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Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

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Gradient-probability-driven discrete search algorithm for on-chip photonics inverse design.

Shanglin Yang, Hao Jia, Lei Zhang

|October 7, 2021

Summary

This summary is machine-generated.

A new algorithm, gradient-probability-driven discrete search (GPDS), enables efficient inverse design for compact photonic devices. This discrete search method improves performance and stability over traditional approaches for on-chip photonics.

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Area of Science:

Photonics
Computational electromagnetics
Materials science

Background:

Gradient-based algorithms offer high convergence for photonic inverse design but struggle with discrete variables.
Pixel-based discrete search methods lack the efficiency of gradient approaches.
On-chip photonic devices require high performance and extreme compactness, necessitating advanced design tools.

Purpose of the Study:

To introduce a novel gradient-probability-driven discrete search (GPDS) algorithm for photonic inverse design.
To bridge the gap between gradient-based methods and discrete search for pixel-based photonic devices.
To enhance device performance, stability, and design flexibility in silicon photonics.

Main Methods:

Developed a GPDS algorithm connecting gradient information with discrete pixel values via probability sampling.
Implemented GPDS as an intrinsic discrete search, avoiding external discretization steps.
Compared GPDS performance against brute-force search (BFS) and traditional gradient methods.

Main Results:

GPDS demonstrated improved device performance and enhanced stability compared to BFS and traditional gradient methods.
The algorithm achieved high-performance silicon photonic structures in fewer iterations.
GPDS exhibited strong multi-objective optimization capabilities and flexible, manufacturing-friendly geometry control.

Conclusions:

The GPDS algorithm is an effective discrete search method for photonic inverse design.
GPDS offers significant advantages in performance, stability, and design efficiency for on-chip photonic applications.
GPDS shows potential as a powerful tool for solving complex multi-objective inverse design problems in photonics.