Porous crystalline materials for memories and neuromorphic computing systems

Chemical Society Reviews, Год журнала: 2023, Номер 52(20), С. 7071 - 7136

Опубликована: Янв. 1, 2023

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

Язык: Английский

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Memristor-Based Artificial Chips

ACS Nano, Год журнала: 2023, Номер 18(1), С. 14 - 27

Опубликована: Дек. 28, 2023

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled a physically integrated core processing unit (CPU) and memory even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing show efficient ability parallel computation high information storage. These advantages position them as potential candidates for future data-centric requirements add remarkable vigor to research next-generation artificial intelligence (AI) systems, particularly those involve brain-like applications. This work provides an overview evolution memristor-based devices, from their initial use in creating synapses neural networks application developing advanced AI systems chips. It offers broad perspective key device primitives enabling special applications view materials, nanostructure, mechanism models. We highlight these demonstrations have field AI, point out existing challenges nanodevices toward chips, propose guiding principle outlook promotion system optimization biomedical field.

Язык: Английский

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A Human‐Computer Interaction Strategy for An FPGA Platform Boosted Integrated “Perception‐Memory” System Based on Electronic Tattoos and Memristors

Advanced Science, Год журнала: 2024, Номер 11(39)

Опубликована: Июль 24, 2024

The integrated "perception-memory" system is receiving increasing attention due to its crucial applications in humanoid robots, as well the simulation of human retina and brain. Here, a Field Programmable Gate Array (FPGA) platform-boosted that enables sensing, recognition, memory for human-computer interaction reported by combination ultra-thin Ag/Al/Paster-based electronic tattoos (AAP) Tantalum Oxide/Indium Gallium Zinc Oxide (Ta

Язык: Английский

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An artificial visual neuron with multiplexed rate and time-to-first-spike coding

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Май 1, 2024

Abstract Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial use in spiking neural network (SNN). However, lack multiplexed data coding schemes reduces ability SNN to emulate perception systems. Here, we present an neuron that enables rate temporal fusion (RTF) external information. can code information at different frequencies (rate coding) precise time-to-first-spike (TTFS) coding. This sensory scheme could improve computing capability efficacy neurons. A hardware-based with RTF exhibits good consistency real-world ground truth achieves highly accurate steering speed predictions self-driving vehicles complex conditions. demonstrates feasibility developing efficient spike-based neuromorphic hardware.

Язык: Английский

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Nanomaterials for Flexible Neuromorphics

Chemical Reviews, Год журнала: 2024, Номер 124(22), С. 12738 - 12843

Опубликована: Ноя. 5, 2024

The quest to imbue machines with intelligence akin that of humans, through the development adaptable neuromorphic devices and creation artificial neural systems, has long stood as a pivotal goal in both scientific inquiry industrial advancement. Recent advancements flexible electronics primarily rely on nanomaterials polymers owing their inherent uniformity, superior mechanical electrical capabilities, versatile functionalities. However, this field is still its nascent stage, necessitating continuous efforts materials innovation device/system design. Therefore, it imperative conduct an extensive comprehensive analysis summarize current progress. This review highlights applications neuromorphics, involving inorganic (zero-/one-/two-dimensional, heterostructure), carbon-based such carbon nanotubes (CNTs) graphene, polymers. Additionally, comparison summary structural compositions, design strategies, key performance, significant these are provided. Furthermore, challenges future directions pertaining materials/devices/systems associated neuromorphics also addressed. aim shed light rapidly growing attract experts from diverse disciplines (e.g., electronics, science, neurobiology), foster further for accelerated development.

Язык: Английский

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Two-Dimensional Materials for Brain-Inspired Computing Hardware

Chemical Reviews, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security healthcare. However, the current strategy implementing artificial intelligence algorithms using conventional silicon hardware is leading unsustainable energy consumption. Neuromorphic based on electronic devices mimicking biological systems emerging as low-energy alternative, although further progress requires materials that can mimic function while maintaining scalability and speed. As result their diverse unique properties, atomically thin two-dimensional (2D) are promising building blocks for next-generation electronics including nonvolatile memory, in-memory neuromorphic computing, flexible edge-computing systems. Furthermore, 2D achieve biorealistic synaptic neuronal responses extend beyond logic memory Here, we provide comprehensive review growth, fabrication, integration van der Waals heterojunctions optoelectronic devices, circuits, For each case, relationship between physical properties device emphasized followed by critical comparison technologies different applications. We conclude with forward-looking perspective key remaining challenges opportunities applications leverage fundamental heterojunctions.

Язык: Английский

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Hardware Implementation of Network Connectivity Relationships Using 2D hBN‐Based Artificial Neuron and Synaptic Devices

Advanced Functional Materials, Год журнала: 2023, Номер 34(10)

Опубликована: Ноя. 5, 2023

Abstract Brain‐inspired neuromorphic computing has been developed as a potential candidate for solving the von Neumann bottleneck of traditional systems. 2D materials‐based memristors have exponentially investigated promising building blocks because their excellent electrical performance, simple structure, and small device scale. However, while many researchers focused on looking into individual artificial devices based memristors, only few studies integration neuron synaptic reported. In this work, both volatile nonvolatile are fabricated by using hexagonal boron nitride film devices, respectively. The leaky‐integrate‐and‐fire performance functions (e.g., weight plasticity spike‐timing‐dependent plasticity) well emulated with devices. MNIST image classification is conducted experimental data. For first time, an neuron‐synapse‐neuron neural network physically constructed to mimic biological networks. connection strength modulation experimentally demonstrated between neurons depending conductance state synapse, paving way development large‐scale hardware.

Язык: Английский

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A modular organic neuromorphic spiking circuit for retina-inspired sensory coding and neurotransmitter-mediated neural pathways

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Апрель 3, 2024

Abstract Signal communication mechanisms within the human body rely on transmission and modulation of action potentials. Replicating interdependent functions receptors, neurons synapses with organic artificial biohybrid is an essential first step towards merging neuromorphic circuits biological systems, crucial for computing at interface. However, most systems are based simple which exhibit limited adaptability to both external internal cues, restricted emulate only specific individual neuron/synapse. Here, we present a modular system combines spiking replicate neural pathway. The neuron mimics sensory coding function afferent from light stimuli, while neuromodulatory activity interneurons emulated by neurotransmitters-mediated synapses. Combining these functions, create connection between multiple establish pre-processing retinal pathway primitive.

Язык: Английский

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Deep Learning Framework for Advanced De-Identification of Protected Health Information

Future Internet, Год журнала: 2025, Номер 17(1), С. 47 - 47

Опубликована: Янв. 20, 2025

Electronic health records (EHRs) are widely used in healthcare institutions worldwide, containing vast amounts of unstructured textual data. However, the sensitive nature Protected Health Information (PHI) embedded within these presents significant privacy challenges, necessitating robust de-identification techniques. This paper introduces a novel approach, leveraging Bi-LSTM-CRF model to achieve accurate and reliable PHI de-identification, using i2b2 dataset sourced from Harvard University. Unlike prior studies that often unify Bi-LSTM CRF layers, our approach focuses on individual design, optimization, hyperparameter tuning both components, allowing for precise performance improvements. rigorous architectural design tuning, underexplored existing literature, significantly enhances model’s capacity tag detection while preserving essential clinical context. Comprehensive evaluations conducted across 23 categories, as defined by HIPAA, ensuring thorough security critical domains. The optimized achieves exceptional metrics, with precision 99%, recall 98%, F1-score underscoring its effectiveness balancing precision. By enabling medical records, this research strengthens patient confidentiality, promotes compliance regulations, facilitates safe data sharing analysis.

Язык: Английский

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A Neural Device Inspired by Neuronal Oscillatory Activity with Intrinsic Perception and Decision‐Making

Advanced Science, Год журнала: 2025, Номер unknown

Опубликована: Фев. 4, 2025

Abstract Bionic neural devices often feature complex structures with multiple interfaces, requiring extensive post‐processing. In this paper, a device intrinsic perception and decision‐making (NDIPD), inspired by neuronal oscillatory activity is introduced. The utilizes alternating signals generated coupling the human body power‐frequency electromagnetic field as both signal source energy source, mimicking activity. peaks valleys of are differentially modulated to replicate baseline shift process in By comparing amplitude NDIPD's electrical output signal, achieves regarding location mechanical stimulation. This accomplished using single interface, which reduces data transmission, simplifies functionality, eliminates need for an external power supply. NDIPD demonstrates low‐pressure detection limit (<0.02 N), fast response time (<20 ms), exceptional stability (>200 000 cycles). It shows great potential applications such game control, UAV navigation, virtual vehicle driving. innovative supply method sensing mechanism expected open new avenues development bionic devices.

Язык: Английский

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