Bio-inspired “Self-denoising” capability of 2D materials incorporated optoelectronic synaptic array

In in-sensor image preprocessing, the sensed image undergoes low level processing like denoising at the sensor end, similar to the retina of human eye. Optoelectronic synapse devices are potential contenders for this purpose, and subsequent applications in artificial neural networks (ANNs). The optoelectronic synapses can offer image pre-processing functionalities at the pixel itself—termed as in-pixel computing. Denoising is an important problem in image preprocessing and several approaches have been used to denoise the input images. While most of those approaches require external circuitry, others are efficient only when the noisy pixels have significantly lower intensity compared to the actual pattern pixels. In this work, we present the innate ability of an optoelectronic synapse array to perform denoising at the pixel itself once it is trained to memorize an image. The synapses consist of phototransistors with bilayer MoS2 channel and p-Si/PtTe2 buried gate electrode. Our 7 × 7 array shows excellent robustness to noise due to the interplay between long-term potentiation and short-term potentiation. This bio-inspired strategy enables denoising of noise with higher intensity than the memorized pattern, without the use of any external circuitry. Specifically, due to the ability of these synapses to respond distinctively to wavelengths from 300 nm in ultraviolet to 2 µm in infrared, the pixel array also denoises mixed-color interferences. The “self-denoising” capability of such an artificial visual array has the capacity to eliminate the need for raw data transmission and thus, reduce subsequent image processing steps for supervised learning.

Duke Scholars

Author Tania Roy Electrical and Computer Engineering