Asymmetric GaN/ZnO Engineered Resistive Memory Device for Electronic Synapses

Khan, Muhammad Umair; Saqib, Qazi Muhammad; Kim, Jungmin; Khan, Sobia Ali; Chougale, Mahesh Y.; Shaukat, Rayyan Ali; Kang, Moon Hee; Kobayashi, Nobuhiko P.; Bae, Jinho; Kwok, Hoi‐Sing

doi:10.1021/acsaelm.1c01006

Cited by 16 publications

(13 citation statements)

References 67 publications

(105 reference statements)

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“…[76,92,101] The resistive switching mechanisms have been reported based on electrochemical, thermal, and electronic effects. [102][103][104][105][106] However, the mechanism study using top rigid metal electrodes is facing problems and it is hard to observe the filament formation and ionic movement in an active layer. [75,76,101,107] To understand the working mechanism, liquid materials as an active switching medium or liquid electrodes are introduced, which help to understand the filament formation using the ion concentration polarization phenomena with the movement of anion and cation resulting in oxidation and reduction on the active metal electrode, which results in electrochemical metallization.…”

Section: Introductionmentioning

confidence: 99%

Advancement in Soft Iontronic Resistive Memory Devices and Their Application for Neuromorphic Computing

Khan

Kim

Chougale

et al. 2022

Advanced Intelligent Systems

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The aqueous electrolyte can be a deformable and stretchable liquid material for iontronic resistive memory devices. An aqueous medium makes a device closer to the brain‐like system with the movement of ions. This review paper proposes advances in liquid resistive memories and neuromorphic computing behavior to emulate electronic synapses. Primarily, the aqueous iontronic resistive memories can be used to study electrode and active layer materials and different device structures. Hence, herein, a timely and comprehensive study of these devices using ionic liquids, hydrogels, salt solutions, and soft electrodes to classify the device mechanism is presented. The filament formation is discussed in detail based on ion concentration polarization, electrode metallization, and movements of ions and charged molecules, which result in the formation of the metal dendrite. To manufacture a higher‐performance memory, device parameters should be optimized based on aqueous electrolytes, electrode materials, and other device design parameters. Aqueous electrolytes have smooth neurotransmission ability to fabricate brain‐inspired resistive memories with stable performance and device repeatability with smooth ion transmission. Aqueous electrode materials can be reliable for neural interface activities to compute electronic synapsis with electrical and chemical properties to ensure device reliability for a longer time period.

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Section: Introductionmentioning

confidence: 99%

Advancement in Soft Iontronic Resistive Memory Devices and Their Application for Neuromorphic Computing

Khan

Kim

Chougale

et al. 2022

Advanced Intelligent Systems

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“…Therefore, many research groups are trying to fabricate artificial synaptic devices for mimicking the biological synaptic properties. 12,13 Today, resistive switching devices are fabricated by utilizing different types of transition-metal oxides (TiO 2 , ZnO, NiO, TaO x , NbO 2 , HfO 2 , etc.). 14,15 However, the conventional materials have their limitations such as high-electroforming voltage, switching nonuniformity, lower device yield, and higher cost.…”

Section: Introductionmentioning

confidence: 99%

“…The resistive switching-based memory devices are considered a strong candidate for futuristic applications because of their simple two-terminal structure, in-memory computing capability, passivity, sub-10 nm scalability, and compatibility with conventional silicon-based fabrication technology. , Moreover, resistive switching devices are worked on very low power, can be integrated with 3D fabrication technology, and have excellent memory retention and endurance properties. , Brain-inspired computing is becoming an attractive field to build next-generation intelligent systems based on artificial intelligence, the Internet of Things, big data, and so on. Therefore, many research groups are trying to fabricate artificial synaptic devices for mimicking the biological synaptic properties. , …”

Section: Introductionmentioning

confidence: 99%

Binder-Free Synthesis of Nanostructured Amorphous Cobalt Phosphate for Resistive Memory and Artificial Synaptic Device Applications

Katkar

Padalkar

Kumbhar

et al. 2022

ACS Appl. Electron. Mater.

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The rise of artificial intelligence and machine learning demands versatile electronic devices for memory and braininspired computing applications. The electronic materials are the backbones of these applications. Considering this, a functional Co x (PO 4 ) 2 nanomaterial was synthesized for resistive memory and neuromorphic computing applications. The synthesized nanomaterial was well characterized by using X-ray diffraction, Fourier transform infrared spectroscopy, field emission-scanning electron microscopy, and X-ray photoelectron spectroscopy. The fabricated Ag/Co x (PO 4 ) 2 /ITO device shows bipolar resistive switching and memristive properties. The SET and RESET voltages were analyzed by using different statistical measures, and their distribution was studied by using the Weibull technique. The results suggested that the SET voltages were more uniformly distributed than the RESET voltage. The switching nonlinearity was modeled and predicted by using Holt's exponential smoothingbased statistical time series analysis method. In the case of nonvolatile memory tests, the device shows good endurance (10 3 cycles) and memory retention (3 × 10 4 s) with excellent memory window (1.7 × 10 3 ) properties. Moreover, the device can mimic the potentiation−depression and spike-timing-dependent plasticity-based Hebbian learning rules, suggesting Co x (PO 4 ) 2 is a potential nanomaterial for the fabrication of artificial synapse. The detailed analysis of electrical results suggested that the space-charge-limited current-based charge transport was responsible for the device conduction, whereas the formation and rupture of conductive filament(s) were responsible for the resistive switching in the Ag/Co x (PO 4 ) 2 /ITO memristive device. The results of the present investigation suggested that the Co x (PO 4 ) 2 nanomaterial is a potential candidate for resistive memory and brain-inspired computing applications

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“…As a processing unit, the artificial synapse must possess many functionalities like potentiation, depression, paired‐pulse facilitation (PPF), and spike‐timing‐dependent plasticity (STDP). [ 19 ] Among these properties, STDP modulation is considered the fundamental Hebbian learning protocol. [ 20–23 ]…”

Section: Introductionmentioning

confidence: 99%

“…As a processing unit, the artificial synapse must possess many functionalities like potentiation, depression, paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP). [19] Among these properties, STDP modulation is considered the fundamental Hebbian learning protocol. [20][21][22][23] RS-based artificial synapses are prominent candidates for neuromorphic computing and signal processing for future artificial intelligence and machine learning development.…”

mentioning

confidence: 99%

Observation of Magnéli Phase Filament Formation in MoO_x Artificial Synapse

Subin

Antony

Saji

et al. 2022

Adv Elect Materials

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Many transition metal oxides (TMOs) exhibit the property of synaptic behavior in sandwiched structures. [12][13][14][15][16] Among TMOs, molybdenum trioxide (MoO 3 ) can have interesting optoelectronic applications, such as resistive random access memory (RRAM) and optoelectronic RRAM. Artificial synapses and neuromorphic vision sensors have attracted many scientists to develop ANNs to perform next-generation computing and image processing. [17,18] The key aspect of the artificial synapse is to mimic the functionalities of the bio-synapse. Many resistive switching (RS) devices show history-dependent behavior according to their previous activity. The devices that show tuneable resistance behavior is the best fit for the artificial synapse. The state-of-the-art MoO x -based devices show good RS characteristics; however, many synaptic properties and a thorough understanding of the mechanism were yet to be reported. The synapses have potential abilities, acting as both processing units and memory units. As memory units, the persistence of the resistance state is termed memorization, usually called plasticity in bio-synapse. It is classified mainly into short-term plasticity (STP) and long-term potentiation (LTP). As a processing unit, the artificial synapse must possess many functionalities like potentiation, depression, paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP). [19] Among these properties, STDP modulation is considered the fundamental Hebbian learning protocol. [20][21][22][23] RS-based artificial synapses are prominent candidates for neuromorphic computing and signal processing for future artificial intelligence and machine learning development. [24][25][26] Large data processing is necessary for image recognition and speech recognition in the state-of-the-art von Neumann architecture. An ANN can break this bottleneck since it can perform in-memory computation. [27,28] One of the key features of the artificial synapse is the multi-memory states. [29,30] Both the short-term memory (STM) and long-term memory (LTM) states are essential for computation possibility. Most RS-based devices show a more stable high conductance state than a decaying state; the decaying rate defines the occurrence of STM and LTM in the artificial synapse. This kind of conductance decay occurs for many reasons, including relaxation of trap-filled states, recombination of oxygen vacancy with oxygen ions, and Artificial synapses are the basic building block of artificial neural networks capable of neuromorphic computing, which might overtake conventional digital computing in areas like artificial intelligence, deep learning, and in-memory computation. Neuromorphic computing with artificial synapses opens a new window for fast in-memory computing and image processing. In this paper, an MoO x -based artificial synapse that mimics almost all characteristics of bio-synapses is demonstrated. The fabricated device shows excellent synaptic properties such as potentiation, depression, forgetting, paired-pulse facilit...

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Asymmetric GaN/ZnO Engineered Resistive Memory Device for Electronic Synapses

Cited by 16 publications

References 67 publications

Advancement in Soft Iontronic Resistive Memory Devices and Their Application for Neuromorphic Computing

Advancement in Soft Iontronic Resistive Memory Devices and Their Application for Neuromorphic Computing

Binder-Free Synthesis of Nanostructured Amorphous Cobalt Phosphate for Resistive Memory and Artificial Synaptic Device Applications

Observation of Magnéli Phase Filament Formation in MoO_x Artificial Synapse

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Asymmetric GaN/ZnO Engineered Resistive Memory Device for Electronic Synapses

Cited by 16 publications

References 67 publications

Advancement in Soft Iontronic Resistive Memory Devices and Their Application for Neuromorphic Computing

Advancement in Soft Iontronic Resistive Memory Devices and Their Application for Neuromorphic Computing

Binder-Free Synthesis of Nanostructured Amorphous Cobalt Phosphate for Resistive Memory and Artificial Synaptic Device Applications

Observation of Magnéli Phase Filament Formation in MoOx Artificial Synapse

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Observation of Magnéli Phase Filament Formation in MoO_x Artificial Synapse