An Interface‐Type Memristive Device for Artificial Synapse and Neuromorphic Computing

Abstract

Interface‐type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament‐type (FT) devices for neuromorphic computing because of their uniform, filament‐free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface‐dominated memristive device is demonstrated using a simple Au/Nb‐doped SrTiO3 (Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface‐controlled RS, the proposed device shows low device‐to‐device, cell‐to‐cell, and cycle‐to‐cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next‐generation neuromorphic computing.

Plasma-Enhanced Atomic Layer Deposition of Titanium Oxynitride (TiOxNy) Thin Films and Their Neuromorphic Applications

“…It should be noted that the structure of the ANN is highly dependent on the long-term potentiation (LTP) and the change in the conductance of the device, which influences the synaptic weight change during the training process. This change is governed by the following equations G normalL normalT normalP = B ( 1 − normale − P / A normalP ) + G min G normalL normalT normalD = prefix− B ( 1 − normale − P − P max / A normalD ) + G max B = G max − G min false( 1 − e − P max / A normalP , normalD false) where G LTP and G LTD are the conductance for LTP and long-term depression, respectively.…”

“…It should be noted that the structure of the ANN is highly dependent on the long-term potentiation (LTP) and the change in the conductance of the device, which influences the synaptic weight change during the training process. This change is governed by the following equations where G LTP and G LTD are the conductance for LTP and long-term depression, respectively.…”

“…Instead, the data set was truncated to manage the computational time and achieve a favorable trade-off between the amount of data used and recognition accuracy. It should be noted that the structure of the ANN is highly dependent on the long-term potentiation (LTP) 52…”

Research Progress on the Application of Topological Phase Transition Materials in the Field of Memristor and Neuromorphic Computing

“…For example, Ryu et al [59] reported a self-rectifying Ti/TiO 2 /HfO 2 /Si interface-type memristor, which exhibits good endurance characteristics (>1000 cycles) and retention characteristics (>10 4 s), as well as more resistance states (~49) to simulate synaptic weights, and because the self-rectifying behavior enhances the interface switching performance, it is better to apply the simulation of artificial neural networks. In addition, Kunwar et al [60] found that the Au/Nb: STO interface-type device showed good simulated resistive switching characteristics, a large on/off ratio (10 5 ), and good retention (10 4 s) and endurance (10 2 cycles) characteristics. The device can successfully simulate various biological synaptic functions such as excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/LTD), and spike timing dependent plasticity (STDP), which proves that the interface-type memristor device can be used to develop highly reliable synaptic devices for neuromorphic computing.…”

Linear and symmetric synaptic weight update characteristics by controlling filament geometry in oxide/suboxide HfOx bilayer memristive device for neuromorphic computing

“…Hansen et al has reported area-dependent switching in oxide heterojunction memristors using Al 2 O 3 /Nb x O y double-barrier layer with uniform current distribution for high and low resistance states; yet the device retention time significantly affected, which is critical to guarantee pattern classification accuracy 33 . Kunwar et al demonstrated versatile synaptic functions with an excellent uniformity through interface-controlled Au/Nb-doped SrTiO 3 Schottky structure with reliable retention 34 . A two-terminal charge trapped memristor based on Pt/Ta 2 O 5 /Nb 2 O 5-x /Al 2 O 3-y /Ti device has been reported exhibiting highly self-rectifying and nonlinear characteristics with a long retention time achieving a good pattern recognition challenge 35 .…”