The Characteristics of Binary Spike-Time-Dependent Plasticity in HfO2-Based RRAM and Applications for Pattern Recognition

Abstract

A binary spike-time-dependent plasticity (STDP) protocol based on one resistive-switching random access memory (RRAM) device was proposed and experimentally demonstrated in the fabricated RRAM array. Based on the STDP protocol, a novel unsupervised online pattern recognition system including RRAM synapses and CMOS neurons is developed. Our simulations show that the system can efficiently compete the handwritten digits recognition task, which indicates the feasibility of using the RRAM-based binary STDP protocol in neuromorphic computing systems to obtain good performance.

Graphene Oxide Quantum Dots Based Memristors with Progressive Conduction Tuning for Artificial Synaptic Learning

“…Moreover, these devices emulate biomimetic synaptic functions, such as spike‐timing‐dependent plasticity (STDP), paired‐pulse facilitation (PPF), and so on, and STDP is a characteristic of a more biofaithful devices for spiking neural network . According to the interactions between presynaptic and postsynaptic neurons, synaptic effects can also be strengthened (long‐term potentiation) or weakened (long‐term depression) . Up to date, many memristors just reported the single side‐only modulation .…”

Multi-Level Switching of Al-Doped HfO2 RRAM with a Single Voltage Amplitude Set Pulse

“…However, they can only carry out a four-level storage state [4,25]. According to Zhou et al [26], TiN/Al 2 O 3 /HfO 2 /Pt bilayer RRAM devices show seven analog reset states for both current scanning and voltage scanning operations with an on/off ratio of around 10. Woo et al [27] explained that RRAM devices with an AlO x /HfO x bi-layer structure present 12 distinct analog multi-levels with on/off ratio over 10.…”

Analytical Modeling of Organic–Inorganic CH3NH3PbI3 Perovskite Resistive Switching and its Application for Neuromorphic Recognition

“…Nowadays, neuromorphic computation, which aims at outperforming performances of Von Neumann architectures, is one of the most investigated topics in artificial intelligence and neural network research. The hardware platform including RS devices has been regarded as a promising candidate to build neuromorphic systems with low energy consumption and high density . Several structures (e.g., crossbar arrays, artificial synapse schemes) based on resistive‐RAM (RRAM) devices have been designed and used for pattern learning, face classification, sparse coding, and other neuromorphic cognitive primitives .…”

“…The hardware platform including RS devices has been regarded as a promising candidate to build neuromorphic systems with low energy consumption and high density . Several structures (e.g., crossbar arrays, artificial synapse schemes) based on resistive‐RAM (RRAM) devices have been designed and used for pattern learning, face classification, sparse coding, and other neuromorphic cognitive primitives . Furthermore, the development of an analytical model capable of capturing RS is crucial to build a theoretical basis allowing to implement neuromorphic computing with RRAM devices.…”