Results 241 to 250 of about 103,132 (288)

Monolithic Co‐Integration of Vertical FET and Memristor for 1T1R Cell

open access: yesAdvanced Electronic Materials, EarlyView.
This work demonstrates a vertically integrated one‐transistor–one‐memristor (1T1R) cell by stacking a MoS2 vertical field‐effect transistor (VFET) with a mortise–tenon‐shaped (MTS) memristor. This compact architecture not only exhibits highly uniform resistive switching characteristics but also provides a strategy for constructing densely packed ...
Fubo Jiao   +15 more
wiley   +1 more source

Nonlocal Mechano-Optical Metasurfaces. [PDF]

open access: yesACS Photonics
van Gorp F   +3 more
europepmc   +1 more source

Robust and Compatible Ferroelectric Memories with Polycrystalline TiO2 Channel for 3D Integration

open access: yesAdvanced Electronic Materials, EarlyView.
Robust and monolithic 3D compatible ferroelectric memories are realized using the polycrystalline TiO2 channel‐based FeFET. The review covers physical mechanisms of the TiO2 channel FeFET, quantitative benchmarking, and advanced planar/vertical architectures for monolithic 3D integration based on HfO2‐TiO2 gate stack, offering a roadmap for reliable ...
Xujin Song   +10 more
wiley   +1 more source

Embedded Direct‐Written Organic Micro‐TEGs for High‐Efficiency Skin‐Heat Harvesting

open access: yesAdvanced Electronic Materials, EarlyView.
A finite‐element–guided design of direct‐written organic micro‐thermoelectric generators is presented for efficient skin‐heat harvesting. Embedding PEDOT:PSS/PBFDO thermoelectric legs within flexible substrates suppresses interfacial heat losses and enhances vertical heat flow.
Milad Jabri   +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

Self-assembly by anti-repellent structures for programming particles with momentum. [PDF]

open access: yesNat Commun
Bae J   +7 more
europepmc   +1 more source

Atomic Layer Deposition of Disordered p‐Type SnO Using a Heteroleptic Tin(II) Precursor: Influence of Disorder on P‐Channel SnO Thin‐Film Transistor Characteristics

open access: yesAdvanced Electronic Materials, EarlyView.
Disordered p‐type SnO thin‐films are deposited via atomic layer deposition using a novel heteroleptic precursor. These films enable low‐temperature fabrication of thin‐film transistors with excellent stability and mobility. Their potential compatibility with flexible substrates and integration with n‐type IGZO transistors makes them candidates for ...
Benjamin J. Peek   +15 more
wiley   +1 more source

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