A crossbar array of magnetoresistive memory devices for in-memory computing

Jung, Seungchul; Lee, Hyungwoo; Myung, Sungmeen; Kim, Hak Soo; Yoon, Seung Keun; Kwon, Sin; Ju, Yong-Min; Kim, Minje; Yi, Won Ju; Han, Shin-Hee; Kwon, B. S.; Seo, Bong-Gun; Lee, Kilho; Koh, G.H.; Lee, Kang-Ho; Song, Y. J.; Choi, Changkyu; Ham, Donhee; Kim, Sang Joon

doi:10.1038/s41586-021-04196-6

Cited by 263 publications

(146 citation statements)

References 37 publications

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“…Various non‐volatile memory devices, such as ferroelectric random access memory (FRAM), phase‐change random access memory (PRAM), spin‐torque‐transfer magnetic random access memory (STT‐MRAM), and resistive switching random access memory (RRAM) have been considered for use as a memory element. [ 11–22 ] Among these devices, the RRAM exhibits outstanding characteristics, such as device scaling down, low power consumption, fast operation speed, simple structure, and high reliability.…”

Section: Introductionmentioning

confidence: 99%

Dot‐Product Operation in Crossbar Array Using a Self‐Rectifying Resistive Device

Jeon

Ryu

Jeong

et al. 2022

Adv Materials Inter

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unit between vector matrices, the workload of MAC with high-dimensional vector matrices increases exponentially. [4,5] Alternatively, the process-in-memory (PIM) unit, a concept inspired by the human brain, outperforms the von Neumann computing system in unstructured data processing considering it can provide a parallel MAC operation and reduces the time of the data bus between the CPU and the memory.A crossbar array (CA) type device is the most suitable and intuitive structure to achieve the PIM owing to its complex parallel connection of unit memory cells in the CA. [6][7][8] On applying the input signal (voltage bias) to the CA, the output signal (current, input voltage (V) × unit cell conductance (S)) is obtained instantly and simultaneously through each output line by Kirchhoff's law. [9,10] Owing to this "instant and simultaneous" calculation method, the CA-based PIM outperforms the serial process based von-Neumann computing in terms of the operation energy and time. Additionally, the CAbased PIM exhibits an immune response in the MAC operation with vector matrix expansion, which is suitable for processing large amounts of unstructured data.A data storage unit in the CA-based PIM should simultaneously exhibit two types of characteristics: 1) Highly reliable and low-power memory device and 2) device for selection function to suppress the interference effect from the parallel-connected neighboring cells in the CA. Various non-volatile memory devices, such as ferroelectric random access memory (FRAM), phase-change random access memory (PRAM), spin-torquetransfer magnetic random access memory (STT-MRAM), and resistive switching random access memory (RRAM) have been considered for use as a memory element. [11][12][13][14][15][16][17][18][19][20][21][22] Among these devices, the RRAM exhibits outstanding characteristics, such as device scaling down, low power consumption, fast operation speed, simple structure, and high reliability.Various selection devices (SD) such as diodes, [23,24] metalinsulator-transition (MIT) devices, [25,26] ovonic threshold switch (OTS), [27][28][29] mixed-ionic-electronic-conductor (MIEC), [30,31] tunneling-oxide-based devices, [32,33] and timing selector [34] have been proposed to add the selection function to the RRAM devices. Furthermore, a 1-transistor and 1-resistor (1T1R) has been proposed as an active unit cell. [35][36][37] However, stacking SD with RRAM can cause various practical issues. First, tailoring Reducing computational complexity is essential in future computing systems for processing a large amount of unstructured data simultaneously. Dot-product operations using crossbar array devices have attracted considerable attention owing to their simple device structure, intuitive operation scheme, and high computational efficiency of parallel operation. The resistive switching device is considered a promising candidate as the main data storage in the crossbar array owing to its highly reliable performance. In this study, a tri-layer TaO x /Al 2 O 3 /Ti:SiO x -based resistive...

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Section: Introductionmentioning

confidence: 99%

Dot‐Product Operation in Crossbar Array Using a Self‐Rectifying Resistive Device

Jeon

Ryu

Jeong

et al. 2022

Adv Materials Inter

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“…In Table 1, we summarize a few representative experimental demonstrations of IMC using memristor arrays for different practical applications. It is notable that other mainstream NVM-based IMC have also been widely investigated, such as flash [29], phase change memory (PCM) [30], ferroelectric field effect transistor (FeFET) [31], magnetic random-access memory (MRAM) [32], and so on. Although the memristive IMC paradigm is validated in such widespread domains, the different applications claim distinct requirements on the computational accuracies and the corresponding hardware solutions including both memory devices and periphery circuits [33][34][35].…”

Section: Memristive In-memory Computingmentioning

confidence: 99%

Toward memristive in-memory computing: principles and applications

Bao

Zhou

et al. 2022

Front. Optoelectron.

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With the rapid growth of computer science and big data, the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories. Memristive in-memory computing paradigm is considered as a prominent candidate to address these issues, and plentiful applications have been demonstrated and verified. These applications can be broadly categorized into two major types: soft computing that can tolerant uncertain and imprecise results, and hard computing that emphasizes explicit and precise numerical results for each task, leading to different requirements on the computational accuracies and the corresponding hardware solutions. In this review, we conduct a thorough survey of the recent advances of memristive in-memory computing applications, both on the soft computing type that focuses on artificial neural networks and other machine learning algorithms, and the hard computing type that includes scientific computing and digital image processing. At the end of the review, we discuss the remaining challenges and future opportunities of memristive in-memory computing in the incoming Artificial Intelligence of Things era. Graphical Abstract

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“…109 Magnetic tunnel junctions emulate binary weights by switching between two magnetization states. [110][111][112] The intrinsic stochastic nature of magnetization switching in twostate magnetic tunnel junctions can also be leveraged for learning. 113 Memristive behavior is obtained by modifying the magnetization texture to obtain gradual switching via spin-torque 114,115 or spin-orbit torques.…”

Section: B Magnetization Dynamicsmentioning

confidence: 99%

Quantum materials for energy-efficient neuromorphic computing

Hoffmann,

Ramanathan,

Grollier

et al. 2022

Preprint

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Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

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A crossbar array of magnetoresistive memory devices for in-memory computing

Cited by 263 publications

References 37 publications

Dot‐Product Operation in Crossbar Array Using a Self‐Rectifying Resistive Device

Dot‐Product Operation in Crossbar Array Using a Self‐Rectifying Resistive Device

Toward memristive in-memory computing: principles and applications

Quantum materials for energy-efficient neuromorphic computing

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