BDAPbI4 Dion Jacobson hybrid perovskite-based artificial nociceptors on biodegradable substrate DOI

Manish Khemnani,

Parth Thakkar,

Aziz Lokhandvala

et al.

Sensors and Actuators A Physical, Journal Year: 2024, Volume and Issue: 373, P. 115382 - 115382

Published: April 16, 2024

Language: Английский

CogniFiber: Harnessing Biocompatible and Biodegradable 1D Collagen Nanofibers for Sustainable Nonvolatile Memory and Synaptic Learning Applications DOI

Kasturi A. Rokade,

Dhananjay D. Kumbhar, Snehal L. Patil

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(24)

Published: March 19, 2024

Here, resistive switching (RS) devices are fabricated using naturally abundant, nontoxic, biocompatible, and biodegradable biomaterials. For this purpose, 1D chitosan nanofibers (NFs), collagen NFs, chitosan-collagen NFs synthesized by an electrospinning technique. Among different the collagen-NFs-based device shows promising RS characteristics. In particular, optimized Ag/collagen NFs/fluorine-doped tin oxide a voltage-tunable analog memory behavior good nonvolatile properties. Moreover, it can also mimic various biological synaptic learning properties be used for pattern classification applications with help of spiking neural network. The time series analysis technique is employed to model predict variations device. have shown cytotoxicity anticancer properties, suggesting excellent biocompatibility as layer. explored NRK-52E (Normal Rat Kidney cell line) MCF-7 (Michigan Cancer Foundation-7 cancer line). Additionally, biodegradability evaluated through physical transient test. This work provides vital step toward developing biocompatible material sustainable neuromorphic computing applications.

Language: Английский

Citations

40

Nanoscale memristor devices: materials, fabrication, and artificial intelligence DOI
Yongchao Yu, Ming Xiao, David Fieser

et al.

Journal of Materials Chemistry C, Journal Year: 2024, Volume and Issue: 12(11), P. 3770 - 3810

Published: Jan. 1, 2024

An overview of fabrication methods, including CMOS, nanojoining, and 3D printing techniques, materials, structures, properties, mechanisms, applications memristors, as well the most recent advancements in molecular is provided.

Language: Английский

Citations

20

Forming-less flexible memristor crossbar array for neuromorphic computing applications produced using low-temperature atomic layer deposition DOI
Minjae Kim, Dong‐Eun Kim, Yue Wang

et al.

Applied Materials Today, Journal Year: 2024, Volume and Issue: 38, P. 102204 - 102204

Published: April 25, 2024

Language: Английский

Citations

11

A High‐Stability Pressure‐Sensitive Implantable Memristor for Pulmonary Hypertension Monitoring DOI
Zelin Cao, Yiwei Liu, Bai Sun

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 12, 2024

Pulmonary hypertension (PH) significantly affects the quality of life and lifespan humans has promoted development flexible implantable electronic devices for PH diagnosis prevention. Traditional based on von Neumann architecture face insurmountable challenges in processing large amounts biological data due to computational bottlenecks. Memristors, with integrated in-memory sensing computing capabilities, can effectively eliminate bottlenecks become one most promising products health monitoring. Here, a memristor Ag/MnO

Language: Английский

Citations

11

Brain-inspired computing systems: a systematic literature review DOI Creative Commons

Mohamadreza Zolfagharinejad,

Unai Alegre-Ibarra, Tao Chen

et al.

The European Physical Journal B, Journal Year: 2024, Volume and Issue: 97(6)

Published: June 1, 2024

Abstract Brain-inspired computing is a growing and interdisciplinary area of research that investigates how the computational principles biological brain can be translated into hardware design to achieve improved energy efficiency. encompasses various subfields, including neuromorphic in-memory computing, have been shown outperform traditional digital in executing specific tasks. With rising demand for more powerful yet energy-efficient large-scale artificial neural networks , brain-inspired emerging as promising solution enabling expanding AI edge. However, vast scope field has made it challenging compare assess effectiveness solutions compared state-of-the-art counterparts. This systematic literature review provides comprehensive overview latest advances hardware. To ensure accessibility researchers from diverse backgrounds, we begin by introducing key concepts pointing out respective in-depth topical reviews. We continue with categorizing dominant platforms. highlight studies potential applications could greatly benefit systems their reported accuracy. Finally, fair comparison performance different approaches, employ standardized normalization approach efficiency reports literature. Graphical abstract

Language: Английский

Citations

9

Nitrogen-induced filament confinement strategy for implementing reliable resistive switching performance in a-HfOx memristors DOI
Yuanyuan Zhu, Yufei Zhang,

Shuning Yang

et al.

Applied Physics Letters, Journal Year: 2025, Volume and Issue: 126(1)

Published: Jan. 6, 2025

Hafnium oxide (HfOx) films are highly valued as functional layers in nonvolatile resistive switching (RS) memristors due to their scalability, compatibility with CMOS technology, and high dielectric constant. However, the low reliability of HfOx-based is key factor hindering widespread practical applications. Herein, amorphous HfOx (a-HfOx) used construct memristors, nitrogen treatment strategy employed enhance characteristics. All fabricated Al/a-HfOx/ITO demonstrate bipolar digital RS behaviors, specifically, 500 °C-treated a-HfOx device exhibits reliable performance, including cycle-to-cycle variability, concentrated distributions operating voltages, long-term retention capacity (>104 s), good cycle endurance (>200 cycles). The mechanisms physical models for enhanced performance thoroughly elucidated, revealing that formation stable oxygen vacancy–dinitrogen complexes confines conductive filament path significantly reduces randomness during rupture. This work renders an effective material engineering widening a toward designing data storage devices striking performances.

Language: Английский

Citations

1

An innovative biomimetic technology: Memristors mimic human sensation DOI
Kun Wang,

Mengna Wang,

Bai Sun

et al.

Nano Energy, Journal Year: 2025, Volume and Issue: unknown, P. 110698 - 110698

Published: Jan. 1, 2025

Language: Английский

Citations

1

Flexible Memristors for Implantable Applications DOI
Wei Lin, Bai Sun, Shuangsuo Mao

et al.

ACS Applied Nano Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

Language: Английский

Citations

1

A reversible implantable memristor for health monitoring applications DOI Creative Commons
Zelin Cao,

Linbiao Xiang,

Bai Sun

et al.

Materials Today Bio, Journal Year: 2024, Volume and Issue: 26, P. 101096 - 101096

Published: May 20, 2024

Conventional implantable electronics based on von Neumann architectures encounter significant limitations in computing and processing vast biological information due to computational bottlenecks. The memristor with integrated memory-computing low power consumption offer a promising solution overcome the bottleneck Moore's law of traditional silicon-based devices, making them most candidates for next-generation devices. In this work, highly stable an Ag/BaTiO3/MnO2/FTO structure was fabricated, demonstrating retention characteristics exceeding 1200 cycles endurance above 1000 s. device successfully exhibited three-stage responses signals after implantation SD (Sprague-Dawley) rats. Importantly, perform remarkable reversibility, maintaining over 100 repetition even extraction from rat. This study provides new perspective biomedical application memristors, expanding potential memristive devices intelligent medical fields such as health monitoring auxiliary diagnostics.

Language: Английский

Citations

7

Multilevel resistive switching in hydrothermally synthesized FeWO4 thin film-based memristive device for non-volatile memory application DOI

Amitkumar R. Patil,

Tukaram D. Dongale,

Rupesh S. Pedanekar

et al.

Journal of Colloid and Interface Science, Journal Year: 2024, Volume and Issue: 669, P. 444 - 457

Published: May 5, 2024

Language: Английский

Citations

6