From fundamentals to frontiers: a review of memristor mechanisms, modeling and emerging applications

Thakkar, Parth; Gosai, Jeny; Gogoi, Himangshu Jyoti; Solanki, Ankur

doi:10.1039/d3tc03692h

Cited by 3 publications

(2 citation statements)

References 165 publications

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“…In artificial synaptic devices, memristors can assist in simulating the functionalities of biological synapses . Memristor performance is based on conductance measurements . The difference in synaptic weights between pre-synaptic and post-synaptic neurons determines a progressive change in conductance …”

Section: Introductionmentioning

confidence: 99%

“…10 Memristor performance is based on conductance measurements. 11 The difference in synaptic weights between pre-synaptic and post-synaptic neurons determines a progressive change in conductance. 12 Recently, research in the field of memristors is undergoing rapid advancements as scientists delve into novel materials, device architectures, and fabrication techniques to enhance performance and reliability.…”

Section: Introductionmentioning

confidence: 99%

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Control-Etched Ti₃C₂T_x MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation

Gosai,

Patel,

Liu

et al. 2024

ACS Appl. Mater. Interfaces

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Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in Ti3C2T x MXene. The presence of a small fraction of Al ions in partially etched MXene (p-Ti3C2T x ) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-Ti3C2T x )-based devices. Former devices exhibit excellent non-volatile data storage properties, with a robust ∼103 ON/OFF ratio, high endurance of ∼104 cycles, multilevel resistance states, and long data retention measured up to ∼106 s. High mechanical stability up to ∼73° bending angle and environmental robustness are confirmed with consistent switching characteristics under increasing temperature and humid conditions. Furthermore, a p-Ti3C2T x MXene memristor is employed to mimic the biological synapse by measuring the learning–forgetting pattern for ∼104 cycles as potentiation and depression. Spike time-dependent plasticity (STDP) based on Hebb’s Learning rules is also successfully demonstrated. Moreover, a remarkable accuracy of ∼95% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) data set with just 29 training epochs is achieved in simulation. Ultimately, our findings underscore the potential of MXene-based flexible memristor devices as versatile components for data storage and neuromorphic computing.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Control-Etched Ti₃C₂T_x MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation

Gosai,

Patel,

Liu

et al. 2024

ACS Appl. Mater. Interfaces

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Memristive oscillator to memristive map, energy characteristic

Guo,

Ma,

Zhang

et al. 2024

Sci. China Technol. Sci.

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BDAPbI4 Dion Jacobson hybrid perovskite-based artificial nociceptors on biodegradable substrate

Khemnani,

Thakkar,

Lokhandvala

et al. 2024

Sensors and Actuators A: Physical

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From fundamentals to frontiers: a review of memristor mechanisms, modeling and emerging applications

Abstract: The escalating demand for artificial intelligence (AI), the internet of things (IoTs), and energy-efficient high-volume data processing has brought the need for innovative solutions to the forefront.

Cited by 3 publications

References 165 publications

Control-Etched Ti₃C₂T_x MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation

Control-Etched Ti₃C₂T_x MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation

Memristive oscillator to memristive map, energy characteristic

BDAPbI4 Dion Jacobson hybrid perovskite-based artificial nociceptors on biodegradable substrate

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From fundamentals to frontiers: a review of memristor mechanisms, modeling and emerging applications

Abstract: The escalating demand for artificial intelligence (AI), the internet of things (IoTs), and energy-efficient high-volume data processing has brought the need for innovative solutions to the forefront.

Cited by 3 publications

References 165 publications

Control-Etched Ti3C2Tx MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation

Control-Etched Ti3C2Tx MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation

Memristive oscillator to memristive map, energy characteristic

BDAPbI4 Dion Jacobson hybrid perovskite-based artificial nociceptors on biodegradable substrate

Contact Info

Product

Resources

About

Control-Etched Ti₃C₂T_x MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation

Control-Etched Ti₃C₂T_x MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation