Abstract
We design, fabricate, and characterize integrated photonic routing manifolds with 10 inputs and 100 outputs using two vertically integrated planes of silicon nitride waveguides. We analyze manifolds via top-view camera imaging. This measurement technique allows the rapid acquisition of hundreds of precise transmission measurements. We demonstrate manifolds with uniform and Gaussian power distribution patterns with mean power output errors (averaged over 10 sets of 10 inputs) of 0.7 and 0.9 dB, respectively, establishing this as a viable architecture for precision light distribution on-chip. We also assess the performance of the passive photonic elements comprising the system via self-referenced test structures, including high-dynamic-range beam taps, waveguide cutback structures, and waveguide crossing arrays.
Highlights
Instead of collecting the light with fibers, we focus it onto a 640 × 512 pixel, 12-bit-depth indium gallium arsenide image sensor array through a microscope objective
One example is synapse S2,7 in the Gaussian manifold case [Fig. 6(b)]. These likely originate from mechanical damage to a small number of the output gratings which occurred during the planarization step
We propose, fabricate, and characterize an integrated photonic routing manifold capable of distributing light with high precision across a 10 × 100 network
Summary
The development of highly compact and energy-efficient optical interconnects[1] has been a major research objective for integrated photonics. Using light for communication in neural systems is very promising,[11,12,13,14,15,16] but constructing a network of nodes each with thousands of connections presents a formidable routing challenge. We present the design and implementation of a two-plane signal distribution network routing 10 input nodes in one network layer to 100 connections on 10 output nodes. This routing manifold accomplishes the routing between two layers of a feed-forward neural network with 10 neurons per layer and all-to-all connectivity. We recently reported a theoretical analysis of the performance and scaling of multi-planar routing strategies for neural computing.[23]
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