High-precision programming of large-scale ring resonator circuits with minimal pre-calibration

TL;DR

This paper presents a novel control method called chip-in-the-loop optimization (ChiL) that enables high-precision, scalable programming of large-scale microring resonator circuits with minimal calibration, demonstrated through photonic computing applications.

Contribution

Introduction of ChiL, a scalable, high-precision control method that reduces calibration complexity and improves accuracy in large-scale photonic integrated circuits.

Findings

Achieved over 9-bit precision in microring resonator control.

Demonstrated photonic solver for matrix eigenvalues with errors around 10^-4.

Implemented a photonic neural network matching digital accuracy.

Abstract

Microring resonators (MRRs) are essential components in large-scale photonic integrated circuits (PICs), but programming these circuits with high precision and efficiency remains an unsolved challenge. Conventional methods rely on complex calibration processes that are both time-consuming and often inaccurate, limiting the scalability of PICs. This work introduces an innovative control method called chip-in-the-loop optimization (ChiL) that addresses this challenge by offering high scalability, precision, fast convergence, and robustness. ChiL reduces the calibration complexity for an $N$ devices system from $O(k^N)$ to a single-shot measurement, while maintaining a record-high precision over 9-bit in the presence of system imperfections, including fabrication variances, thermal crosstalk, and temperature drift. Using ChiL, we experimentally demonstrate a photonic solver for computing matrix eigenvalues and eigenvectors with errors on the order of $10^{-4}$. Additionally, we achieve a photonic neural network (PNN) with accuracy and a confusion matrix identical to those of digital computers. ChiL offers a practical approach for programming large-scale PICs and bridges the gap between analog photonic and digital electronic computing and signal processing in both scale and precision.