Bidirectional Electric-Induced Conductance Based on GeTe/Sb2Te3 Interfacial Phase Change Memory for Neuro-Inspired Computing

Kang, Shin Young; Jin, Soo‐Min; Lee, Ju Young; Woo, Dong Hun; Shim, Tae Hun; Nam, In Ho; Park, Jea‐Gun; Sutou, Yuji; Song, Yun Heub

doi:10.3390/electronics10212692

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…Reproduced under the terms of the CC‐BY Creative Commons Attribution 4.0 International License. [ 81 ] Copyright 2021, Multidisciplinary Digital Publishing Institute. f) Gradual reset achieved by stair‐case LTP pulses and gradual set achieved by voltage increasing LTD pulses.…”

Section: Synaptic Devices For Neuromorphic Computingmentioning

confidence: 99%

“…www.advmattechnol.de Kang et al proposed that iPCM can achieve multiple conductance states through partial phase transitions in the GeTe layers. [81] They fabricated (GeTe/Sb 2 Te 3 ) 16 iPCM and demonstrated linear and symmetric weight update, an on/off ratio of 13.1×, and 40 conductance states by applying identical 70-ns LTP, LTD pulses (Figure 4e). However, the phase distributions within multiple GeTe layers may differ from device-to-device and cycleto-cycle, requiring subsequent evaluation.…”

Section: Phase-change Memorymentioning

confidence: 99%

“…e) Synaptic weight modulation in (GeTe/Sb 2 Te 3 ) 16 iPCM by applying LTP (70 ns, 0.6 V) and LTD (70 ns, 1.8 V) pulses. Reproduced under the terms of the CC-BY Creative Commons Attribution 4.0 International License [81]. Copyright 2021, Multidisciplinary Digital Publishing Institute.…”

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confidence: 99%

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Recent Advances in Synaptic Nonvolatile Memory Devices and Compensating Architectural and Algorithmic Methods Toward Fully Integrated Neuromorphic Chips

Byun

Choi

Kwon

et al. 2022

Adv Materials Technologies

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Nonvolatile memory (NVM)‐based neuromorphic computing has been attracting considerable attention from academia and the industry. Although it is not completely successful yet, remarkable achievements have been reported pertaining to synaptic devices that can leverage NVM capable of storing multiple states. The analog synaptic devices performing computation similar to biological nerve systems are crucial in energy‐efficient analog neuromorphic computing systems. To use NVM as an analog synaptic device, researchers focus on improving device characteristics. Among various characteristics, the most challenging one is linearity and symmetry of synaptic weight update that is required for on‐chip training. In this regard, this review paper discusses recent synaptic device improvements focusing on novel schemes tailored for each NVM device to improve the linearity and symmetry. In addition to device‐level studies, recent research achievements are reviewed expanded up to chip‐level studies because in realizing neuromorphic hardware systems beyond a single synaptic device, several considerations and requirements are needed to confirm for high‐level design, and accordingly, cooptimize among synaptic devices, synapse arrays, electrical circuits, neural networks, algorithms, and implementation. Also, this review paper introduces various circuit and algorithmic approaches to compensate for the non‐ideality of the analog synaptic device.

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Section: Synaptic Devices For Neuromorphic Computingmentioning

confidence: 99%

Section: Phase-change Memorymentioning

confidence: 99%

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Recent Advances in Synaptic Nonvolatile Memory Devices and Compensating Architectural and Algorithmic Methods Toward Fully Integrated Neuromorphic Chips

Byun

Choi

Kwon

et al. 2022

Adv Materials Technologies

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“…Although the switching mechanism of iPCM has still not been elucidated, it operates at low energy consumption by restricting the atomic movement of Ge atoms. [ 12–14 ] Many approaches have been used to improve the properties of memory devices of the iPCM; however, the experimental configuration of an artificial synapse still needs to be investigated. In this article, we demonstrate the synaptic properties by fabricating different numbers of GeTe/Sb 2 Te 3 layers via sputtering.…”

Section: Introductionmentioning

confidence: 99%

“…The (GeTe/Sb 2 Te 3 ) n iPCM realized more suitable nonlinearity, dynamic range, and number of conductance states of the 8‐ and 16‐layer (GeTe/Sb 2 Te 3 ) n iPCM in the short pulse width for artificial synapses than the Ge 2 Sb 2 Te 5 alloy. [ 12 ]…”

Section: Introductionmentioning

confidence: 99%

Layer‐Dependent Effects of Interfacial Phase‐Change Memory for an Artificial Synapse

Kang

Jin

Lee

et al. 2022

Physica Rapid Research Ltrs

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Recent research on artificial intelligence (AI) has focused on the computational performance of the human brain as a model to process large amounts of data to overcome the limits of current technology. [1,2] This is because the conventional von-Neumann architecture used to operate current AI algorithms causes a performance bottleneck between the computation required and the capacity of memory units. To achieve performance comparable to the biological brain using electronic devices, neuromorphic computing systems have been proposed. [3] This design, consisting of numerous artificial synapses that are essential for hardware advancement, has recently been demonstrated to have power-efficient computation and be extremely compact. [4] Therefore, the artificial synapse should be a simple twoterminal device to achieve brain-level (%10 15 synapses) compactness. Notably, artificial synaptic devices have low energy consumption for gradual conductance states to realize analog-like transitions, including long-term potentiation/longterm depression (LTP/LTD) and spike-timing-dependent plasticity. [5] Based on emerging nonvolatile memory technologies for realizing such characteristics, the devices are classified into phase-change synaptic devices, [6] resistive change synaptic devices, [7] and conductive-bridge synaptic devices relying on a physical switching mechanism. [8] Among them, phase-change synaptic devices have attracted attention because of their reliability and scalability down to the nanometer regime. [6,9] Ge 2 Sb 2 Te 5 alloys, a commonly used phase-change material, exhibit unique switching that creates a resistance difference between amorphous (RESET) and crystalline (SET) states by joule heating. However, a large electrical programming current pulse is needed for melting phase-change materials. Although several efforts have been made to reduce the RESET current, more energyefficient structures or materials are still needed, including stability improvements such as atomic migration, resistance drift, and phase segregation. [10] For this reason, an interfacial phase-change memory (iPCM) with a superlattice-like structure created by alternately depositing a GeTe thin film and Sb 2 Te 3 thin film was introduced by Tominaga et al. [11] The resistance difference is created by the behavior of Ge atoms in the GeTe film sandwiched by the van der Waals gap between the Sb 2 Te 3 films. Although the switching mechanism of iPCM has still not been elucidated, it operates at low energy consumption by restricting the atomic movement of Ge atoms. [12][13][14] Many approaches have been used to improve the properties of memory devices of the iPCM; however, the experimental configuration of an artificial synapse still needs to be investigated. In this article, we demonstrate the synaptic properties by fabricating different numbers of GeTe/Sb 2 Te 3 layers via sputtering.

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Long‐term and short‐term plasticity independently mimicked in highly reliable Ru‐doped Ge₂Sb₂Te₅ electronic synapses

Wang,

Wang

et al. 2024

InfoMat

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In order to fulfill the complex cognitive behaviors in neuromorphic systems with reduced peripheral circuits, the reliable electronic synapses mimicked by single device that achieves diverse long‐term and short‐term plasticity are essential. Phase change random access memory (PCRAM) is of great potential for artificial synapses, which faces, however, difficulty to realize short‐term plasticity due to the long‐lasting resistance drift. This work reports the ruthenium‐doped Ge2Sb2Te5 (RuGST) based PCRAM, demonstrating a series of synaptic behaviors of short‐term potentiation, pair‐pulse facilitation, long‐term depression, and short‐term plasticity in the same single device. The optimized RuGST electronic synapse with the high transformation temperature of hexagonal phase >380°C, the outstanding endurance >108 cycles, the low resistance drift factor of 0.092, as well as the extremely high linearity with correlation coefficients of 0.999 and 0.976 in parts of potentiation and depression. Further investigations also go insight to mechanisms of Ru doping according to thorough microstructure characterization, revealing that Ru dopant is able to enter GST lattices thus changing and stabilizing atomic arrangement of GST. This leads to the short‐term plasticity realized by RuGST PCRAM. Eventually, the proposed RuGST electronic synapses performs a high accuracy of ~94.1% in a task of image recognition of CIFAR‐100 database using ResNet 101. This work promotes the development of PCRAM platforms for large‐scale neuromorphic systems.

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Bidirectional Electric-Induced Conductance Based on GeTe/Sb2Te3 Interfacial Phase Change Memory for Neuro-Inspired Computing

Cited by 4 publications

References 25 publications

Recent Advances in Synaptic Nonvolatile Memory Devices and Compensating Architectural and Algorithmic Methods Toward Fully Integrated Neuromorphic Chips

Recent Advances in Synaptic Nonvolatile Memory Devices and Compensating Architectural and Algorithmic Methods Toward Fully Integrated Neuromorphic Chips

Layer‐Dependent Effects of Interfacial Phase‐Change Memory for an Artificial Synapse

Long‐term and short‐term plasticity independently mimicked in highly reliable Ru‐doped Ge₂Sb₂Te₅ electronic synapses

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Bidirectional Electric-Induced Conductance Based on GeTe/Sb2Te3 Interfacial Phase Change Memory for Neuro-Inspired Computing

Cited by 4 publications

References 25 publications

Recent Advances in Synaptic Nonvolatile Memory Devices and Compensating Architectural and Algorithmic Methods Toward Fully Integrated Neuromorphic Chips

Recent Advances in Synaptic Nonvolatile Memory Devices and Compensating Architectural and Algorithmic Methods Toward Fully Integrated Neuromorphic Chips

Layer‐Dependent Effects of Interfacial Phase‐Change Memory for an Artificial Synapse

Long‐term and short‐term plasticity independently mimicked in highly reliable Ru‐doped Ge2Sb2Te5 electronic synapses

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Product

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Long‐term and short‐term plasticity independently mimicked in highly reliable Ru‐doped Ge₂Sb₂Te₅ electronic synapses