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Abstract Memory technologies and applications implemented fully or partially using emerging 2D materials have attracted increasing interest in the research community in recent years. Their unique characteristics provide new possibilities for highly integrated circuits with superior performances and low power consumption, as well as special functionalities. Here, an overview of progress in 2D‐material‐based memory technologies and applications on the circuit level is presented. In the material growth and fabrication aspects, the advantages and disadvantages of various methods for producing large‐scale 2D memory devices are discussed. Reports on 2D‐material‐based integrated memory circuits, from conventional dynamic random‐access memory, static random‐access memory, and flash memory arrays, to emerging memristive crossbar structures, all the way to 3D monolithic stacking architecture, are systematically reviewed. Comparisons between experimental implementations and theoretical estimations for different integration architectures are given in terms of the critical parameters in 2D memory devices. Attempts to use 2D memory arrays for in‐memory computing applications, mostly on logic‐in‐memory and neuromorphic computing, are summarized here. Finally, challenges that impede the large‐scale applications of 2D‐material‐based memory are reviewed, and perspectives on possible approaches toward a more reliable system‐level fabrication are also given, hopefully shedding some light on future research.
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Perspective: Uniform switching of artificial synapses for large-scale neuromorphic arrays
Resistive random-access memories are promising analog synaptic devices for efficient bio-inspired neuromorphic computing arrays. Here we first describe working principles for phase-change random-access memory, oxide random-access memory, and conductive-bridging random-access memory for artificial synapses. These devices could allow for dense and efficient storage of analog synapse connections between CMOS neuron circuits. We also discuss challenges and opportunities for analog synaptic devices toward the goal of realizing passive neuromorphic computing arrays. Finally, we focus on reducing spatial and temporal variations, which is critical to experimentally realize powerful and efficient neuromorphic computing systems.
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- Award ID(s):
- 1740184
- PAR ID:
- 10595021
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- APL Materials
- Volume:
- 6
- Issue:
- 12
- ISSN:
- 2166-532X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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