Color‐Recognizing Si‐Based Photonic Synapse for Artificial Visual System

Abstract

Recently, photonic synapses that can directly respond to light signals have attracted much attention due to their huge application potential in neuromorphic chips and artificial vision systems. However, the implementation of an artificial visual system based on a synapse remains a considerable challenge due to the limitation that most current photonic synapses fail to recognize color. Here, a photonic synapse with color recognition capability is proposed and demonstrated. Through the light‐induced adjustment of dangling bond defects inside the amorphous silicon (a‐Si) film, the device exhibits switchable volatile and nonvolatile photoconductivity (PC) behaviors at the wavelengths of 635 and 450 nm, respectively. Based on the dual PC, the photonic synapse enables dual synaptic plasticity depending on the stimulation light, allowing not only various synaptic functions, but also learning experiences, associative learning behaviors, and recognition of the red and blue colors. Moreover, the dual synaptic plasticity of the photonic synapse could be modulated via a voltage in the range below ≈1 V to realize the stable detection and precise extraction of the grayscale and color signals. With its simple structure and compatibility with existing Si‐complementary metal oxide semiconductor (CMOS) technology, this photonic synapse shows potential for application in neuromorphic computing and advanced robot vision systems.

High‐Performance Memristor Based on 2D Layered BiOI Nanosheet for Low‐Power Artificial Optoelectronic Synapses

“…This behavior is analogous to the LTP observed in the electrical synapse, suggesting a photonic synapse based on the fabricated memristor. [51] Moreover, when the light duration and interval are both changed to 30 s, a similar current trend can be observed (Figure 6d). Besides, for the photonic synapse the current decays when the light pulse is turned off, which is the short-term plasticity.…”

Retina‐Inspired Color‐Cognitive Learning via Chromatically Controllable Mixed Quantum Dot Synaptic Transistor Arrays

“…The extracted τ values were 18.2, 65.2, and 27.1 s under R-, G-, and B-light stimulation, respectively, whereas extracted β values were 0.48, 0.67, and 0.51, which were compared with those of previously reported photonic synapses in Table S1, Supporting Information. [13,30,32,51,54] The long-term PSC retention properties were also measured to validate the empirical performance (Figure S13, Supporting Information). Although the device showed rather rapid attenuation in the PSC immediately after stimulated by each RGB-light (at initial state), the PSC decayed slowly thereafter, demonstrating good retention characteristics for around 8 ks.…”

Mimicking Neuroplasticity via Ion Migration in van der Waals Layered Copper Indium Thiophosphate

“…Conventional digital computers, based on the von Neumann architecture, are facing great challenges in massive data processing in the big data era. [1,2] The separated logic and memory units usually give rise to long latency and high energy consumption while handling complex computing issues, such as pattern recognition, [3] autonomous vehicles, and natural language processing. [4] Brain-inspired neuromorphic computing, which has highly parallel computing and adaptive learning capabilities, holds great promise for overcoming this bottleneck of von Neumann architecture and achieving high data processing efficiency.…”