Results 41 to 50 of about 267 (149)

Evaluating the Robustness of Complementary Channel Ferroelectric FETs Against Total Ionizing Dose Towards Radiation-Tolerant Embedded Nonvolatile Memory

, 2023
In this work, a thorough assessment of the robustness of complementary channel HfO2 ferroelectric FET (FeFET) against total ionizing dose (TID) radiation is conducted, with the goal of determining its suitability for use as high-performance and energy ...
Zubair Faris (15194859), sven beyer (15626089), Zhouhang Jiang (15625733), steven soss (15626080), Ronald D. Schrimpf (15626126), Halid Mulaosmanovic (15626035), santosh kurinec (15626099), Xuyi Luo (15625990), stefan duenkel (15626077), Hussam Amrouch (15351787), Zixiang Guo (15625960), munazza sayed (15626011), Kai Ni (15194944), xiao gong (15626091), Daniel M. Fleetwood (15626106), Vijaykrishnan Narayanan (15194917), enxia zhang (15626103)   +16 more
core   +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 Dual‐Memory Ferroelectric Transistor Emulating Synaptic Metaplasticity for High‐Speed Reservoir Computing

Advanced Electronic Materials, EarlyView.
A CMOS‐compatible ferroelectric transistor harnesses the interplay between stable gate polarization memory and volatile non‐quasi‐static channel charge dynamics to emulate how biological synapses regulate their own plasticity. This brain‐inspired dual‐memory mechanism, realized in a single device, enables a physical reservoir computer that solves ...
Yifan Wang, Muhammad Sakib Shahriar, Salma Soliman, Noah Vaillancourt, Lance Fernandes, Andrea Padovani, Asif Islam Khan, Md Sakib Hasan, Raisul Islam   +8 more
wiley   +1 more source

Silicon Nitride Resistive Memories

Advanced Electronic Materials, EarlyView.
Amorphous SiNx is an attractive resistance switching material for ReRAM applications due to its physicochemical properties, such as humidity resistance, low oxygen diffusivity, and is used as a metal diffusion blocker. By modifying the ratio between N and Si atoms, the microstructure of the SiNx is affected, rendering it possible to change the ...
Alexandros‐Eleftherios Mavropoulis, Nikolaos Vasileiadis, Iosif‐Angelos Fyrigos, Emmanouil Stavroulakis, Caroline Bonafos, Pascal Normand, Georgios Ch. Sirakoulis, Panagiotis Dimitrakis   +7 more
wiley   +1 more source

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

Power-Delay Area-Efficient Processing-In-Memory Based on Nanocrystalline Hafnia Ferroelectric Field-Effect Transistors

, 2022
Ferroelectric field-effect transistors (FeFETs) have attracted enormous attention for low-power and high-density nonvolatile memory devices in processing-in-memory (PIM).
Sehee Lim (14307898), Minyoung Jo (1726837), Sanghun Jeon, Yeongseok Jeong (14307907), Young Kyu Lee (14076058), Seongook Jung, Dong Han Ko, Young Kyu Lee, Sangho Lee (1629250), Giuk Kim, Yeongseok Jeong, Giuk Kim (13805829), Minhyun Jung (4326718), Sehee Lim, Taeho Kim (1275954), Hunbeom Shin (14307904), Minyoung Jo, Taehyong Eom, Hunbeom Shin, Taehyong Eom (14307901), Seongook Jung (14307910), Sanghun Jeon (1535008), Taeho Kim, Minhyun Jung, Dong Han Ko (14307895), Sangho Lee   +25 more
core   +1 more source

Logic Functionality and Circuit Design of In2Se3‐Based Split‐Gate Ferroelectric Field‐Effect Transistor for Zero‐Trust Applications

Advanced Electronic Materials, EarlyView.
ABSTRACT Van der Waals ferroelectric materials are emerging as key building blocks for future logic devices and integrated circuits. Among them, α‐In2Se3 offers a unique combination of robust room temperature ferroelectricity and semiconducting behavior.
Ankita Ram, Cédric Marchand, Shehr Bano Masood, Aymen Mahmoudi, Takashi Taniguchi, Kenji Watanabe, Bernard Doudin, Abdelkarim Ouerghi, Ian O'Connor, Alexei Gruverman, Jean‐François Dayen   +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

Novel Fluorite Structure Ferroelectric and Antiferroelectric Hafnium Oxide-based Nonvolatile Memories

, 2022
The ferroelectricity in fluorite structure based hafnium oxide (HfO2) material expanded the horizon for realizing nonvolatile ferroelectric memory concepts. Due to the excellent HfO2 ferroelectric film properties, CMOS compatibility, and scalability; the
Ali, Tarek
core  

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