Porous crystalline materials for memories and neuromorphic computing systems

Ding, Guanglong; Zhao, JiYu; Zhou, Kui; Zheng, Qi; Han, Su-Ting; Peng, Xiaojun; Zhou, Ye

doi:10.1039/d3cs00259d

Cited by 77 publications

(26 citation statements)

References 579 publications

(1,121 reference statements)

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“…Based on our current understanding of solid-phase electrosynthesis, suitable applications are considered to be neuromorphic electronics, information storage, electrocatalysis, etc. In particular, although many types of materials are involved in neuromorphic studies, − these metallopolymer monolayers are distinguished by their highly tunable function and batch-to-batch consistency of micrometer-sized devices with high yield. Sequence-controlled polymers as well as DNA are well-known for high-density information storage .…”

Section: Discussionmentioning

confidence: 99%

Solid-Phase Electrosynthesis

Li,

2023

Acc. Chem. Res.

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Section: Discussionmentioning

confidence: 99%

Solid-Phase Electrosynthesis

Li,

2023

Acc. Chem. Res.

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“…Memristor is an essential component in achieving energy-efficient operations in practical hardware neural networks that replicate the behavior of biological synapses. Memristors are known for their pinch hysteresis loops, a phenomenon observed across various materials such as binary oxides, complex perovskite oxides, solid dielectric materials, and organic polymer materials. − Typically, two-terminal memristors consist of a switching layer sandwiched between two metallic electrodes. By leveraging materials and interface engineering, it is possible to develop memristive devices with either nonvolatile or volatile characteristics.…”

Section: Introductionmentioning

confidence: 99%

Slow Migration-Controlled Resistive Switching in Stable Dion–Jacobson Hybrid Perovskites for Flexible Memristive Applications

Patel,

Gosai,

Lokhandwala

et al. 2024

ACS Appl. Electron. Mater.

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The limitations of Moore's law and the von Neumann bottleneck have sparked an increasing interest in advanced intelligent systems, such as memristors and neuromorphic devices. This work unveils the role of slow ion migration for resistive switching (RS) and exceptional environmental and mechanical resilience achieved with butane-1,4diammonium (BDA)-based BDAPbI 4 memristors, meticulously fabricated and measured in ambient conditions. These memristors demonstrate exceptional durability with consistent characteristics for up to 60 days and a slight decay in the ON/OFF ratio on the 140th day. Devices show the potential for flexible random-access memories with a low operating voltage of ∼100 mV and strong data retention and endurance measured up to 35 h and ∼1000 cycles, respectively. RS in these devices is attributed to energy barrier modulation at the perovskite/Ag interface and ion migration in the perovskite film. Furthermore, the initial investigations into their synaptic characteristics reveal stable learning behavior (potentiation and depression) and an invariant paired pulse facilitation (PPF), tested on flat and 5 mm bending radii. Additionally, the application of the spike time-dependent plasticity (STDP) Hebbian learning rule effectively demonstrates the feasibility of these memristors for neuromorphic computing applications. This is particularly promising for use in extreme mechanical conditions, such as electronic skins, and extends their potential beyond traditional data storage solutions.

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“…However, when the input stimulus is removed, these memristors gradually restore their resting conductance state . Memristors that have been employed as reservoir layers include perovskite halide, , porous crystalline, biomolecular, , and metal-oxide memristors, − to name a few. Unlike conventional reservoirs that employ recurrent spatial nodes to map the fed inputs to a higher-dimensional feature space, dynamic memristor-based reservoirs leverage the inherent short-term memory and nonlinear dynamics of the memristors.…”

Section: Introductionmentioning

confidence: 99%

Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity

Armendarez,

Mohamed,

Dhungel

et al. 2024

ACS Appl. Mater. Interfaces

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Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and shortterm memory dynamics, are excellent candidates as informationprocessing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10 −3 , using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.

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Porous crystalline materials for memories and neuromorphic computing systems

Abstract: This review highlights the film preparation methods and the application advances in memory and neuromorphic electronics of porous crystalline materials, involving MOFs, COFs, HOFs, and zeolites.

Cited by 77 publications

References 579 publications

Solid-Phase Electrosynthesis

Solid-Phase Electrosynthesis

Slow Migration-Controlled Resistive Switching in Stable Dion–Jacobson Hybrid Perovskites for Flexible Memristive Applications

Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity

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