Multi-Level Neuromorphic Devices Built on Emerging Ferroic Materials: A Review

Cheng, Wei; Agrawal, Amogh; Yu, Eunseon; Roy, Kaushik

doi:10.3389/fnins.2021.661667

Cited by 10 publications

(6 citation statements)

References 91 publications

(129 reference statements)

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“…Ferroelectric thin film materialis also a promising candidate dielectric layer for the neuromorphic OFET device because of its distinctive ferroelectric associated resistive switching effect and nonvolatile polarization [80][81][82][83][84][85]. Meanwhile, the unique polarization plasticity of ferroelectric material is similar to the brain-like synaptic function, and Poly(vinylidene fluoride) (PVDF) is the most used ferroelectric material in OFET devices, which possess excellent ferroelectricity and thermal stability and is suitable for neuromorphic OFET device [86].…”

Section: Ferroelectric Neuromorphic Ofet Devicementioning

confidence: 99%

Recent advances in fabrication and functions of neuromorphic system based on organic field effect transistor

Liu,

Lian,

Chen

et al. 2024

Int. J. Extrem. Manuf.

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The development of various artificial electronics and machines would explosive increase the information and data, which need to be processed in-situ remediation. Bioinspired synapse devices can store and process signals in a parallel way, then improve fault tolerance and decrease the power consumption of artificial systems. The organic field effect transistor (OFET) is a promising component for bioinspired neuromorphic systems because it is suitable for large-scale integrated circuits and flexible devices. In this review, the organic semiconductor materials, structures and fabrication, and different artificial sensory perception systems functions based on micro-sized neuromorphic OFET devices are summarized. Finally, a summary and challenges of neuromorphic OFET devices are provided. This review presents a detailed introduction to the recent progress of neuromorphic OFET devices from semiconductor materials to perception systems, which would provide a reference for the achievement of neuromorphic systems in future bioinspired electronics.

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Section: Ferroelectric Neuromorphic Ofet Devicementioning

confidence: 99%

Recent advances in fabrication and functions of neuromorphic system based on organic field effect transistor

Liu,

Lian,

Chen

et al. 2024

Int. J. Extrem. Manuf.

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“…[24][25][26] Memristive materials (we term them MMs) are promising candidates for achieving in-memory computing. [27][28][29][30][31] Upon the application of an external electrical stimulus, the MM system discloses programmable conductance states. [32][33][34] The prototypical In-memory computing and in-sensor computation using MM systems.…”

Section: Introductionmentioning

confidence: 99%

“…Memristive materials (we term them MMs) are promising candidates for achieving in‐memory computing. [ 27–31 ] Upon the application of an external electrical stimulus, the MM system discloses programmable conductance states. [ 32–34 ] The prototypical MM operations, enabled by the phase‐change, i.e., thermally induced crystalline–amorphous transitions, tunnel magnetoresistance, viz., spin‐dependent tunnel conductance, and electrochemical reaction, e.g., redox and ion migration, are based on the switching of a dielectric layer in a two‐terminal metal–dielectric–metal configuration.…”

Section: Introductionmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

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Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.

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“…Despite remarkable progress in nanotechnology, a lot of work is required for the hardware-level implementation of artificial neural networks (ANNs) . To date, numerous electronic devices with resistive switching memory, , phase change memory, , and ferroelectric memory − have been developed to mimic the synaptic functionality of the human brain. Two-terminal devices, such as memristor-based artificial synapses, have a simple structure, low power consumption, and large device density .…”

Section: Introductionmentioning

confidence: 99%

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

Gupta

Kim

Park

et al. 2023

ACS Appl. Mater. Interfaces

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Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼10 3 ) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.

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Multi-Level Neuromorphic Devices Built on Emerging Ferroic Materials: A Review

Cited by 10 publications

References 91 publications

Recent advances in fabrication and functions of neuromorphic system based on organic field effect transistor

Recent advances in fabrication and functions of neuromorphic system based on organic field effect transistor

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

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