Abstract
We propose an object recognition architecture relying on a neural network algorithm in optical sensors. Precisely, by applying the high-speed and low-power Fourier transform operation in the optical domain, we can transfer the high-cost part of the traditional convolutional neural network algorithm to the sensor side to achieve faster computing speed. An optical neuron unit (ONU) consisting of transition metal sulfide (TMD) material is fabricated for a vivid validation of this architecture. Using the embedded gate pair structure inside our ONU, TMD materials can be electrically doped at different levels, forming an in-plane PN junction, which allows for effective manipulation of light response to imitate biological nerve synapses. The results demonstrate that our ONU could reach the ability of optic neurons, providing experimental support for future in-sensor computing architecture.
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