Evolution of Bio‐Inspired Artificial Synapses: Materials, Structures, and Mechanisms

Yu, Haiyang; Wei, Huanhuan; Gong, Jue; Han, Hong; Ma, Mingxue; Wang, Yongfei; Xu, Wentao

doi:10.1002/smll.202000041

Cited by 74 publications

(78 citation statements)

References 131 publications

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“…Among numerous functions performed by the synapses, some critical functions like fast response and information filtering are usually achieved by the STP, which rapidly fades away after several minutes or much shorter time. 15,46 PPF is one of the most important learning rules in STP, which is significant for transmitting information contained in the temporal pattern of a spike train. 47,48 In PPF, the synaptic current triggered by the second spike is higher than that evoked by the first one when the second spike follows the previous one subsequently.…”

Section: Resultsmentioning

confidence: 99%

“…[50][51][52] Compared with the fast synaptic response decay in STP, the synaptic efficiency in LTP can last longer than STP from hours to days, which is greatly complementary to STP in biological synaptic systems. 46,48,51 The LTP can be usually realized by increasing the duration and intensity of the stimulus to reach the threshold value or applying a strong train of stimuli. 48 The human memory is believed to be modulated by the dynamic changes in the strength of the synaptic connections, which is related to the high-order synaptic activities like metaplasticity and learning experience-dependent plasticity.…”

Section: Resultsmentioning

confidence: 99%

“…On the contrary, the anti-Hebbian learning rule presents depression for t Δ > 0 and potentiation for t Δ < 0, respectively. 46,54,58 Through designing a specific pulse scheme ( Figure S9C), Figure 5B presents a negative synaptic weight change for t Δ > 0 and vice versa, obeying the asymmetric anti-Hebbian learning rule. The STDP behavior can be quantified by plotting the synaptic weight variation ( W Δ ) as a function of t Δ , and it can be well fitted by the following equations: Figure 5B.…”

Section: Resultsmentioning

confidence: 99%

“…Compared with the fast synaptic response decay in STP, the synaptic efficiency in LTP can last longer than STP from hours to days, which is greatly complementary to STP in biological synaptic systems 46,48,51 . The LTP can be usually realized by increasing the duration and intensity of the stimulus to reach the threshold value or applying a strong train of stimuli 48 .…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Intrinsic polarization coupling in 2D α‐In₂Se₃ toward artificial synapse with multimode operations

Gao

Zheng

et al. 2021

SmartMat

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Emulation of advanced synaptic functions of the human brain with electronic devices contributes an important step toward constructing high-efficiency neuromorphic systems. Ferroelectric materials are promising candidates as synaptic weight elements in neural network hardware due to their controllable polarization states. However, the increased depolarization field at the nanoscale and the complex fabrication process of the traditional ferroelectric materials hamper the development of high-density, low-power, and highly sensitive synaptic devices. Here, we report the implementation of twodimensional (2D) ferroelectric α-In 2 Se 3 as an active channel material to emulate typical synaptic functions. The α-In 2 Se 3-based synaptic device features multimode operations, enabled by the coupled ferroelectric polarization under various voltage pulses applied at both drain and gate terminals. Moreover, the energy consumption can be reduced to~1 pJ by using high-κ dielectric (Al 2 O 3). The successful control of ferroelectric polarizations in α-In 2 Se 3 and its application in artificial synapses are expected to inspire the implementation of 2D ferroelectric materials for future neuromorphic systems. K E Y W O R D S 2D ferroelectrics, artificial synapse, high-κ dielectric, multimode operations, α-In 2 Se 3 1 | INTRODUCTION Over the past few decades, computers based on the conventional von Neumann architecture have achieved great success in performing arithmetic operations, leading to the revolution of information technology. 1 However, the physically separated logic and memory blocks, known as von Neumann bottleneck, inevitably limit the computational efficiency and speed as well as the scalability of the architecture. 2,3 Inspired by human brain, neuromorphic

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Intrinsic polarization coupling in 2D α‐In₂Se₃ toward artificial synapse with multimode operations

Gao

Zheng

et al. 2021

SmartMat

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“…Resistive switching (RS), an electric property shown by metal‐insulator‐metal (MIM) structures, whose resistance can be repeatedly switched between different states when exposed to electrical stresses, has attracted intensive attention due to its potential application for reconfigurable logic circuits, neuromorphic computing, and most importantly, non‐volatile memory (NVM). [ 1–8 ] Among different types of memristive devices utilizing RS effects, [ 9,10 ] the metallic conducting filament (CF) based devices demonstrate outstanding properties, including nonvolatile memristive character, low power consumption, high ON/OFF ratio, and good data retention. [ 11–14 ] In general, a CF based RS device consists of an active electrode (AE) and an inert electrode (IE) clamping the insulating layer (i.e., MIM).…”

Section: Introductionmentioning

confidence: 99%

In Situ Resistive Switching Effect Scrutinization on Co‐Designed Graphene Sensor

Xiong

Wang

Bøjesen

et al. 2021

Small

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Resistive switching (RS), an electric property based on the forming and rupture of conductive filaments in metal‐insulator‐metal structures, has attracted intensive attention due to its potential application in next generation energy‐efficient and area‐efficient memory devices. In situ studies of the RS effect are urgently needed for its mechanism understanding and memristive performance improvement. Here investigations of both the RS effect as well as the gate tunable conductance quantization effect are realized by co‐designing an Ag/SiO2 based memory structure on a graphene local sensor. This design enables self‐monitoring of the working states of the memristor in real‐time by virtue of the graphene sensor. These findings pave the way for further investigations of on‐chip electronics and quantum physics.

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Non‐Volatile Electrolyte‐Gated Transistors Based on Graphdiyne/MoS₂ with Robust Stability for Low‐Power Neuromorphic Computing and Logic‐In‐Memory

Yao

Chen

et al. 2021

Adv Funct Materials

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Artificial synapses are the key building blocks for low-power neuromorphic computing that can go beyond the constraints of von Neumann architecture. In comparison with two-terminal memristors and three-terminal transistors with filament-formation and charge-trapping mechanisms, emerging electrolyte-gated transistors (EGTs) have been demonstrated as a promising candidate for neuromorphic applications due to their prominent analog switching performance. Here, a novel graphdiyne (GDY)/MoS 2 -based EGT is proposed, where an ion-storage layer (GDY) is adopted to EGTs for the first time. Benefitting from this Li-ion-storage layer, the GDY/MoS 2 -based EGT features a robust stability (variation < 1% for over 2000 cycles), an ultralow energy consumption (50 aJ µm −2 ), and long retention characteristics (>10 4 s). In addition, a quasi-linear conductance update with low noise (1.3%), an ultrahigh G max /G min ratio (10 3 ), and an ultralow readout conductance (<10 nS) have been demonstrated by this device, enabling the implementation of the neuromorphic computing with near-ideal accuracies. Moreover, the nonvolatile characteristics of the GDY/MoS 2 -based EGT enable it to demonstrate logic-in-memory functions, which can execute logic processing and store logic results in a single device. These results highlight the potential of the GDY/MoS 2 -based EGT for next-generation low-power electronics beyond von Neumann architecture.

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Evolution of Bio‐Inspired Artificial Synapses: Materials, Structures, and Mechanisms

Cited by 74 publications

References 131 publications

Intrinsic polarization coupling in 2D α‐In₂Se₃ toward artificial synapse with multimode operations

Intrinsic polarization coupling in 2D α‐In₂Se₃ toward artificial synapse with multimode operations

In Situ Resistive Switching Effect Scrutinization on Co‐Designed Graphene Sensor

Non‐Volatile Electrolyte‐Gated Transistors Based on Graphdiyne/MoS₂ with Robust Stability for Low‐Power Neuromorphic Computing and Logic‐In‐Memory

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Evolution of Bio‐Inspired Artificial Synapses: Materials, Structures, and Mechanisms

Cited by 74 publications

References 131 publications

Intrinsic polarization coupling in 2D α‐In2Se3 toward artificial synapse with multimode operations

Intrinsic polarization coupling in 2D α‐In2Se3 toward artificial synapse with multimode operations

In Situ Resistive Switching Effect Scrutinization on Co‐Designed Graphene Sensor

Non‐Volatile Electrolyte‐Gated Transistors Based on Graphdiyne/MoS2 with Robust Stability for Low‐Power Neuromorphic Computing and Logic‐In‐Memory

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Product

Resources

About

Intrinsic polarization coupling in 2D α‐In₂Se₃ toward artificial synapse with multimode operations

Intrinsic polarization coupling in 2D α‐In₂Se₃ toward artificial synapse with multimode operations

Non‐Volatile Electrolyte‐Gated Transistors Based on Graphdiyne/MoS₂ with Robust Stability for Low‐Power Neuromorphic Computing and Logic‐In‐Memory