Artificial Multisensory Neuron with a Single Transistor for Multimodal Perception through Hybrid Visual and Thermal Sensing

Abstract

An artificial multisensory device applicable to insensor computing is demonstrated with a single-transistor neuron (1T-neuron) for multimodal perception. It simultaneously receives two sensing signals from visual and thermal stimuli. The 1T-neuron transforms these signals into electrical signals in the form of spiking and then fires them for a spiking neural network at the same time. This feature makes it feasible to realize input neurons for multimodal sensing. Visual and thermal sensing is achieved due to the inherent optical and thermal behaviors of the 1T-neuron. To demonstrate a neuromorphic multimodal sensing system with the artificial multisensory 1T-neuron, fingerprint recognition, widely used for biometric security, is implemented. Owing to the simultaneous sensing of heat as well as light, the proposed fingerprint recognition system composed of multisensory 1T-neurons not only identifies a genuine pattern but also judges whether or not it is forged.

Recent Research for HZO-Based Ferroelectric Memory towards In-Memory Computing Applications

“…[60][61][62][63][64][65]. The basic behavior of computing is based on the matrix product of the voltage generated in artificial spiking neurons and the variable resistance called synapses [66]. Conventional CMOS can also construct neurons and synapses, but it requires a great deal of unit devices, resulting in low energy efficiency and difficulty in device integration [67].…”

PBDB-T/Pentacene-Based Organic Optoelectronic Synaptic Transistor with Adjustable Critical Flicker Fusion Frequency for Dynamic Vision

“…In the era of big data, processing and storage of massive visual information pose heightened demands on real-time image processing and storage devices. − The human visual system possesses the capability for rapid, efficient, and energy-efficient visual information processing in complex environments. , Inspired by this, neuromorphic vision systems integrate photosensors, information processing units, and data storage units to achieve complex image processing. − Within this context, light-gated organic synaptic devices (organic optoelectronic synapses) have been developed to enhance the light-sensing capability to traditional electronic synapses. , This additional capability opens up opportunities for analog image preprocessing and recognition. ,− Moreover, it is imperative to recognize that beyond the realm of static image recognition and processing, the identification and detection of moving objects have become equally significant areas of research. Several studies have proposed novel optoelectronic transistor arrays to implement dynamic visual systems. , The challenge in device recognition of moving objects lies in determining object velocity or the photosensitivity frequency of the device itself.…”

Reconfigurable Physical Reservoir Enabled by Polarization of Ferroelectric Polymer P(VDF–TrFE) and Interface Charge‐Trapping/Detrapping in Dual‐Gate IGZO Transistor

“…Unlike traditional RNNs, the recursive network in the reservoir layer is fixed and its weights are unchanged during the training process and can be approximated by dynamic hardware behaviors, enabling the implementation of RC in specific physical domains. [13][14][15][16][17][18] The framework of the RC system can be roughly divided into two parts: the reservoir and readout layers. The reservoir layer, with fading memory and nonlinear dynamics properties, is used to map input information into a high-dimensional feature space.…”

“…Unlike traditional RNNs, the recursive network in the reservoir layer is fixed and its weights are unchanged during the training process and can be approximated by dynamic hardware behaviors, enabling the implementation of RC in specific physical domains. [ 13–18 ]…”