Recent progress in memristor-based gas sensor (Gasistor; gas sensor + memristor): Device modeling, mechanisms, performance, and prospects DOI Creative Commons
Mohsin Ali, Doowon Lee, Ibtisam Ahmad

и другие.

Sensors and Actuators Reports, Год журнала: 2024, Номер unknown, С. 100269 - 100269

Опубликована: Дек. 1, 2024

Язык: Английский

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

Threshold-Switching Memristors for Neuromorphic Thermoreception DOI Creative Commons
Haotian Li, Chunsheng Jiang, Qilin Hua

и другие.

Sensors, Год журнала: 2025, Номер 25(5), С. 1533 - 1533

Опубликована: Март 1, 2025

Neuromorphic devices emulating the temperature-sensing capabilities of biological thermoreceptors hold significant promise for neuron-like artificial sensory systems. In this work, Bi2Se3-based threshold-switching memristors were presented in constructing neuron circuits, leveraging its exceptional attributes, such as high switching ratio (>106), low threshold voltage, and thermoelectric response. The spiking oscillation response to resistance temperature variations was analyzed using Hspice simulation memristor model based on on/off states, voltage (Vth), (Vhold). These results show great potential enabling biorealistic thermoreception applications.

Язык: Английский

Процитировано

0

Exploring resistive switching in flexible, forming-free Ti/NiO/AZO/PET memory device for future wearable electronics DOI Creative Commons

Adiba Adiba,

Ph. Nonglen Meitei, Tufail Ahmad

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 10, 2025

Resistive Random Access Memory (ReRAM) is an emerging class of non-volatile memory that stores data by altering the resistance a material within cell. Unlike traditional technologies, ReRAM operates using voltage to induce change in metal oxide layer, which can then be read as binary state (0 or 1). In this work, we present flexible, forming-free, device aluminium-doped zinc (AZO) electrode and nickel (NiO) active layer. The fabricated Ti/NiO/AZO/PET demonstrates reliable bipolar resistive switching (BRS) with two distinct stable states, crucial for neuromorphic computing. Electrical tests showed high low states set (VSET) ≈ 5.4 V reset (VRESET) 2.9 V, endurance over 400 cycles retention around 10³ seconds. Different conduction mechanisms were observed (HRS) (LRS) like ohmic space charge limited current (SCLC). characterization under bending conditions demonstrated device's performance reliability, minimal variation VSET VRESET values. These results highlight potential NiO/AZO-based flexible high-density storage wearable electronics applications.

Язык: Английский

Процитировано

0

Electric Eels Inspired Iontronic Artificial Skin with Multimodal Perception and In‐Sensor Reservoir Computing DOI
Haiqing Pei,

Huiqian Hu,

Yu Dong

и другие.

Advanced Functional Materials, Год журнала: 2025, Номер unknown

Опубликована: Апрель 18, 2025

Abstract As the largest sensory organ, human skin generates ionic signals in response to tactile, thermal, and electrical stimuli, which are then transmitted neurons processed by brain, thereby enabling sensing memory, ultimately promoting conscious perception decision‐making. However, existing artificial skins face significant challenges including inability achieve multimodal memory simultaneously (i.e., stimuli), difficulty detecting ultra‐low currents, limitations rich synaptic behaviors that essential for highly efficient in‐sensor reservoir computing. Inspired electric eels, study here develops an based on iontronic p‐n junctions consisting of PolyAT PolyES bi‐layered structures. This features broad detection ranges temperature (−80 120 °C, well beyond reach hydrogel counterparties), pressure (0.075 Pa 400 kPa, among highest sensitivities ever reported), current (1–200 nA), meanwhile demonstrates functions. Additionally, incorporating a robotic hand can grasp objects with different temperatures weights demand. Further, fully memristive computing is implemented skin, allowing sensing, decoding, learning via stimulation, achieving 91.3% accuracy classifying MNIST handwritten digit images.

Язык: Английский

Процитировано

0

Recent progress in memristor-based gas sensor (Gasistor; gas sensor + memristor): Device modeling, mechanisms, performance, and prospects DOI Creative Commons
Mohsin Ali, Doowon Lee, Ibtisam Ahmad

и другие.

Sensors and Actuators Reports, Год журнала: 2024, Номер unknown, С. 100269 - 100269

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

3