4.6 Article

A Van Der Waals Photo-Ferroelectric Synapse

Journal

ADVANCED ELECTRONIC MATERIALS
Volume 8, Issue 10, Pages -

Publisher

WILEY
DOI: 10.1002/aelm.202200326

Keywords

halide perovskite; neuromorphic computing; photocurrents; photo-ferroelectric; van der Waals

Funding

  1. National Natural Science Foundation of China [61974107]
  2. National Science Foundation [1916652]
  3. China Scholarship Council
  4. Directorate For Engineering
  5. Div Of Electrical, Commun & Cyber Sys [1916652] Funding Source: National Science Foundation

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This study reports the design and experimental demonstration of a van der Waals (vdW) photo-ferroelectric synapse, which extracts synaptic memory by reading photocurrent and can be written or edited by electrical pulses. The results show that the photo-ferroelectric synapse has good performance and can be used for classic training and inference.
For hardware artificial intelligence, the central task is to design and develop artificial synapses with needed characteristics. Here, the design and experimental demonstration of a van der Waals (vdW) photo-ferroelectric synapse are reported. In the photo-ferroelectric synapse, the synaptic memory is extracted by reading the photocurrent, and written or edited by electrical pulses. The semiconducting vdW organic-inorganic halide perovskite ((R)-(-)-1-cyclohexylethylammonium)PbI3 (R-CYHEAPbI(3)) photo-ferroelectric serves as the model photo-ferroelectric channel. Here, the vdW organic layer provides ferroelectric dipole and the PbI6 octahedron is responsible for photon absorption and charge transport. The R-CYHEAPbI(3) photo-ferroelectric synapse show a writing/reading dynamics with >200 synaptic states, close to 10(3) on/off ratio, and reasonable endurance and retention characteristics. With the experimentally measured weight dynamics (parallel reading through ferroelectric photovoltaic effect and writing by electrical pulses) of R-CYHEAPbI(3) synapses, the feasibility of using a crossbar circuit to implement classic training and inference of hand-written digits is presented. An image recognition accuracy of up to 90% is obtained. The demonstration of such a vdW photo-ferroelectric synapse opens a window in the design of advanced devices for artificial intelligence.

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