Work

Single Wavelength Operating Neuromorphic Device Based on a Graphene–Ferroelectric Transistor

Year: 2023

Type: article

Abstract: As global data generation continues to rise, there is an increasing demand for revolutionary in-memory computing methodologies and efficient machine learning solutions. Despite recent progress in electrical and electro-optical simulations of machine learning devices, the all-optical nonthermal function remains challenging, with single wavelength operation still elusive. Here we report on an optical and monochromatic way of neuromorphic signal processing for brain-inspired functions, eliminating the need for electrical pulses. Multilevel synaptic potentiation-depression cycles are successfully achieved optically by leveraging photovoltaic charge generation and polarization within the photoferroelectric substrate interfaced with the graphene sensor. Furthermore, the demonstrated low-power prototype device is able to reproduce exact signal profile of brain tissues yet with more than 2 orders of magnitude faster response. The reported properties should trigger all-optical and low power artificial neuromorphic development based on photoferroelectric structures.

Source: ACS Applied Materials & Interfaces

DOI: 10.1021/acsami.3c10010

Authors: Krishna Maity, Jean-François Dayen, Bernard Doudin, Roman Gumeniuk, Bohdan Kundys

Institutions: Centre National de la Recherche Scientifique, Institut de Physique et Chimie des Matériaux de Strasbourg, Université de Strasbourg, TU Bergakademie Freiberg

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Agence Nationale de la Recherche, Université de Strasbourg, Institut Universitaire de France

Award: Mixed Dimensional Heterostructures under 2D Ferroelectric control