In-sensor computing using Ti3C2Tx MXene memristor crossbar arrays for wearable electronics DOI
Jeny Gosai, Mansi Patel,

Anjalee Gosai

и другие.

Flexible and Printed Electronics, Год журнала: 2024, Номер 9(4), С. 045013 - 045013

Опубликована: Дек. 1, 2024

Abstract The potential of memristor systems in sensing, storing, and processing signals make them highly efficient ideal for power-efficient, comfortable wearable in-sensor computing applications. In this work, we demonstrate a 3 × crossbar array based on Ti C 2 T x MXene with non-volatile characteristics, exhibiting an ON/OFF ratio ∼10 . This -based also showcases remarkable synaptic properties. Additionally, achieve near perfect accuracy pattern training after just 9 epochs as well retaining ability even 24 h. A notable feature these arrays is their to integrate storage, capabilities, demonstrated real-time muscle monitoring healthcare device. multi-channel surface electromyography data was recorded using the MXene-based track forearm movements during series distinct hand gestures. These findings open up exciting possibilities development adaptable flexible memristive arrays, which hold great promise advanced neuromorphic computing,

Язык: Английский

Physical reservoir computing-based online learning of HfSiOx ferroelectric tunnel junction devices for image identification DOI
Seungjun Lee, Gaoyun An,

Gimun Kim

и другие.

Applied Surface Science, Год журнала: 2025, Номер unknown, С. 162459 - 162459

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
An innovative biomimetic technology: Memristors mimic human sensation DOI
Kun Wang,

Mengna Wang,

Bai Sun

и другие.

Nano Energy, Год журнала: 2025, Номер unknown, С. 110698 - 110698

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Lead-Free Halide Perovskite-Based Flexible Memristor for an Artificial Mechano-nociceptive System DOI
Yuchan Wang, Qian Ran, Ting Chen

и другие.

The Journal of Physical Chemistry Letters, Год журнала: 2025, Номер unknown, С. 3177 - 3184

Опубликована: Март 20, 2025

Herein, novel lead-free Cs3Bi2I9 nanocrystals (NCs) were preferred through first-principles calculations and crystal orbital Hamilton population (COHP). An artificial nociceptor was designed using the halide perovskite (HP) NCs doped into poly(methyl methacrylate) (PMMA). The resulting composite material memristor demonstrated remarkable resistive switching performance conductive atomic force microscopy (C-AFM). PMMA&Cs3Bi2I9-based memristors show an ultrafast speed of 30 ns low threshold voltage ≈0.6 V with little variation, which attributed to synergistic effect active metal electrodes vacancy filaments. Impressively, high mechanical bending stability (bending times = 1000) still exhibit excellent resistance state (RS) properties multilevel storage after days exposed ambient conditions. More importantly, fundamental nociceptive functions fully demonstrated. Furthermore, a mechano-nociceptor system simulate mechanism biological pain perception, could selectively react mild harmful stimuli. Our study provides new strategies for developing efficient neuromorphic materials devices.

Язык: Английский

Процитировано

0
Deciphering Mechanisms of Oxygen Vacancy Conducting Channel-Based LiSiOx Artificial Nociceptors DOI

Z.Y. Li,

Yao Shi,

Dianyou Song

и другие.

Vacuum, Год журнала: 2025, Номер unknown, С. 114296 - 114296

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Recent Developments on Novel 2D Materials for Emerging Neuromorphic Computing Devices DOI Creative Commons

Muhammad Hamza Pervez,

Ehsan Elahi,

Muhammad Asghar Khan

и другие.

Small Structures, Год журнала: 2024, Номер unknown

Опубликована: Окт. 8, 2024

The rapid advancement of artificial intelligent and information technology has led to a critical need for extremely low power consumption excellent efficiency. capacity neuromorphic computing handle large amounts data with garnered lot interest during the last few decades. For applications, 2D layered semiconductor materials have shown pivotal role due their distinctive properties. This comprehensive review provides an extensive study recent advancements in materials‐based devices especially multiterminal synaptic devices, two‐terminal neuronal integration devices. Herein, wide range potential applications memory, computation, adaptation, intelligence is incorporated. Finally, limitations challenges based on novel are discussed. Thus, this aims illuminate design fabrication van der Waals (vdW) heterostructure materials, leveraging promising engineering techniques excel hardware implementations.

Язык: Английский

Процитировано

2
A zinc oxide-based threshold switching memristor for simulating synaptic plasticity and artificial nociceptor DOI
Xiaoqi Li, Jianbo Jiang, Guangyu Liu

и другие.

Journal of Materials Science Materials in Electronics, Год журнала: 2024, Номер 35(24)

Опубликована: Авг. 1, 2024

Язык: Английский

Процитировано

1
In-sensor computing using Ti3C2Tx MXene memristor crossbar arrays for wearable electronics DOI
Jeny Gosai, Mansi Patel,

Anjalee Gosai

и другие.

Flexible and Printed Electronics, Год журнала: 2024, Номер 9(4), С. 045013 - 045013

Опубликована: Дек. 1, 2024

Abstract The potential of memristor systems in sensing, storing, and processing signals make them highly efficient ideal for power-efficient, comfortable wearable in-sensor computing applications. In this work, we demonstrate a 3 × crossbar array based on Ti C 2 T x MXene with non-volatile characteristics, exhibiting an ON/OFF ratio ∼10 . This -based also showcases remarkable synaptic properties. Additionally, achieve near perfect accuracy pattern training after just 9 epochs as well retaining ability even 24 h. A notable feature these arrays is their to integrate storage, capabilities, demonstrated real-time muscle monitoring healthcare device. multi-channel surface electromyography data was recorded using the MXene-based track forearm movements during series distinct hand gestures. These findings open up exciting possibilities development adaptable flexible memristive arrays, which hold great promise advanced neuromorphic computing,

Язык: Английский

Процитировано

0