Literature DB >> 35044005

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

Chanho Jo1, Jaehyun Kim2, Jee Young Kwak1, Sung Min Kwon1, Joon Bee Park1, Jeehoon Kim3, Gyeong-Su Park4, Myung-Gil Kim3, Yong-Hoon Kim3, Sung Kyu Park1.   

Abstract

Artificial photonic synapses are emerging as a promising implementation to emulate the human visual cognitive system by consolidating a series of processes for sensing and memorizing visual information into one system. In particular, mimicking retinal functions such as multispectral color perception and controllable nonvolatility is important for realizing artificial visual systems. However, many studies to date have focused on monochromatic-light-based photonic synapses, and thus, the emulation of color discrimination capability remains an important challenge for visual intelligence. Here, an artificial multispectral color recognition system by employing heterojunction photosynaptic transistors consisting of ratio-controllable mixed quantum dot (M-QD) photoabsorbers and metal-oxide semiconducting channels is proposed. The biological photoreceptor inspires M-QD photoabsorbers with a precisely designed red (R), green (G), and blue (B)-QD ratio, enabling full-range visible color recognition with high photo-to-electric conversion efficiency. In addition, adjustable synaptic plasticity by modulating gate bias allows multiple nonvolatile-to-volatile memory conversion, leading to chromatic control in the artificial photonic synapse. To ensure the viability of the developed proof of concept, a 7 × 7 pixelated photonic synapse array capable of performing outstanding color image recognition based on adjustable wavelength-dependent volatility conversion is demonstrated.
© 2022 Wiley-VCH GmbH.

Entities:  

Keywords:  color recognition; heterojunction phototransistors; multiple nonvolatile detection; photonic synapses; quantum dots

Mesh:

Year:  2022        PMID: 35044005     DOI: 10.1002/adma.202108979

Source DB:  PubMed          Journal:  Adv Mater        ISSN: 0935-9648            Impact factor:   30.849


  1 in total

Review 1.  Progress of Materials and Devices for Neuromorphic Vision Sensors.

Authors:  Sung Woon Cho; Chanho Jo; Yong-Hoon Kim; Sung Kyu Park
Journal:  Nanomicro Lett       Date:  2022-10-15
  1 in total

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