Results 141 to 150 of about 61,087 (328)

Organic Thin‐Film Transistors for Neuromorphic Computing

open access: yesAdvanced Electronic Materials, EarlyView.
Organic thin‐film transistors (OTFTs) are reviewed for neuromorphic computing applications, highlighting their power‐efficient, and biological time‐scale operation. This article surveys OFET and OECT devices, compares them with memristors and CMOS, analyzes how fabrication parameters shape spike‐based metrics, proposes standardized characterization ...
Luke McCarthy   +2 more
wiley   +1 more source

RRAM Variability Harvesting for CIM‐Integrated TRNG

open access: yesAdvanced Electronic Materials, EarlyView.
This work demonstrates a compute‐in‐memory‐compatible true random number generator that harvests intrinsic cycle‐to‐cycle variability from a 1T1R RRAM array. Parallel entropy extraction enables high‐throughput bit generation without dedicated circuits. This approach achieves NIST‐compliant randomness and low per‐bit energy, offering a scalable hardware
Ankit Bende   +4 more
wiley   +1 more source

Exfoliated‐MoS2 Gradual Resistive Switching Devices as Artificial Synapses

open access: yesAdvanced Electronic Materials, EarlyView.
A vertical memristor based on untreated, exfoliated MoS2 is presented, revealing gradual resistive switching governed by Schottky barrier modulation at the MoS2/metal interface from the trapping/detrapping of charges. Furthermore, the device emulates synaptic‐like plasticity functions, including: potentiation, depression, and spike‐amplitude‐dependent ...
Deianira Fejzaj   +3 more
wiley   +1 more source

Photoresponsive Rotaxanes Switch Lipid Bilayer Neuromorphic Behavior with Light

open access: yesAdvanced Electronic Materials, EarlyView.
A rotaxane consisting of a macrocycle ring with two azobenzene units mechanically interlocked onto an amphiphilic axle was incorporated into droplet interface bilayers (DIBs). Photoswitching between the azobenzene configurations on the ring resulted in cycling between memristive and memcapacitive behaviors in lipid bilayers, enabling programmable ...
P.T. Podar   +4 more
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

open access: yesAdvanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho   +6 more
wiley   +1 more source

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