Affiliation:
1. Division of Electronics and Electrical Engineering, Dongguk University 1 , Seoul 04620, Republic of Korea
2. Department of Computer Science and Engineering, Incheon National University 2 , Incheon 22012, South Korea
Abstract
Efficient data processing is heavily reliant on prioritizing specific stimuli and categorizing incoming information. Within human biological systems, dorsal root ganglions (particularly nociceptors situated in the skin) perform a pivotal role in detecting external stimuli. These neurons send warnings to our brain, priming it to anticipate potential harm and prevent injury. In this study, we explore the potential of using a ferroelectric memristor device structured as a metal–ferroelectric–insulator–semiconductor as an artificial nociceptor. The aim of this device is to electrically receive external damage and interpret signals of danger. The TiN/HfAlOx (HAO)/HfSiOx (HSO)/n+ Si configuration of this device replicates the key functions of a biological nociceptor. The emulation includes crucial aspects, such as threshold reactivity, relaxation, no adaptation, and sensitization phenomena known as “allodynia” and “hyperalgesia.” Moreover, we propose establishing a connection between nociceptors and synapses by training the Hebbian learning rule. This involves exposing the device to injurious stimuli and using this experience to enhance its responsiveness, replicating synaptic plasticity.
Funder
National Research Foundation of Korea
Reference85 articles.
1. Top electrode engineering for freedom in design and implementation of ferroelectric tunnel junctions based on HfxZrxO2;ACS Appl. Electron. Mater.,2022
2. Resistive random access memory (RRAM): An overview of materials, switching mechanism, performance, multilevel cell (MLC) storage, modeling, and applications;Nanoscale Res. Lett.,2020
3. Energy-efficient monolithic three-dimensional on-chip memory architectures;IEEE Trans. Nanotechnol.,2018
4. Energy-efficient artificial synapses based on oxide tunnel junctions;ACS Appl. Mater. Interfaces,2019
5. Synaptic electronics: Materials, devices and applications;Nanotechnology,2013