Essential Characteristics of Memristors for Neuromorphic Computing

Abstract

increasingly problematic. Unlike the Von Neumann computing platform, the human brain relies on neurons and synapses for storage and computation, which do not have clear boundaries between them. Therefore, nanodevices that mimic synapses, for high-efficiency computing, have been investigated; among these nanodevices, memristors have attracted most attention because of their low power consumption, high integration density, and the ability to simulate synaptic plasticity, which meet the standards of neuromorphic computing. [4] The first report on the resistive switching phenomenon dates back to the 1960s; [5] since early theories were insufficient to explain this phenomenon, research had been done on it. It was not until the memristor was theoretically proposed in 1971, that the mechanisms underpinning the resistive switching became abundant. [6] The first memristor was manufactured by Hewlett-Packard in 2008. [7] Since then, memristors made of diverse materials have been successfully studied, including conductive filament memristors, magnetic tunnel junctions, ferroelectric tunnel junctions, phase-change memristors, and so on (Figure 1). These devices have been used for storage and computing purposes. [8,9] In recent years, there have been many reviews investigating neuromorphic computing from the perspectives of device electrical properties, [9,10] resistive switching materials, [11,12] memristive synapses and neurons, [13] algorithm optimization, [14] and circuit design. [15] Different from the existing literature, we discuss the possibility of achieving brain-like computing from the perspective of memristor technology and review the establishment of spiking neural network neuromorphic computing systems. In this article, we first review the resistive switching mechanisms of different types of memristors and focus on factors, which affect device stability and the corresponding optimization measures that have been applied. Furthermore, we study the stochasticity, power consumption, switching speed, retention, endurance, and other properties of memristors, which are the basis for neuromorphic computing implementations. We then review various memristor-based neural networks and the building of spike neural network neuromorphic computing systems. Finally, we shed light upon the major challenges and offer our perspectives and opinions for memristor-based brainlike computing systems.The memristor is a resistive switch where its resistive state is programable based on the applied voltage or current. Memristive devices are thus capable of storing and computing information simultaneously, breaking the Von Neumann bottleneck. Since the first nanomemristor made by Hewlett-Packard in 2008, advances so far have enabled nanostructured, low-power, high-durability devices that exhibit superior performance over conventional CMOS devices. Herein, the development of memristors based on different physical mechanisms is reviewed. In particular, device stability, integration density, power consumption, switching speed, retention, and e...

Porous crystalline materials for memories and neuromorphic computing systems

“…, artificial synapses and artificial neurons) is crucial for constructing neuromorphic computing systems capable of overcoming the von Neumann bottleneck in this post-Moore's law era. 71,72,74,75,89,393,394,479,480 Up to now, various devices, including memristor, 70,72,73,481–488 flash memory, 285,489–492 EG-FET, 293,295,296,489,490,493–496 and memtransistor, 497–499 based on different functional materials, 484,500,501 such as 2D materials, 85–88,387,502–508 perovskite, 76–80,389,509,510 biomaterials, 81,82 TMO, 385,511–513 and organic materials, 71,90,514,515 have been utilized for neuromorphic devices.…”

Low-Temperature Solution-Based Molybdenum Oxide Memristors

“…Nonvolatile memory devices (NVMs) are emerging due to the high demand for simultaneous data storage and processing that actual technologies cannot overcome due to physical limitations . Memristors are one type of NVMs that stand out for their low power consumption, high density, and ability to emulate the synaptic plasticity of the brain, with an expected market growth of 52% until 2028. , One of its major applications is in neuromorphic computing, as memristors can emulate the synaptic behavior of a neuron. , …”

“…1 Memristors are one type of NVMs that stand out for their low power consumption, high density, and ability to emulate the synaptic plasticity of the brain, 2 with an expected market growth of 52% until 2028. 3,4 One of its major applications is in neuromorphic computing, as memristors can emulate the synaptic behavior of a neuron. 5,6 Memristors are two terminal devices with fast operation based on a reversible switching (RS) mechanism between a low resistance state (LRS) and a high resistance state (HRS).…”

Light- and Pressure-Stimulated Silver Oxide (AgOx)-Based Memristors for In-Sensor Memory and Computing Applications

“…DC sputtering, electron beam evaporation, thermal oxidation, , and pulsed laser deposition are some of the standard methods for growing silver oxide thin films. Hence, there is a need to study and explore this material’s memristive nature and its sensing ability for a low-cost and energy-efficient future memristive device for possible neuromorphic computing applications. ,, …”