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Growing Perovskite Quantum Dots on Carbon Nanotubes for Neuromorphic Optoelectronic Computing
Author(s) -
Li Jinxin,
Dwivedi Priyanka,
Kumar Kowsik Sambath,
Roy Tania,
Crawford Kaitlyn E.,
Thomas Jayan
Publication year - 2021
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.202000535
Subject(s) - neuromorphic engineering , materials science , photonics , optoelectronics , carbon nanotube , perovskite (structure) , quantum dot , memristor , nanotechnology , von neumann architecture , computer science , electronic engineering , artificial neural network , artificial intelligence , engineering , chemical engineering , operating system
Brain‐inspired (neuromorphic) computing that offers lower energy consumption and parallelism (simultaneous processing and memorizing) compared to von Neumann computing provides excellent opportunities in many computational tasks ranging from image recognition to speech processing. To accomplish neuromorphic computing, highly efficient optoelectronic synapses, which can be the building blocks of optoelectronic neuromorphic computers, are necessary. Currently, carbon nanotubes (CNTs), an attractive candidate to develop circuit‐level photonic synapses, provide weak light responses. The inferior photoresponse of CNTs increases the energy consumption of neuromorphic optoelectronic devices. Herein, a method to grow organic–inorganic halide perovskite quantum dots (PQDs) directly on multiwall CNTs (MWCNTs) to increase the photosensitivity of optoelectronic synapses is demonstrated. The new hybrid material synchronizes the high photoresponse of PQDs and the excellent electrical properties of MWCNTs to provide photonic memory under very low light intensity (125 µW cm −2 ). However, neat MWCNTs do not show any detectable light response at the tested light intensity, as high as 25 mW cm −2 . Since the PQDs are grown directly on and in the MWCNTs, the hybrid PQD‐MWCNT provides a new direction for the future MWCNT‐based optoelectronic devices for neuromorphic computing with a potential to break the von Neumann bottleneck.