Results 61 to 70 of about 11,359 (209)

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

open access: yesAdvanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath   +4 more
wiley   +1 more source

Comprehensive analysis of In0.53Ga0.47As SOI-FinFET for enhanced RF/wireless performance

open access: yesFrontiers in Electronics
This paper comprehensively analyses the RF (Radio Frequency) and wireless performance characteristics of high-k In0.53Ga0.47As silicon-on-insulator FinFET (InGaAs-SOI-FinFET).
Priyanka Agrwal, Ajay Kumar
doaj   +1 more source

Review of Nanosheet Transistors Technology

open access: yesTikrit Journal of Engineering Sciences, 2021
Nano-sheet transistor can be defined as a stacked horizontally gate surrounding the channel on all direction. This new structure is earning extremely attention from research to cope the restriction of current Fin Field Effect Transistor (FinFET ...
Firas .. Agha   +2 more
doaj   +1 more source

Correlation between the golden ratio and nanowire transistor performance [PDF]

open access: yes, 2018
An observation was made in this research regarding the fact that the signatures of isotropic charge distributions in silicon nanowire transistors (NWT) displayed identical characteristics to the golden ratio (Phi).
Al-Ameri, Talib
core   +1 more source

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

open access: yesAdvanced 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   +3 more
wiley   +1 more source

Voltage Differencing Buffered Amplifier Realisation Using 32 nm FinFET Technology and Universal Filter Applications

open access: yesElektronika ir Elektrotechnika
This paper presents high-frequency universal filter applications based on a voltage differential buffered amplifier (VDBA) using 32 nm fin field effect transistor (FinFET) technology.
Sevda Altan Yagci   +2 more
doaj   +1 more source

Design of Polymer-Based Trigate Nanoscale FinFET for the Implementation of Two-Stage Operational Amplifier

open access: yesInternational Journal of Polymer Science, 2022
The major motivation behind transistor scaling is the requirement for high-speed transistors with lower fabrication costs. When the fin thickness or breadth is smaller than 10 nm in a trigate FET, charges travel in a nonconfined fashion, resulting in the
Jami Venkata Suman   +2 more
doaj   +1 more source

Programmable Dimensional Lithography with Digital Micromirror Devices for Multifunctional Microarchitectures

open access: yesAdvanced Materials Technologies, Volume 11, Issue 5, 6 March 2026.
This review explores recent advances in digital micromirror device (DMD)‐based lithography, focusing on its programmable light modulation, multi‐material compatibility, and dimensional patterning strategies. It highlights innovations from optical system design to materials integration and multifunctional applications, positioning DMD lithography as a ...
Yubin Lee   +5 more
wiley   +1 more source

Ternary Content‐Addressable Memory Using One Capacitor and One Nanoelectromechanical Memory Switch for Data‐Intensive Applications

open access: yesAdvanced Intelligent Systems, Volume 8, Issue 3, March 2026.
A charge‐domain ternary content‐addressable memory using one capacitor one nanoelectromechanical memory switch (1C‐1N TCAM) is proposed for energy‐efficient, high‐reliability computations. Integrated with the back‐end‐of‐line process, the 1C‐1N TCAM leverages the air gap capacitance to achieve a high capacitance ratio and ternary functionality.
Jin Wook Lee   +5 more
wiley   +1 more source

Hybrid Convolutional Neural Network‐Analytical Model for Prediction of Line Edge Roughness‐Induced Performance Variations in Fin‐Shaped Field‐Effect Transistor Devices and SRAM

open access: yesAdvanced Intelligent Systems, Volume 8, Issue 3, March 2026.
This work presents a hybrid model for predicting the electrical characteristics of fin‐shaped field‐effect transistor devices and SRAM with line edge roughness. The model consists of a convolutional neural network (CNN) and an analytical model that simulates the electrical characteristics of transistors using the outputs of CNN, enabling fast and ...
Jaehyuk Lim   +4 more
wiley   +1 more source

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