Optoelectronic Perovskite Synapses for Neuromorphic Computing

Ma, Fumin; Zhu, Yuan; Xu, Zhiai; Liu, Yang; Zheng, X. H.; Ju, Songman; Li, Qianqian; Ni, Ziquan; Hu, Hailong; Chai, Yang; Wu, Chaoxing; Kim, Tae Whan; Li, Fushan

doi:10.1002/adfm.201908901

Cited by 153 publications

(129 citation statements)

References 52 publications

(72 reference statements)

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“…While ion migration can be detrimental in the case of solar cells and light-emitting diodes, the mixed ionic-electronic nature of hybrid perovskites opens up possibilities for other emerging applications, such as in resistive switches and batteries (Tress, 2017;Zhao et al, 2019;Tress, 2017;Zhao et al, 2019). In particular, ion migration enable switching devices from a high resistive state (HRS) to a low resistive state (LRS; Figures 2A-D) (Zhu et al, 2017;Ma et al, 2020). These resistive switches enable the fabrication of 'memristors', which allows for simultaneous storage and processing of information with low energy consumption and high computing power, similar to the human brain.…”

Section: Utilizationmentioning

confidence: 99%

Mixed Conductivity of Hybrid Halide Perovskites: Emerging Opportunities and Challenges

Futscher

Milić

2021

Front. Energy Res.

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Hybrid halide perovskites feature mixed ionic-electronic conductivities that are enhanced under device operating conditions. This has been extensively investigated over the past years by a wide range of techniques. In particular, the suppression of ionic motion by means of material and device engineering has been of increasing interest, such as through compositional engineering, using molecular modulators as passivation agents, and low-dimensional perovskite materials in conjunction with alternative device architectures to increase the stabilities under ambient and operating conditions of voltage bias and light. While this remains an ongoing challenge for photovoltaics and light-emitting diodes, mixed conductivities offer opportunities for hybrid perovskites to be used in other technologies, such as rechargeable batteries and resistive switches for neuromorphic memory elements. This article provides an overview of the recent developments with a perspective on the emerging utility in the future.

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Section: Utilizationmentioning

confidence: 99%

Mixed Conductivity of Hybrid Halide Perovskites: Emerging Opportunities and Challenges

Futscher

Milić

2021

Front. Energy Res.

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“…In doing so, this RRAM device has the capacity to mimic ionic mechanisms in synapses faithfully that enables efficient learning and data processing. [18][19][20][21][22][23][24] We also present an empirically-driven conduction-based model of our device.…”

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confidence: 99%

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Lin

Chen

et al. 2020

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The biological brain has set a golden standard in computational efficiency, both due to its massive parallelism, and its ability to perform in-memory computing within the same ionic substrate. Ion-gated channels determine synaptic strength which is known to be a mechanism for memory storage, and the very same ions that pass through these gates encode data in the form of spikes. Communication, computation, and storage all occur within the same local medium. The brain's ability to perform in-memory processing within a unified ionic mechanism has driven many researchers to apply ion-driven non-volatile memories to emulate learning rules at the device-level. [1-12] The operating principles of resistive random access memory (RRAM) draw parallel with biological synapses. From a physical standpoint, the top electrode (TE) corresponds to a pre-synaptic terminal, the insulator layer acts as the substrate through which neurotransmitters are released, and the bottom electrode (BE) Biologically plausible computing systems require fine-grain tuning of analog synaptic characteristics. In this study, lithium-doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state-dependent decay to be reliably achieved. As a result, this device offers multi-bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short-term memory and long-term memory are emulated across dynamical timescales. Spike-timing-dependent plasticity and paired-pulse facilitation are also demonstrated. These mechanisms are capable of self-pruning to generate efficient neural networks. Time-dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.

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“…Kim et al reported an artificial synapse on the basis of the dual‐phase (Cs 3 Bi 2 I 9 ) 0.4 −(CsPbI 3 ) 0.6 perovskite memristor, [ 109 ] which could also show EPSC, IPSC, STP, STD, LTP, LTD, STDP, and spike‐width‐dependent plasticity (SWDP) behaviors. Recently, Ma et al reported an artificial synapse on the basis of the CsPbBr 3 perovskite nanoplates, [ 126 ] which had both electronic and photonic synaptic plasticity. Versatile synaptic functions were successfully emulated, including PPF, STP, LTP, transition from short‐term to long‐term memory, and learning‐experience behavior.…”

Section: Halide Perovskite Memristor Applicationsmentioning

confidence: 99%

Recent Advances in Halide Perovskite Memristors: Materials, Structures, Mechanisms, and Applications

Xiao

Tang

et al. 2020

Adv Materials Technologies

120

121

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The memristor is the fourth fundamental circuit element discovered after resistors, capacitors, and inductors. Although this concept has only been proposed in 1971 and confirmed in 2008, many materials with memristive properties have been found since 1962. Halide perovskites have been widely used in solar cells, and recently it has been found that they also possess good memristive properties. Different halide perovskites have been applied to memristors, including 3D organic–inorganic hybrid perovskites, 2D organic–inorganic hybrid perovskites, all‐inorganic cesium/rubidium lead halide perovskites, lead‐less and lead‐free perovskites, and halide perovskite quantum dots. Flexible and fiber‐shaped halide perovskite memristors have been fabricated. Several resistive switching mechanisms of halide perovskite memristors have been proposed, and the relationships between halide perovskite memristors and perovskite solar cells have been discussed. Based on halide perovskite memristors, light‐induced resistive switching and logic gate, high‐density and cross‐bar array data storage unit, and artificial synapse have been designed. Herein, recent advances in halide perovskite memristors are comprehensively and systematically reviewed. Finally, the current challenges and potential future directions in this field are discussed.

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Optoelectronic Perovskite Synapses for Neuromorphic Computing

Cited by 153 publications

References 52 publications

Mixed Conductivity of Hybrid Halide Perovskites: Emerging Opportunities and Challenges

Mixed Conductivity of Hybrid Halide Perovskites: Emerging Opportunities and Challenges

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Recent Advances in Halide Perovskite Memristors: Materials, Structures, Mechanisms, and Applications

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