Probing switching mechanism of memristor for neuromorphic computing

Yang, Zhe; Zhang, Zirui; Li, Ce; Yang, Dongliang; Hui, Fei; Sun, Linfeng

doi:10.1088/2632-959x/acd70c

Cited by 4 publications

(1 citation statement)

References 142 publications

(158 reference statements)

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“…Memristive devices with resistive switching features have gathered tremendous consideration owing to their attractive competency in emerging applications such as evolving semiconducting materials with memories, logic circuits, and neuromorphic applications [1][2][3][4][5]. In addition, memristive devices hold simple device structures, are compatible with complementary metal oxide semiconductor (CMOS) technologies, and exhibit an exceptional capacity for next-generation volatile and nonvolatile memories owing to their high storage densities, fast read/write operations, reasonable durability, and retention time [6][7][8][9]. Various engineered materials have recently been reported as the switching layer in memristive devices.…”

Section: Introductionmentioning

confidence: 99%

A stable undoped low-voltage memristor cell based on Titania (TiOx)

Ullah,

Tarkhan,

Fredj

et al. 2023

Nano Ex.

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An asymmetric memristive device fabricated with a titania (TiOx)-based switching layer deposited through atomic layer deposition with a thickness of ∼37 nm was investigated. X-ray photoelectron spectroscopy and high-resolution transmission electron microscopy coupled with energy-dispersive x-ray spectroscopy were employed for device structural characterization. A unipolar resistive switching behavior (both at positive and negative voltages) was observed through the memristor’s current–voltage characteristics. A remarkably smaller forming voltage (from the top Pt electrode to the grounded Au electrode) of 0.46 V was achieved, while it approached (positive bias from the Au electrode and holding Pt electrode as grounded) 0.25 V, which is a much smaller forming voltage than has ever been reported for titanium-based oxides without doping. The retention and endurance characterization over 2000 switching cycles were satisfactory without degradation.

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Section: Introductionmentioning

confidence: 99%

A stable undoped low-voltage memristor cell based on Titania (TiOx)

Ullah,

Tarkhan,

Fredj

et al. 2023

Nano Ex.

View full text Add to dashboard Cite

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Neurotransmitter‐Mediated Plasticity in 2D Perovskite Memristor for Reinforcement Learning

Wang,

Chen,

Cheng

et al. 2023

Adv Funct Materials

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The neuromorphic computing architecture is a promising artificial intelligence for implementing hierarchical processing, in‐memory computing, event‐driven operation and functional specialization in computing systems. However, current investigations mainly focus on unisensory processing without objective experience which is contrary to the flexible sensory learning capability in the human brain that can sense and process information according to the ever‐changing environment. For example, a dominant paradigm for reconfigurable bio‐learning features is the emotional experience. The neurotransmitter dopamine is released during arousal, influencing the vital brain functions involved in cognition, reward learning, movement and motivation. Here, the on‐demand configuration of a biorealistic synaptic connection based on a 2D CaTa2O7 (CTO) device is demonstrated that can be adaptively reconfigured for a reinforcement learning purpose by the light‐active resistive switching, which originated from the photon‐regulated metaplasticity. The low energy consumption of 12.4 fJ endows the reinforcement learning system with high power efficiency and reliability. Finally, in‐sensor computing with a CTO synapse is implemented with a filtering function to process digital data in a neuromorphic engineering manner. This work demonstrates the feasibility of 2D perovskite neuromorphic device with enhanced biological plausibility in the approaching post‐Moore era.

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