Results 51 to 60 of about 156,693 (147)

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source

Toward Capacitive In‐Memory‐Computing: A Device to Systems Level Perspective on the Future of Artificial Intelligence Hardware

Advanced Intelligent Discovery, EarlyView.
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj, Ella Paasio, Sayani Majumdar   +2 more
wiley   +1 more source

1.58-b FeFET-Based Ternary Neural Networks: Achieving Robust Compute-In-Memory With Weight-Input Transformations

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Ternary weight neural networks (TWNs), with weights quantized to three states (−1, 0, and 1), have emerged as promising solutions for resource-constrained edge artificial intelligence (AI) platforms due to their high energy efficiency with ...
Imtiaz Ahmed, Akul Malhotra, Revanth Koduru, Sumeet Kumar Gupta   +3 more
doaj   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

MEFET-Based CAM/TCAM for Memory-Augmented Neural Networks

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Memory-augmented neural networks (MANNs) require large external memories to enable long-term memory storage and retrieval. Content-addressable memory (CAM) is a type of memory used for high-speed searching applications and is well-suited for MANNs ...
Sai Sanjeet, Jonathan Bird, Bibhu Datta Sahoo   +2 more
doaj   +1 more source

Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation

Advanced Intelligent Systems, EarlyView.
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison, Kevin Nadaud, Josquin Harrison, Guillaume F. Nataf, Laura Bégon‐Lours   +4 more
wiley   +1 more source

Ge N‐Channel Ferroelectric FET Memory With Al2O3/AlN Interfacial Layer by Microwave Annealing

Advanced Electronic Materials
While n‐FeFET memory devices have shown promising characteristics for data storage and neuromorphic computing, implementing such devices with a Ge channel, which is expected to be adopted in advanced technology nodes, has never been reported due to the ...
Sheng‐Yen Zheng, Wei‐Ning Kao, Yu‐Hsing Chen, Yung‐Hsien Wu   +3 more
doaj   +1 more source

MXene‐Based Flexible Memory and Neuromorphic Devices

Small, EarlyView.
The unique two‐dimensional structure, excellent electrical conductivity, and diverse surface groups of MXenes have garnered significant attention. Coupled with their exceptional flexibility, MXene‐based devices hold immense potential for flexible memory and neuromorphic systems. This review comprehensively discusses the fundamentals of flexible devices,
Yan Li, Guanglong Ding, Yongbiao Zhai, Ziyu Lv, Yan Yan, Shuangmei Xue, Kui Zhou, Meng Zhang, Yutong Zhang, Qi‐Jun Sun, Yi Liu, Vellaisamy A. L. Roy, Ye Zhou, Su‐Ting Han   +13 more
wiley   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, Volume 12, Issue 17, 7 September 2026.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

FeFET-based MirrorBit cell for High-density NVM storage [PDF]

, 2023
HfO2-based Ferroelectric field-effect transistor (FeFET) has become a center of attraction for non-volatile memory applications because of their low power, fast switching speed, high scalability, and CMOS compatibility. In this work, we show an n-channel
Srinu, Rowtu, Lashkare, Sandip, Singh, Ajay Kumar, Saraswat, Vivek, Beyer, Sven, Dünkel, Stefan, Mulaosmanovic, Halid, Meihar, Paritosh, Ganguly, Udayan   +8 more
core   +1 more source