Results 81 to 90 of about 553 (192)

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

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

Ferroelectric Field-Effect Transistor Differential Amplifier Circuit Analysis

, 2008
There has been considerable research investigating the Ferroelectric Field-Effect Transistor (FeFET) in memory circuits. However, very little research has been performed in applying the FeFET to analog circuits.
Phillips, Thomas A., Ho, Fat D., MacLeod, Todd C.   +2 more
core  

Hardware‐Based On‐Chip Learning Using a Ferroelectric AND‐Type Array With Random Synaptic Weights

Advanced Intelligent Systems, EarlyView.
This work demonstrates an energy‐efficient on‐chip learning system using an Metal‐Ferroelectric‐Insulator‐Semiconductor FeAND synaptic array. By employing a feedback alignment scheme with a separate backward array using fixed random weights, the system overcomes directional limitations of AND‐type arrays and achieves robust, low‐power learning suitable
Minsuk Song, Changhyeon Han, Joonhyeok Choi, Jongwoo Lee, Hyun‐min Kim, Wongi Hong, Ujin Kim, Sung‐Tae Lee, Daewoong Kwon   +8 more
wiley   +1 more source

Enhancement of Ion-Sensitive Field-Effect Transistors through Sol-Gel Processed Lead Zirconate Titanate Ferroelectric Film Integration and Coplanar Gate Sensing Paradigm

Chemosensors
To facilitate the utility of field effect transistor (FET)-type sensors, achieving sensitivity enhancement beyond the Nernst limit is crucial. Thus, this study proposed a novel approach for the development of ferroelectric FETs (FeFETs) using lead ...
Dong-Gyun Mah, Seong-Moo Oh, Jongwan Jung, Won-Ju Cho   +3 more
doaj   +1 more source

Effects of Interface Trap on Transient Negative Capacitance Effect: Phase Field Model

, 2020
Ferroelectric materials have received significant attention as next-generation materials for gates in transistors because of their negative differential capacitance.
Taegeon Kim, Changhwan Shin
core   +1 more source

A 28nm HKMG super low power embedded NVM technology based on ferroelectric FETs

, 2022
S.11.5.1-11.5.4We successfully implemented a one-transistor (1T) ferroelectric field effect transistor (FeFET) eNVM into a 28nm gate-first super low power (28SLP) CMOS technology platform using two additional structural masks.
Müller, S., Polakowski, P., Jansen, S., Paul, J., Richter, R., Flachowsky, S., Beyer, S., Mikolajick, T., Schreiter, J., Utess, D., Reimer, B., Rice, B., Slesazeck, S., Müller, J., Ocker, J., Noack, M., Trentzsch, M., Mulaosmanovic, H.   +17 more
core   +1 more source

A Neuromorphic Simulation Framework for Indium‐Gallium‐Zinc‐Oxide Charge‐Trap Synaptic Transistors: From Device Modeling to System Simulation

Advanced Intelligent Systems, EarlyView.
This work presents a comprehensive framework bridging device fabrication, modeling, and system‐level simulation for an indium‐gallium‐zinc‐oxide (IGZO) charge‐trap synaptic transistor‐based neuromorphic system. By developing a precise SPICE model derived from fabricated IGZO synaptic transistors, the study incorporates parasitic RC loads into array ...
Yumin Yun, Junhyeong Park, Sunyeol Bae, Chae‐Hwan Park, Dong Hyeon Lee, Soo‐Yeon Lee   +5 more
wiley   +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

FeFET Based Nonvolatile TCAM and DRAM Development [PDF]

, 2018
Ferroelectric Field Effect Transistor (FeFET) is a promising nonvolatile device which provides high integration density, fast programming speed, and excellent CMOS compatibility.
Bayram, Ismail
core