Integrated diffractive optical neural network with space-time interleaving

Abstract

Integrated diffractive optical neural networks (DONNs) have significant potential for complex machine learning tasks with high speed and ultralow energy consumption. However, the on-chip implementation of a high-performance optical neural network is limited by input dimensions. In contrast to existing photonic neural networks, a space-time interleaving technology based on arrayed waveguides is designed to realize an on-chip DONN with high-speed, high-dimensional, and all-optical input signal modulation. To demonstrate the performance of the on-chip DONN with high-speed space-time interleaving modulation, an on-chip DONN with a designed footprint of 0.0945 mm 2 is proposed to resolve the vowel recognition task, reaching a computation speed of about 1.4 × 10 13 operations per second and yielding an accuracy of 98.3% in numerical calculation. In addition, the function of the specially designed arrayed waveguides for realizing parallel signal inputs using space-time conversion has been verified experimentally. This method can realize the on-chip DONN with higher input dimension and lower energy consumption.

Multimode diffractive optical neural network

“…11,[22][23][24] In our preceding studies, the length of the silicon (Si) etching slots in the DONNs is optimally designed to modulate the phase of the optical field carrying information, allowing classification, regression, and convolution computations to be actualized. 11,[25][26][27][28] Notwithstanding these advancements, certain challenges have emerged. Specifically, to ensure stable interference in the DONN, a relatively large spacing between metalines and open boundaries is required, leading to severe light leakage and a substantial footprint.…”