In the past decade, the field of neuromorphic photonics has experienced significant growth. To extend the reach of this technology, researchers continue to push the limits of these systems with respect to network size and bandwidth. However, without proper RF-optimized architectural designs, as operating frequencies are scaled up, significant losses of RF power can be incurred at each neuron. Within the broadcast and weight neuromorphic photonic architecture, this excess loss will be accumulated until processing is no longer feasible. If designed properly, RF loss can be minimized significantly, and residual loss could be compensated by cointegrated transimpedance amplifiers, thus enabling further scaling of the network. In this paper, the authors present broadband weighting of RF input signals with a 3-dB bandwidth of 4.28 GHz, utilizing the linear front-end of a silicon photonic neural network. Additionally, the authors present link loss measurements and analysis.
Microwave photonics and neuromorphic photonics are two parallel research areas which have simultaneously emerged at the forefront of next generation processors. These fields, while initially independent, are naturally converging to a combined silicon photonic platform. An optical processing approach yields wide bandwidth, low latency, and dense interconnection. These photonic systems are capable of supporting applications previously unfeasible. Systems such as photonic cancellers, photonic blind source separation, photonic recurrent neural networks for RF fingerprinting, and photonic neural networks for nonlinear dispersion compensation. This paper will focus on the convergence of microwave photonics and neuromorphic photonics towards an RF optimized machine learning solution. Additionally, this paper investigated the RF noise performance of neuromorphic photonic front-end. The results indicated poor RF performances, leading to the proposal of a balanced linear front-end for noise figure reduction.
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