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  1. Abstract

    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 MoS2channel and p-Si/PtTe2buried 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.

     
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  2. Abstract

    Memristors for neuromorphic computing have gained prominence over the years for implementing synapses and neurons due to their nano-scale footprint and reduced complexity. Several demonstrations show two-dimensional (2D) materials as a promising platform for the realization of transparent, flexible, ultra-thin memristive synapses. However, unsupervised learning in a spiking neural network (SNN) facilitated by linearity and symmetry in synaptic weight update has not been explored thoroughly using the 2D materials platform. Here, we demonstrate that graphene/MoS2/SiOx/Ni synapses exhibit ideal linearity and symmetry when subjected to identical input pulses, which is essential for their role in online training of neural networks. The linearity in weight update holds for a range of pulse width, amplitude and number of applied pulses. Our work illustrates that the mechanism of switching in MoS2-based synapses is through conductive filaments governed by Poole-Frenkel emission. We demonstrate that the graphene/MoS2/SiOx/Ni synapses, when integrated with a MoS2-based leaky integrate-and-fire neuron, can control the spiking of the neuron efficiently. This work establishes 2D MoS2as a viable platform for all-memristive SNNs.

     
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    Free, publicly-accessible full text available December 1, 2024
  3. Free, publicly-accessible full text available February 14, 2025
  4. Abstract Crystallographically anisotropic two-dimensional (2D) molybdenum disulfide (MoS 2 ) with vertically aligned (VA) layers is attractive for electrochemical sensing owing to its surface-enriched dangling bonds coupled with extremely large mechanical deformability. In this study, we explored VA-2D MoS 2 layers integrated on cellulose nanofibers (CNFs) for detecting various volatile organic compound gases. Sensor devices employing VA-2D MoS 2 /CNFs exhibited excellent sensitivities for the tested gases of ethanol, methanol, ammonia, and acetone; e.g. a high response rate up to 83.39% for 100 ppm ethanol, significantly outperforming previously reported sensors employing horizontally aligned 2D MoS 2 layers. Furthermore, VA-2D MoS 2 /CNFs were identified to be completely dissolvable in buffer solutions such as phosphate-buffered saline solution and baking soda buffer solution without releasing toxic chemicals. This unusual combination of high sensitivity and excellent biodegradability inherent to VA-2D MoS 2 /CNFs offers unprecedented opportunities for exploring mechanically reconfigurable sensor technologies with bio-compatible transient characteristics. 
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    Two-dimensional (2D) molybdenum disulfide (MoS 2 ) layers are suitable for visible-to-near infrared photodetection owing to their tunable optical bandgaps. Also, their superior mechanical deformability enabled by an extremely small thickness and van der Waals (vdW) assembly allows them to be structured into unconventional physical forms, unattainable with any other materials. Herein, we demonstrate a new type of 2D MoS 2 layer-based rollable photodetector that can be mechanically reconfigured while maintaining excellent geometry-invariant photo-responsiveness. Large-area (>a few cm 2 ) 2D MoS 2 layers grown by chemical vapor deposition (CVD) were integrated on transparent and flexible substrates composed of 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO)-oxidized cellulose nanofibers (TOCNs) by a direct solution casting method. These composite materials in three-dimensionally rollable forms exhibited a large set of intriguing photo-responsiveness, well preserving intrinsic opto-electrical characteristics of the integrated 2D MoS 2 layers; i.e. , light intensity-dependent photocurrents insensitive to illumination angles as well as highly tunable photocurrents varying with the rolling number of 2D MoS 2 layers, which were impossible to achieve with conventional photodetectors. This study provides a new design principle for converting 2D materials to three-dimensional (3D) objects of tailored functionalities and structures, significantly broadening their potential and versatility in futuristic devices. 
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