Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence

Choi, Chanyeol; Kim, Hyunseok; Kang, Ji‐Hoon; Song, Moon Hee; Yeon, Han-Wool; Chang, Celesta S.; Suh, Jun Min; Shin, Jiho; Lu, Kuangye; Park, Bo‐In; Kim, Yeongin; Lee, Han Eol; Lee, Doyoon; Lee, Jae Yong; Jang, Ikbeom; Pang, Subeen; Ryu, Kanghyun; Bae, Sang‐Hoon; Nie, Yifan; Kum, Hyun; Park, Min-Chul; Lee, Suyoun; Wu, Huaqiang; Lin, Peng; Kim, J.

doi:10.1038/s41928-022-00778-y

Cited by 81 publications

(40 citation statements)

References 39 publications

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“…(g) Letter images are denoised by inserting a denoising layer that includes memristor crossbar arrays, as described in the block diagram. Reproduced with permission from [ 221 ], copyright 2022, Springer Nature.…”

Section: Applicationsmentioning

confidence: 99%

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A review of memristor: material and structure design, device performance, applications and prospects

Xiao

Jiang

Chen

et al. 2023

Science and Technology of Advanced Materials

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With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.

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Section: Applicationsmentioning

confidence: 99%

“…As shown in Figure 11(g) , the letter images were then denoised by inserting a denoising layer including a memristor crossbar array. The results showed that the stackable and replaceable chips exhibited excellent immunity to high noise level [ 221 ]. Zhong et al reported a dynamic memristor-based parallel reservoir computing system.…”

Section: Applicationsmentioning

confidence: 99%

A review of memristor: material and structure design, device performance, applications and prospects

Xiao

Jiang

Chen

et al. 2023

Science and Technology of Advanced Materials

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“…Choi et al, developed heterointegrated chips comprising optoelectronic devices (light-emitting diode (LED) and PD arrays) and neuromorphic cores (memristor crossbar arrays) [ 157 ]. In their system, stackable and replaceable chips were embedded for classifying light-based input image information.…”

Section: Light-sensitive Synaptic Device For Image Sensormentioning

confidence: 99%

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Seok

Son

Jathar

et al. 2023

Sensors

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Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

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mentioning

confidence: 99%

Thousands of conductance levels in memristors monolithically integrated on CMOS

Yang

2023

Proceedings of the Neuromorphic Materials, Devices, Circuits and Systems

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Neural networks based on memristive devices [1][2][3] have shown potential in substantially improving throughput and energy efficiency for machine learning [4] and artificial intelligence [5], especially in edge applications. [6][7][8][9][10][11][12][13][14][15][16][17][18][19] Because training a neural network model from scratch is very costly, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications (Figure 1a). Some posttuning in memristor conductance to adapt local situations may follow afterward or during applications. Therefore, a critical requirement on memristors for neural network applications is a high-precision programming ability to guarantee uniform and accurate performance across a massive number of memristive networks. [20][21][22][23][24][25][26] That translates into the requirement of many distinguishable conductance levels on each memristive device, not just lab-made devices but more importantly, devices fabricated in foundries. High precision memristors also benefit other neural network applications, such as training and scientific computing. [23,27] Here we report over 2048 conductance levels, the largest number among all types of memories ever reported, achieved with memristors in fully integrated chips with 256 ´ 256 memristor arrays monolithically integrated on CMOS circuits in a standard foundry. We have unearthed the underlying physics that previously limited the number of achievable conductance levels in memristors and developed electrical operation protocols to circumvent such limitations. These results reveal insights into the fundamental understanding of the microscopic picture of memristive switching and provide approaches to enable high-precision memristors for various applications.Memristive switching devices are known for their relatively large dynamical range of conductance, which can potentially lead to a large number of discrete conductance levels. However, the highest number reported to date has been no more than two hundred. [20]

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Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence

Cited by 81 publications

References 39 publications

A review of memristor: material and structure design, device performance, applications and prospects

A review of memristor: material and structure design, device performance, applications and prospects

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Thousands of conductance levels in memristors monolithically integrated on CMOS

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