Nanophotonic Cavity Based Synapse for Scalable Photonic Neural Networks

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.

Automated summary

The demands for bandwidth and energy in neural networks have sparked interest in developing novel neuromorphic hardware, including photonic integrated circuits. However, the channel count of photonic systems is limited by the devices within, affecting the overall throughput and feasibility of high-dimensional input applications. Experimental demonstrations show that photonic crystal nanobeam based synapses can overcome this limitation and increase data throughput, enabling applications such as natural language processing and high-resolution image processing. Additionally, the smaller physical footprint and higher energy efficiency of these synapses offer a path towards realizing highly scalable photonic neural networks.