MoS<sub>2</sub>/Polymer Heterostructures Enabling Stable Resistive Switching and Multistate Randomness

Chai, Jianwei; Tong, S. W.; Li, Changjian; Manzano, Carlos; Li, Bing; Liu, Yanpeng; Lin, Ming; Cheng, Jianwei; Wu, Jing; Lau, Chit Siong; Xie, Qidong; Pennycook, Stephen J.; Medina, Henry; Yang, Ming; Wang, Shijie; Chi, Dongzhi

doi:10.1002/adma.202002704

Cited by 28 publications

(26 citation statements)

References 38 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The highly crystalline structures of the MoS 2 film was confirmed by the periodic hexagonal lattices in the TEM and the radial symmetric spots in the diffraction pattern (figure 2(a)) [34,35]. SEM and OM images (figures S1 and S2 (https://stacks.iop.org/NCE/2/014004/mmedia)) show that the monolayer MoS 2 film grown by CVD was continuous and flat.…”

Section: Resultsmentioning

confidence: 81%

Two-dimensional molybdenum disulfide artificial synapse with high sensitivity

Huang

Liu

Jiang

et al. 2022

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

This paper reports the fabrication of an artificial synapse (AS) based on two-dimensional molybdenum disulfide (MoS2) film. The AS emulates important synaptic functions such as paired-pulse facilitation, spike-rate dependent plasticity, spike-duration dependent plasticity and spike-number dependent plasticity. The spike voltage can mediate ion migration in the ion gel to regulate the MoS2 conductive channel, thereby realizing the emulation of synaptic plasticity. More importantly, benefiting from the atomically-flat surface of MoS2 film, the device has a high sensitivity to external stimuli. It can effectively respond to presynaptic spikes that have an amplitude of 100 mV. The development of this device provides a new idea for constructing a highly-sensitive and multifunctional neuromorphic system.

show abstract

Section: Resultsmentioning

confidence: 81%

Two-dimensional molybdenum disulfide artificial synapse with high sensitivity

Huang

Liu

Jiang

et al. 2022

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

show abstract

“…2D memory devices have also been explored for achieving RNG, [149,214] which is a significant application for in-memory computing in many fields, [9,182] such as stochastic computing, machine learning and data encryption. For a basic RNG application, it requires that the probability of the next state occurring is independent of the past states.…”

Section: Random Number Generationmentioning

confidence: 99%

Emerging 2D Memory Devices for In‐Memory Computing

et al. 2021

View full text Add to dashboard Cite

cube (HMC), [5] high bandwidth memory (HBM), [6,7] and 3D monolithic integration, [8] have been successively developed and achieved some success, the challenge of latency and energy consumption caused by massive data transmission still remains. Therefore, it is urgently necessary to enhance the information interaction capability by improving the architectural relationship between processing and memory units.The emergence of non-von-Neumann architecture solves the problem of the separation of processing and memory units, and alleviates the impact of bus bandwidth on computing efficiency. As a non-Von Neumann architecture, in-memory computing has been considered to be one of the future mainstream trends of hardware implementation for AI algorithms, [9] such as machine learning and deep learning. In-memory computing, where calculations are carried out in situ within each memory unit, [10] has massive parallelism and distributed computing characteristic through integrating millions of memory devices in a crossbar array (Figure 1), analogous to the neurobiological system. [11] Thus, it can radically subvert the von Neumann architecture and achieve the fusion of data processing and storage, thereby totally eliminating the latency and energy loss associated with data access. For enabling this computational architecture, new material systems and highperformance memory devices are highly pursued.2D layered materials refer to the material family that held together by strong in-plane chemical bonds and relatively weak out-of-plane van der Waals interactions, and have attracted worldwide attention due to their unique structure and physical properties. [12][13][14][15][16][17][18][19][20][21] On the one hand, the atomically thin thickness of 2D layered materials provide a significant advantage in achieving high-density integration and low-power operation of high-performance devices. [22][23][24][25][26][27][28] On the other hand, their dangling-bond-free surface and planar structure make them not only compatible with traditional wafer technology, but also can be stacked on top of each other unrestricted by lattice mismatch. Thus, varieties of available 2D layered materials with different electrical properties, such as graphene, transition metal dichalcogenides (TMDs, including MoS 2 , WSe 2 , MoTe 2 , etc.), black phosphorus (BP) and hexagonal boron nitride (h-BN), could be arbitrarily assembled to create a wide range of artificial van der Waals heterostructures (vdWHs) with wholly new functionalities that unavailable in the individual material. [29][30][31][32][33] For example, stable data storage can be realized with the aid of potential barrier formed in vdWHs. [34][35][36][37][38][39] Additionally, It is predicted that the conventional von Neumann computing architecture cannot meet the demands of future data-intensive computing applications due to the bottleneck between the processing and memory units. To try to solve this problem, in-memory computing technology, where calculations are carried out in situ within each nonv...

show abstract

“…In the last two sections, we have already seen examples of how advanced technologies enable electronic mimics of individual parts of the neural system with demonstrated simple computational functionalities. We do not intend to survey the neuromorphic hardware implementation again as this has been done in numerous reviews, just to name a few, at materials level, [ 48,661–722 ] at device level, [ 10,244,263,723–790 ] at more circuit level, or above. [ …”

Section: Implementation Levelmentioning

confidence: 99%

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo

Zou

et al. 2021

Advanced Intelligent Systems

View full text Add to dashboard Cite

Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic electronic systems are complex, having multiple levels of organization. Inspired by David Marr's famous three‐level analytical framework developed for neuroscience, the advances in neuromorphic electronic systems are selectively surveyed and given significance to these research endeavors as appropriate from the computational level, algorithmic level, or implementation level. Under this framework, the problem of how to build a neuromorphic electronic system is defined in a tractable way. In conclusion, the development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low‐level knowledge (implementations and algorithms) to achieve high‐level brain‐like (human‐level) computational functions. An opportunity arises from the communication among different levels and their codesign. Neuroscience lab‐on‐neuromorphic chip platforms offer additional opportunity for mutual benefit between the two disciplines.

show abstract

MoS₂/Polymer Heterostructures Enabling Stable Resistive Switching and Multistate Randomness

Cited by 28 publications

References 38 publications

Two-dimensional molybdenum disulfide artificial synapse with high sensitivity

Two-dimensional molybdenum disulfide artificial synapse with high sensitivity

Emerging 2D Memory Devices for In‐Memory Computing

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Contact Info

Product

Resources

About

MoS2/Polymer Heterostructures Enabling Stable Resistive Switching and Multistate Randomness

Cited by 28 publications

References 38 publications

Two-dimensional molybdenum disulfide artificial synapse with high sensitivity

Two-dimensional molybdenum disulfide artificial synapse with high sensitivity

Emerging 2D Memory Devices for In‐Memory Computing

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Contact Info

Product

Resources

About

MoS₂/Polymer Heterostructures Enabling Stable Resistive Switching and Multistate Randomness