Synaptic Plasticity and Learning Behaviors Mimicked in Single Inorganic Synapses of Pt/HfOx/ZnOx/TiN Memristive System

Abstract

In this work, a kind of new memristor with the simple structure of Pt/HfOx/ZnOx/TiN was fabricated completely via combination of thermal-atomic layer deposition (TALD) and plasma-enhanced ALD (PEALD). The synaptic plasticity and learning behaviors of Pt/HfOx/ZnOx/TiN memristive system have been investigated deeply. Multilevel resistance states are obtained by varying the programming voltage amplitudes during the pulse cycling. The device conductance can be continuously increased or decreased from cycle to cycle with better endurance characteristics up to about 3 × 103 cycles. Several essential synaptic functions are simultaneously achieved in such a single double-layer of HfOx/ZnOx device, including nonlinear transmission properties, such as long-term plasticity (LTP), short-term plasticity (STP), and spike-timing-dependent plasticity. The transformation from STP to LTP induced by repetitive pulse stimulation is confirmed in Pt/HfOx/ZnOx/TiN memristive device. Above all, simple structure of Pt/HfOx/ZnOx/TiN by ALD technique is a kind of promising memristor device for applications in artificial neural network.

Pseudo-Interface Switching of a Two-Terminal TaOx/HfO2 Synaptic Device for Neuromorphic Applications

“…In response to the demand for new computing architecture, a neuromorphic system has been proposed and self-learning neuromorphic chips have already been developed. Neurons and synapses are the key components necessary for learning and memory function in the human brain [8]. For computing, neurons integrate signals from adjacent neurons and generate output signals.…”

Recent progress on optoelectronic synaptic devices

“…尖峰时序可塑性 (spike-timing-dependent plasticity, STDP) 是人脑信息处理过程中的一种重要的 突触学习规则, 是 Hebbian 学习规则的补充和完善 [79,80] . 由于具有形式上的简洁和理论上的合理性, 成功模拟 STDP 已经成为检验脉冲神经网络 (SNNs) 硬件系统的重要标准之一 [81,82] .…”

Application of Probe Nanotechnologies for Memristor Structures Formation and Characterization

“…Another goal is to study titanium ONS thickness effect and applied voltage pulses on the memristor effect. For this, four ONSs were formed with lateral 2 × 2 μm dimensions and 1.6-3.6 nm height, which, based on the expression describing the oxide height and depth ratio presented in [3], corresponds to 3.6-8.2 nm thickness. The current-voltage characteristics were measured on these structures surface by applying ±2.4 voltage pulse (Figure 6).…”

“…This is referred as the von Neumann bottleneck and often limits the performance of the system [1]. One possible solution to this problem is the transition of computing systems to an architecture close to the structure of a biological brain, which is a set of elements of low power connected in parallel neurons interconnected via special channels synapses [1][2][3][4]. Processors built on this architecture have concurrent computing and will be able to surpass modern computers in tasks related to unstructured data classification, pattern recognition, as well as in applications with adaptable and self-learning control systems.…”

“…Processors built on this architecture have concurrent computing and will be able to surpass modern computers in tasks related to unstructured data classification, pattern recognition, as well as in applications with adaptable and self-learning control systems. One of the possible ways of implementing such an architecture is to manufacture ICs based on neuromorphic structures, which are memory elements in the form of cells (neurons) interconnected by data buses (synapses) and are capable of changing their electrical resistance under the influence of an external electric field (the effect of resistive switch) [1][2][3][4][5]. Such structures can maintain cell resistance after the termination of the external electric field and are the basis of non-volatile resistive memory (ReRAM).…”