Recent advances on crystalline materials-based flexible memristors for data storage and neuromorphic applications

Abstract

Memristors have recently emerged as promising contenders for in-memory computing and artificial neural networks, attributed to their analogies to biological synapses and neurons in structural and electrical behaviors. From the diversity level, a variety of materials have been demonstrated to have great potential for memristor applications. Herein, we focus on one class of crystalline materials (CMs)-based flexible memristors with state-of-the-art experimental demonstrations. Firstly, the typical device structure and switching mechanisms are introduced. Secondly, the recent advances on CMs-based flexible memristors, including 2D materials, metal-organic frameworks, covalent organic frameworks, and perovskites, as well as their applications for data storage and neuromorphic devices are comprehensively summarized. Finally, the future challenges and perspectives of CMs-based flexible memristors are presented.

To Be or Not to Be – Review of Electrical Bistability Mechanisms in Polymer Memory Devices

“…These factors that have been reported to determine how the nanoparticles behave and interact in the switching process; was found integral to be included. These have been added in the further subsections (Section 6.1.1., 6 Simmon-Verderber-like switching [45] Al/Alq3/Ag,Cr,Mg,Cu/Alq3/Al Simmon-Verderber-like switching [45] Al/Alq3/Ni/Alq3/Al Space-Charge-Limited Current (SCLC), Thermionic-field-emission [46] Al/α-NPD/Alq3/Ni/ Alq3/α-NPD/Al SCLC [47] ITO/Alq3/Ag CF formation [48] Al/Alq3 Al/Alq3/Al CF formation [53] Al/Alq3/n-Si Schottky-type conduction [54] materials and device structures. The charge storage mechanism has been presented in great detail in Section 7; attempts are made to present the differences within the different models that exist under the broad umbrella of charge storage mechanisms and how they differ from each other.…”

“…[4,5] Organic memory devices (OMDs) are a rapidly evolving field with much improvement in device performance, fabrication, and application. Much work has been done in this field with reports about new material properties [6][7][8] or device structure [9][10][11][12][13] or even novel fabrication techniques [13][14][15][16][17] being reported. But the reports have been disparate and hugely diverse and, there is no consensus of the observed electrical behavior; this review article is thus intended to present an overview of the development of the field and the different mechanisms that explain the switching behavior of various systems and contradictions among the reported work.…”

Variable Learning‐Memory Behavior from π‐Conjugated Ligand to Ligand‐Containing Cobalt(II) Complex†

“…Organic semiconductor‐based memristive devices, denoted as OSMs, have been considered to be a promising device concept for next‐generation memory & data‐storage technology. [ 1‐3 ] Recent years have been discovering a variety of organic semiconductor materials for the application of OSMs, including organic small molecules, polymers, organic‐inorganic hybrid materials, and multi‐component materials. [ 4‐6 ] Among these, organic small molecules display outstanding characteristics of easy fabrication, tailored geometric construction, simple purification, and so on, which have gained increasing interest over the past years.…”

Two‐Terminal Self‐Gating Random‐Access Memory Based on Partially Aligned 2D Heterostructures

“…[4][5][6][7] Various non-volatile memory arrays have been adopted to implement logic-in memory, which includes floating gate field-effect transistors (FGFETs) based on 2D materials [1] or ferroelectric materials, [8][9][10][11] and two-terminal nonvolatile memory devices, such as phase-change memory (PCM) [12][13][14][15] and resistive random access memory (ReRAM). [16][17][18][19][20][21][22][23][24] Compared to the FGFETs with three terminals, two-terminal nonvolatile memory devices have the advantage of possible integration implemented using a 2 × 1 array. The AND logic gate could be converted to an OR logic gate by learning or potentiation, and this OR logic gate could be changed back to an AND logic gate by depression.…”