Results 241 to 250 of about 30,870 (310)

Electrode‐Engineered Dual‐Mode Multifunctional Lead‐Free Perovskite Optoelectronic Memristors for Neuromorphic Computing

Advanced Electronic Materials, EarlyView.
A lead‐free perovskite memristive solar cell structure that call emulate both synaptic and neuronal functions controlled by light and electric fields depending on top electrode type. ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb ...
Michalis Loizos, Konstantinos Rogdakis, Konstantinos Chatzimanolis, Katerina Anagnostou, Emmanuel Kymakis   +4 more
wiley   +1 more source

Start-Up Circuits for Ultra-Low-Voltage Thermoelectric Energy Harvesting: A Topology-Oriented Review and Design Guide. [PDF]

Nanomaterials (Basel)
Ali M, Hassan SJU, Cho S.
europepmc   +1 more source

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

Advanced 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, Chenxi Yu, Shangze Li, Dijiang Sun, Jiajia Zhang, Haotong Zhu, Xiaomin Xiao, Xionghao Qin, Xiaoyan Liu, Xing Zhang, Jinfeng Kang   +10 more
wiley   +1 more source

Data-In-situ Computing with One-Pixel-Multiple-Memristor Architecture for Neuromorphic Sequential Vision. [PDF]

Nat Commun
Sun Y, Tong P, Shen J, Xu H, Cao R, Liu C, Chen C, Song B, Wang Y, Wang W, Yang Y, Li Q.   +11 more
europepmc   +1 more source

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

Advanced 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, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

A 326,000 fps 640 × 480 Resolution Continuous-Mode Ultra-High-Speed Global Shutter CMOS BSI Imager. [PDF]

Sensors (Basel)
Bacq JL, Thite M, Vandebriel R, Mahato SB, Coppejans P, Borremans J, Wu L, Rączkowski K, Cevik I, Motsnyi V, Haspeslagh L, Suess A, Flon B, Jantzen D, Jantzen P, Cavaco C, Spagnolo A.   +16 more
europepmc   +1 more source

Ultra‐Low Power Consumption and Highly Durability in Sm:HfO2 Thin Film Ferroelectric Memristor for Edge Detection

Advanced Electronic Materials, EarlyView.
ABSTRACT With the continuous development of computer image processing, developing efficient and low‐power computing devices has become a key challenge. Memristors have integrated in‐situ storage and computing capabilities, making them an ideal choice for low‐power image processing computing architectures. However, current memristors are confronted with
Tengyu Li, YongRui Wang, Weifeng Zhang, Jikang Xu, Xiaobing Yan   +4 more
wiley   +1 more source

A large-scale coherent 4D imaging sensor. [PDF]

Nature
Settembrini FF, Gungor AC, Forrer A, Fortune SA, Dell'Aquila A, Padmanabhan P, Opris IE, Sotto M, Dordevic N, Perelman Y, Christen T, Wang M, Nicolaescu R.   +12 more
europepmc   +1 more source