DR

This paper introduces a physics-aware deep learning approach for inverse design of nanowire-based SPADs, enabling direct inference of device structures from desired performance metrics, thus streamlining the design process.

Contribution

The authors develop a novel deep learning-based inverse design workflow that incorporates physical principles, applicable to various photonic devices including SPADs, photodetectors, and solar cells.

Findings

Successfully infers nanowire parameters for target photon detection efficiency

Demonstrates applicability to different SPAD structures and devices

Reduces design time compared to traditional iterative methods

Abstract

Single-photon avalanche detectors (SPADs) have enabled various applications in emerging photonic quantum information technologies in recent years. However, despite many efforts to improve SPAD's performance, the design of SPADs remained largely an iterative and time-consuming process where a designer makes educated guesses of a device structure based on empirical reasoning and solves the semiconductor drift-diffusion model for it. In contrast, the inverse problem, i.e., directly inferring a structure needed to achieve desired performance, which is of ultimate interest to designers, remains an unsolved problem. We propose a novel physics-aware inverse design workflow for SPADs using a deep learning model and demonstrate it with an example of finding the key parameters of semiconductor nanowires constituting the unit cell of an SPAD, given target photon detection efficiency. Our inverse design workflow is not restricted to the case demonstrated and can be applied to design conventional planar structure-based SPADs, photodetectors, and solar cells.