Demonstration of electronic and optical synaptic properties modulation of reactively sputtered zinc-oxide-based artificial synapses

Mahata, Chandreswar; Park, Jong-Min; Ismail, Muhammad; Kim, Sungjun

doi:10.1016/j.jallcom.2022.168539

Cited by 18 publications

(5 citation statements)

References 76 publications

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“…Figure a depicts the similarity between the learning process based on the neurotransmitter intensity between synaptic neurons and the resistance changes owing to the filament stability between the memristor electrodes. Linear changes in synaptic weight directly lead to learning and memory activities in the neuromorphic system via synaptic plasticity. − This study confirms the availability of a synaptic device in the neuromorphic system using STM and LTM properties. Figure b,c demonstrates linear conductance changes based on the EPSC synaptic function using 10 programming pulses.…”

Section: Resultssupporting

confidence: 66%

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Lee,

Huang,

Chang

et al. 2024

ACS Nano

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In this study, we investigate the coexistence of short-and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS 2 / Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS 2 in a crossbar structure. The presence of both short-and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide. Short-and long-term properties are validated based on programmable multilevel retention tests. Moreover, we confirm various synaptic characteristics of the device, demonstrating its potential use as a synaptic device in a neuromorphic system. Excitatory postsynaptic current, paired-pulse facilitation, spikerate-dependent plasticity, and spike-number-dependent plasticity synaptic applications are implemented by operating the device at a low-conductance level. Furthermore, long-term potentiation and depression exhibit symmetrical properties at highconductance levels. Synaptic learning and forgetting characteristics are emulated using programmable retention properties and composite synaptic plasticity. The learning process of artificial neural networks is used to achieve high pattern recognition accuracy, thereby demonstrating the suitability of the use of the device in a neuromorphic system. Finally, the device is used as a physical reservoir with time-dependent inputs to realize reservoir computing by using short-term memory properties. Our study reveals that the proposed device can be applied in artificial intelligence-based computing applications by utilizing its programmable retention properties.

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Section: Resultssupporting

confidence: 66%

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Lee,

Huang,

Chang

et al. 2024

ACS Nano

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“…Additionally, a neuromorphic system can be implemented with conductance changes that have a broad dynamic range and linear, symmetric properties. [60,61] From the perspective of a neuromorphic system, the P&D responses of the TiN/TiO 2 /WO X /Pt device to pulse stimulation demonstrate stability and excellent conductivity programming. When analyzing synaptic characteristics, the P&D curves of the TiN/TiO 2 /WO X /Pt device may be used as synaptic weights in neural networks; as the linearity and symmetric features of the curves are enhanced, the synaptic characteristics become more predictable.…”

Section: Applications As a Synaptic Devicementioning

confidence: 99%

Synaptic Properties and Short‐Term Memory Dynamics of TiO₂/WO_x Heterojunction Memristor for Reservoir Computing

So,

Lee,

Mahata

et al. 2024

Adv Materials Technologies

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A hard breakdown phenomenon occurs in the TiN/WOX/Pt device owing to the metallic nature of the WOX layer deposited by pulsed direct current (DC) sputtering. In particular, analog resistive switching (RS) is achieved as the defect states of the naturally occurring TiON layer (oxygen vacancies region) between TiN and TiO2 fluctuate based on the polarity of the bias. Interestingly, the TiN/TiO2/WOX/Pt device displays gradual, bipolar, SET and RESET operations during DC voltage sweep cycling without requiring an electroforming process. Excellent linearity in potentiation and depression is demonstrated via identical pulse trains based on the analog RS behavior. Additionally, the neuromorphic system simulation achieved a pattern‐recognition accuracy of over 95% when conductance is employed as the weight in the neural network. Furthermore, essential synaptic functions, such as spike‐rate‐dependent plasticity (SRDP), spike‐number‐dependent plasticity (SNDP), the transition from short‐term plasticity to long‐term plasticity, “learning‐experience” behaviors, and paired‐pulse facilitation (PPF), are demonstrated to emulate biological synapses for neuromorphic computing applications. Lastly, a reservoir computing system (RC) is implemented using the short‐term memory effect of the TiN/TiO2/WOX/Pt device. Specifically, it is deployed to differentiate all 16 (4‐bit) states using various pulse trains, and a simple algorithm is suggested to implement a low‐power consumption system.

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“…[3,4] Among various non-volatile memories, phase change random access memory (PCRAM) is attractive due to outstanding performances including the reliability, the energy efficiency and the mature mass fabrication, and thus PCRAM becomes a strong candidate for hardware electronic synapse. [5][6][7][8] As a dominant material for PCRAM, the Ge-Sb-Te (GST) based alloy has achieved a remarkable success in the industrialization of data storage. However, extensive studies on GST based PCRAM in the past decades mainly focused on improving the binary data storage performances, such as enlarging the ON/Off ratio, reducing the power performance, enhancing the reliability and speeding up the erase/write operation (fast switching), [9][10][11][12] which do not necessarily lead to the linearly and continuously controllable conductance.…”

Section: Introductionmentioning

confidence: 99%

Set/Reset Bilaterally Controllable Resistance Switching Ga‐doped Ge₂Sb₂Te₅ Long‐Term Electronic Synapses for Neuromorphic Computing

Wang

Luo

Wang

et al. 2023

Adv Funct Materials

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Long‐term plasticity of bio‐synapses modulates the stable synaptic transmission that is quite related to the encoding of information and its emulation using electronic hardware is one of important targets for neuromorphic computing. Ge2Sb2Te5 (GST) based phase change random access memory (PCRAM) has become a strong candidate for complementary‐metal‐oxide‐semiconductor (CMOS) compatible integrated long‐term electronic synapses to cope with the high‐efficient and low power consumption data processing tasks for neuromorphic computing. However, the performance of PCRAM electronic synapses is still quite limited due to the challenges in linear and continuous conductance regulation, which originates from the fast and uncontrollable resistance switching characteristic of conventional PCRAM for the data storage application. Here an in‐depth study is reported on the impact of gallium (Ga) doping on GST (GaGST) structural properties and on the corresponding 0.13 µm CMOS technology fabricated PCRAM integrated devices with a mushroom structure. The Ga doping effectively retarded the crystallization process of GST by augmenting the local disorder of GeTe4‐nGen tetrahedron, which subsequently leads to the Set/Reset bilaterally controllable resistance switching of corresponding PCRAM devices. The optimized 6.5%GaGST electronic synapses demonstrate gradual resistance switching characteristics and a good multilevel retention feature and eventually exhibit outstanding long‐term synaptic plasticity like potentiation/depression and spiking time dependent plasticity in four forms. Such long‐term electronic synapses are applied to handwritten digits recognition (96.22%) and CIFAR‐10 image categorization (93.6%) and attain very high accuracy for both tasks. These results provide an effective method to achieve high performance PCRAM electronic synapses and highlight the great potential of GaGST PCRAM as a component for future high‐performance neuromorphic computing.

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Demonstration of electronic and optical synaptic properties modulation of reactively sputtered zinc-oxide-based artificial synapses

Cited by 18 publications

References 76 publications

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Synaptic Properties and Short‐Term Memory Dynamics of TiO₂/WO_x Heterojunction Memristor for Reservoir Computing

Set/Reset Bilaterally Controllable Resistance Switching Ga‐doped Ge₂Sb₂Te₅ Long‐Term Electronic Synapses for Neuromorphic Computing

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Demonstration of electronic and optical synaptic properties modulation of reactively sputtered zinc-oxide-based artificial synapses

Cited by 18 publications

References 76 publications

Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Synaptic Properties and Short‐Term Memory Dynamics of TiO2/WOx Heterojunction Memristor for Reservoir Computing

Set/Reset Bilaterally Controllable Resistance Switching Ga‐doped Ge2Sb2Te5 Long‐Term Electronic Synapses for Neuromorphic Computing

Contact Info

Product

Resources

About

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Synaptic Properties and Short‐Term Memory Dynamics of TiO₂/WO_x Heterojunction Memristor for Reservoir Computing

Set/Reset Bilaterally Controllable Resistance Switching Ga‐doped Ge₂Sb₂Te₅ Long‐Term Electronic Synapses for Neuromorphic Computing