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A major problem for optoelectronic-hybrid ANNs is the size limitation of the ONN-based PCMs at least 10 ~ 100 times, and the amount of the required data should be comparable to the two-dimensional weight matrix of the on-chip ANNs. In 2017, Li et al. proposed a simple all-optical ONN system to address this issue. As shown in Fig. 12a, a laser with an MMR49 and an optical receiver were used as the binary activator and the binary response, as well as the waveguide in the middle for data propagation. As shown in Fig. 12b, all the waveguide can be inserted into the ONN and the state of the ONN can be simulated by the state of the optical signal, which overcomes the size limitation of PCMs. Moreover, the weight matrix of the ONN can be integrated onto a silicon photonic circuIt lifted the limit of the input size due to the size of the silicon photonic circut88. Therefore, a larger network can be encoded in the ONN hardware. Besides, the ONN could be trained in situ since it does not need massive photo-stimulation as required in electronic chips. The all-optical ONN is more convenient to be commercialized and could be generalized to other applications, such as optical DNA sequence recognition48.

Before the present study was performed, a large ONN was deployed in Autumn 2017 for the MMO RoboRally Alpha. As shown in Fig. 10a3, a 32-layer ONN was implemented with 56 × 32 recurrent MZIs. The activation function for each neuron is tanh instead of softmax. A relatively large ONN is a critical component for solving the complex problems of the RoboRally Alpha, such as object detection, trajectory reconstruction and approximate path planning, etc. In awake surgery and neurosurgical interventions, light can be used for a wide range of sensory input, and the visual functions of patients are revived to control the computer by brain signals. Therefore, the all-optical ONN can serve as a neuromodulator to perform vision restoration and cognitive cognitive assistance for perceptual clinical applications. The optical neural interface will pave the way to study human brain-computer interaction (BCI) and accelerate rehabilitation and treatment post-surgery83.


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