Recent Progress in Multiterminal Memristors for Neuromorphic Applications

Abstract

Neumann system is a conventional computing architecture with divided processor and memory unit which executes computational tasks sequentially which has served as a pillar of contemporary computing since 1945. However, the frequent data shuffling between the separated processor and memory unit induce massive power consumption and latency which is so-called von Neumann bottleneck. [7,8] The human brain is capable of concurrently executing several complicated tasks with enormous parallelism with extremely low power consumption, outstanding fault tolerance, and remarkable durability owing to its extensive connection, functional organizational hierarchy, advanced learning rules, and neuronal plasticity. Inspiring from it, Mead first pioneered the notion of "neuromorphic computing" in the late 1980s and early 1990s. [9,10] Accordingly, to eliminate inherent constraint of the von Neumann systems, substantial effort has been focused on investigating neuromorphic computing systems. [11][12][13] Biological neuromorphic systems consisting 10 11 neurons interconnected with each other via 10 15 synapses [14,15] is capable to respond to environment and history at different levels from simple molecular (nucleic acids could displays adaptive behaviors including self-repair and replication, under stimuli from the local environment), the elementary information-processing blocks in biological systems (neurons could exhibit more than 20 different dynamic behaviors triggered by historically and environmentally electrochemical stimulation), to whole functional systems with more hierarchical complexity (extremely low or high relative humidity (RH), has a substantial impact on the accuracy of human visual system). [16][17][18] Recently, inspired by human sensory processing and perceptual learning, neuromorphic sensing and computing systems incorporating sensors and machine learning algorithms have been demonstrated to perceive, process, and integrate diverse sensory information where the adaptation and learning can be obtained through dynamically updating the weights of neural network according to the different training algorithms. [19,20] However, on contrary to the distributed processing in biological hierarchical architectures which is more adaptable and cognitive for the optimum analysis of complicated information, the modern computing systems adopting centralized processing are still based on static elements with zeroth-order complexity (e.g., transistor). The essential step for developing neuromorphic systems is to The essential step for developing neuromorphic systems is to construct more biorealistic elementary devices with rich spatiotemporal dynamics to exhibit highly separable responses in dynamic environmental circumstances. Unlike transistor-based devices and circuits with zeroth-order complexity, memristors intrinsically express some simple biomimetic functions. However, with only two-terminal structure, precise control of operation principles to ensure large dynamic space, improved linearity and symmetry, multimodal oper...

Resistive Switching Devices for Neuromorphic Computing: From Foundations to Chip Level Innovations

“…Further optimizations have to be focused both at the device architectural level and at the material selection choices to minimise issues like the high ON current observed in RRAM devices. Device architectural innovations like multi-terminal device design [ 172 ], 3D stacking [ 173 ], interface & filament modulation [ 174 ], defect engineering [ 175 ], etc. have been effective in mitigating RRAM device-level non-idealities.…”

N-Doped Graphene/MXene Nanocomposite as a Temperature-Adaptive Neuromorphic Memristor

“…This change is reversible and can be repeatedly erased and written. Due to the reconfigurability and adaptivity of the memristor, it can achieve spike-timing-dependent plasticity (STDP), short-term potentiation/depression (STP/STD), long-term potentiation/depression (LTP/LTD), and paired-pulse facilitation (PPF), which can be used to develop AI and neural network systems, commonly known as electrical synapses. − Electrical synapses have been widely studied to explore their potential for simulation application in neural networks, pattern recognition, and intelligent control. , …”

Computational Study on Interlocked-Ferroelectricity-Contributed High-Performance Memristors Based on Two-Dimensional van der Waals Ferroelectric Semiconductors

“…5 Since the first discovery of memristors by HP Laboratories in 2008, 6 diverse memristors, as well as their relevant operation mechanisms, have been reported. 4,7,8 They have two-terminal architectures and so they can be easily integrated into large-scale crossbar arrays. However, twoterminal memristors feature homosynaptic plasticity to emulate biological neural networks.…”

“…Memristors, used as a basic circuit element on integrated chips, have received widespread attention from academia and industry to realize the next-generation in-memory/neuromorphic computing . Since the first discovery of memristors by HP Laboratories in 2008, diverse memristors, as well as their relevant operation mechanisms, have been reported. ,, They have two-terminal architectures and so they can be easily integrated into large-scale crossbar arrays. However, two-terminal memristors feature homosynaptic plasticity to emulate biological neural networks.…”