Nanophotonic cavity based synapse for scalable photonic neural networks

TL;DR

This paper demonstrates nanophotonic crystal nanobeam synapses that are free from free-spectral range limitations, significantly increasing channel capacity and efficiency for scalable photonic neural networks.

Contribution

It introduces FSR-free nanobeam cavities as synapses, overcoming MRR channel limitations and enabling high-dimensional, high-throughput photonic neural networks.

Findings

Nanobeam cavities are FSR-free within C-band.

Enhanced data throughput and high-dimensional input support.

Higher tuning energy efficiency and compute density.

Abstract

The bandwidth and energy demands of neural networks has spurred tremendous interest in developing novel neuromorphic hardware, including photonic integrated circuits. Although an optical waveguide can accommodate hundreds of channels with THz bandwidth, the channel count of photonic systems is always bottlenecked by the devices within. In WDM-based photonic neural networks, the synapses, i.e. network interconnections, are typically realized by microring resonators (MRRs), where the WDM channel count (N) is bounded by the free-spectral range of the MRRs. For typical Si MRRs, we estimate N <= 30 within the C-band. This not only restrains the aggregate throughput of the neural network but also makes applications with high input dimensions unfeasible. We experimentally demonstrate that photonic crystal nanobeam based synapses can be FSR-free within C-band, eliminating the bound on channel count. This increases data throughput as well as enables applications with high-dimensional inputs like natural language processing and high resolution image processing. In addition, the smaller physical footprint of photonic crystal nanobeam cavities offers higher tuning energy efficiency and a higher compute density than MRRs. Nanophotonic cavity based synapse thus offers a path towards realizing highly scalable photonic neural networks.