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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Modeling of a Metal-Ferroelectric-Semiconductor Field-Effect Transistor NAND Gate

Considerable research has been performed by several organizations in the use of the Metal- Ferroelectric-Semiconductor Field-Effect Transistors (MFSFET) in memory circuits. However, research has been limited in expanding the use of the MFSFET to other electronic circuits. This research project investigates the modeling of a NAND gate constructed from MFSFETs. The NAND gate is one of the fundamental building blocks of digital electronic circuits. The first step in forming a NAND gate is to develop an inverter circuit. The inverter circuit was modeled similar to a standard CMOS inverter. A n-channel MFSFET with positive polarization was used for the n-channel transistor, and a n-channel MFSFET with negative polarization was used for the p-channel transistor. The MFSFETs were simulated by using a previously developed current model which utilized a partitioned ferroelectric layer. The inverter voltage transfer curve was obtained over a standard input of zero to five volts. Then a 2-input NAND gate was modeled similar to the inverter circuit. Voltage transfer curves were obtained for the NAND gate for various configurations of input voltages. The resultant data shows that it is feasible to construct a NAND gate with MFSFET transistors.

Phillips, Thomas A.↗
NASA NTRS2020

U309 N-Channel Silicon Junction Field Effect Transistor Total Ionizing Dose Characterization Report

The purpose of this testing was to characterize the InterFET Corporation U309 for total dose response. This test shall serve as the radiation lot acceptance test (RLAT) for the lot date code (LDC) tested. The U309 is a discrete Junction Field Effect Transistor (JFET). High dose rate (HDR) irradiations were performed in this test so that the device susceptibility to Total Ionizing Dose (TID) could be determined.

Total Ionizing Dose (TID)↗
NASA NTRS2006

Metal-Ferroelectric-Semiconductor Field-Effect Transistor NAND Gate Switching Time Analysis

Previous research investigated the modeling of a N Wga te constructed of Metal-Ferroelectric- Semiconductor Field-Effect Transistors (MFSFETs) to obtain voltage transfer curves. The NAND gate was modeled using n-channel MFSFETs with positive polarization for the standard CMOS n-channel transistors and n-channel MFSFETs with negative polarization for the standard CMOS p-channel transistors. This paper investigates the MFSFET NAND gate switching time propagation delay, which is one of the other important parameters required to characterize the performance of a logic gate. Initially, the switching time of an inverter circuit was analyzed. The low-to-high and high-to-low propagation time delays were calculated. During the low-to-high transition, the negatively polarized transistor pulls up the output voltage, and during the high-to-low transition, the positively polarized transistor pulls down the output voltage. The MFSFETs were simulated by using a previously developed model which utilized a partitioned ferroelectric layer. Then the switching time of a 2-input NAND gate was analyzed similarly to the inverter gate. Extension of this technique to more complicated logic gates using MFSFETs will be studied.

Phillips, Thomas A.↗
DOE OSTI2021

Effects of polymer grain boundary passivation on organic–inorganic hybrid perovskite field-effect transistors

Despite successful applications of solution-processed organic–inorganic hybrid perovskites (OIHPs) such as archetypical methylammonium lead iodide (MAPI) in high-performance optoelectronic devices including solar cells and light emitting diodes, their application in field-effect transistors (FETs) remains relatively limited due to the unresolved issues caused by ion migration in OIHPs, such as screening of gate electric fields, lowered device on-off ratios and field-effect mobility, and large hysteresis in the FET transfer characteristics. Here, we report improved performances of the MAPI-based FET via a polymer-additive-based grain boundary (GB) passivation approach that suppresses the ion migration. Polycaprolactone (PCL) was incorporated into the MAPI FET as a GB-passivation additive as confirmed by scanning electron and atomic force microscopies. Unlike the typical n-type behavior and large transfer hysteresis in the starting, pristine MAPI FETs, the GB passivation by PCL led to a drastically reduced hysteresis in FET transfer characteristics, while hinting at an ambipolar transport and slight improvement in mobility, indicating a reduced ion migration in the PCL-incorporated MAPI FET. The effect of PCL GB passivation in suppressing ion migration was directly confirmed by the measured, increased activation energy for ion migration in the PCL-incorporated MAPI. Here, the results not only represent the first report of the polymer-additive-based mitigation of the ion migration in the MAPI FET but also suggest potential utilities of the approach for enabling high-performance OIHP FETs and electronic devices in general.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Ferroelectric capacitors and field-effect transistors as in-memory computing elements for machine learning workloads

Abstract This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate machine learning (ML) workloads. We conducted an exploration of device fabrication and proposed system-algorithm co-design to boost performance. A novel FeCap device, incorporating an interfacial layer (IL) and$$\text {Hf}_{0.5}\text {Zr}_{0.5}\text {O}_2$$ Hf 0.5 Zr 0.5 O 2 (HZO), ensures a reduction in operating voltage and enhances HZO scaling while being compatible with CMOS circuits. The IL also enriches ferroelectricity and retention properties. When integrated into crossbar arrays, FeCaps and FeFETs demonstrate their effectiveness as IMC components, eliminating sneak paths and enabling selector-less operation, leading to notable improvements in energy efficiency and area utilization. However, it is worth noting that limited capacitance ratios in FeCaps introduced errors in multiply-and-accumulate (MAC) computations. The proposed co-design approach helps in mitigating these errors and achieves high accuracy in classifying the CIFAR-10 dataset, elevating it from a baseline of 10% to 81.7%. FeFETs in crossbars, with a higher on-off ratio, outperform FeCaps, and our proposed charge-based sensing scheme achieved at least an order of magnitude reduction in power consumption, compared to prevalent current-based methods.

Science & Technology - Other Topics↗
DOE OSTI2021

Switching Characteristics of a 1.2 kV, 50 mΩ SiC Monolithic Bidirectional Field Effect Transistor (BiDFET) with Integrated JBS Diodes

—The switching performance of large area (1cm x 1cm) monolithic 1.2 kV 50 mΩ 4H-SiC bidirectional field effect transistor (BiDFET) with integrated JBS diodes is reported for the first time. The devices were fabricated in a 6-inch commercial foundry and then packaged in a custom-designed four-terminal module. The switching performance of the BiDFET has been observed to be 1.4x better than that of its internal JBSFETs. Dynamic characterization was performed at 800 V with different gate resistances, current levels and case temperatures. An increase in switching losses was observed for the BiDFET with increasing gate resistance and current level as observed for SiC power MOSFETs. The BiDFET showed a 9% reduction in total switching loss from 25 °C to 150 °C with a current of 10 A.

Silicon Carbide, Monolithic, Bidirectional, FourQu↗
DOE OSTI2023

Simulation of gallium nitride vertical fin-shaped field effect transistor for use as thermal neutron detector

Through the use of a radiation detection system simulation framework, a gallium nitride vertical fin-shaped field effect transistor (FinFET) was studied for output response when utilized as a thermal neutron detector. The FinFET was assumed to had been backfilled with boron carbide, reactive to thermal neutrons. The GaN FinFET was modeled with radiation transport from MCNP, and the electronic transport from COMSOL Multiphysics. Fabricated FinFET devices (not neutron reactive) were tested to aid in the tuning of the COMSOL FinFET model. Through time-depenent studies, the drain current response pulse to simulated ionization due to single events lead to building of a database of device responses to radiation. By integrating the current pulses over time, the induced charge was calculated. Using the results of the radiation transport PTRAC file in combination with the induced charge database, an integrated charge spectrum was calculated.

Davidson, Bryce L.↗
DOE OSTI2021

Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning

Nanoelectronic devices emulating neuro-synaptic functionalities through their intrinsic physics at low operating energies is imperative toward the realization of brain-like neuromorphic computers. In this work, we leverage the non-linear voltage dependent partial polarization switching of a ferroelectric field effect transistor to mimic plasticity characteristics of biological synapses. Here, we provide experimental measurements of the synaptic characteristics for a 28nm high-k metal gate technology based device and develop an experimentally calibrated device model for large-scale system performance prediction. Decoupled read-write paths, ultra-low programming energies and the possibility of arranging such devices in a cross-point architecture demonstrate the synaptic efficacy of the device. Our hardware-algorithm co-design analysis reveals that the intrinsic plasticity of the ferroelectric devices has potential to enable unsupervised local learning in edge devices with limited training data

42 ENGINEERING↗
DOE OSTI2025

Prediction of Alpha-Particle-Immune Gate-All-Around Field-Effect Transistors (GAA-FET) Based SRAM Design

Alpha particles are known to be a major source of particles creating soft errors in semiconductor devices, such as content flipping in Static Random-Access Memory (SRAM). Recent advancements in transistor nodes have led to the introduction of Gate-All-Around Field Effect Transistors (GAA-FETs), which have better gate control, thus better electrostatics. Moreover, the introduction of bottom dielectric isolation (BDI) eliminates substrate leakage and thus is expected to enhance its radiation hardness. It is thus important to explore if one can design an SRAM that is completely radiation-hard to alpha particles. In this paper, using 3D Technology Computer-Aided-Design (TCAD) simulations, we show that it is possible to design an SRAM using GAA-FET technology so that it is immune to single alpha particle radiation error. In other words, with the design, there will be no single-event upset (SEU) due to alpha particles. We first use ab initio calculations in PHITS to show that there is a maximum linear energy transfer (LET), LET max , for the alpha particle in Si and Si x Ge 1-x . Based on that, by de signing a sub-7nm GAA-FET-based SRAM with BDI, we show that the SRAM does not flip even if the particle strike is in the worst-case scenario for LET > LET max .

42 ENGINEERING↗
NASA NTRS1988

Models of second-order effects in metal-oxide-semiconductor field-effect transistors for computer applications

Second-order effects in metal-oxide-semiconductor field-effect transistors (MOSFETs) are important for devices with dimensions of 2 microns or less. The short and narrow channel effects and drain-induced barrier lowering primarily affect threshold voltage, but formulas for drain current must also take these effects into account. In addition, the drain current is sensitive to channel length modulation due to pinch-off or velocity saturation and is diminished by electron mobility degradation due to normal and lateral electric fields in the channel. A model of a MOSFET including these considerations and emphasizing charge conservation is discussed.

Benumof, Reuben↗
DOE OSTI2021

Molecular Engineering of 2D Nanomaterial Field-Effect Transistor Sensors: Fundamentals and Translation across Innovation Spectrum

We report over the last decade, 2D layered nanomaterials have attracted significant attention across the scientific community due to their rich and exotic properties. Various nanoelectronic devices based on these 2D nanomaterials have been explored and demonstrated, including those for environmental applications. Here, the fundamental attributes of 2D layered nanomaterials for field-effect transistor (FET) sensors and tunneling FET (TFET) sensors, which provide versatile detection of water contaminants such as heavy-metal ions, bacteria, nutrients, and organic pollutants, are discussed. The major challenges and opportunities are also outlined for designing and fabricating 2D nanomaterial FET/TFET sensors with superior performance. Translation of these FET/TFET sensors from fundamental research to applied technology is illustrated through a case study on graphene-based real-time FET water sensors. A second case study centers on large-scale sensor networks for water-quality monitoring to enable intelligent drinking water and river-water systems. Overall, 2D nanomaterial FET sensors have significant potential for enabling a human-centered intelligent water system that can likely be applied to other precarious water supplies around the globe.

2D nanomaterials↗
DOE OSTI2022

Remote Floating-Gate Field-Effect Transistor with 2-Dimensional Reduced Graphene Oxide Sensing Layer for Reliable Detection of SARS-CoV-2 Spike Proteins

Despite intensive research of nanomaterials-based field-effect transistors (FETs) as a rapid diagnostic tool, it remains to be seen for FET sensors to be used for clinical applications due to a lack of stability, reliability, reproducibility, and scalability for mass production. In this work, we propose a remote floating-gate (RFG) FET configuration to eliminate device-to-device variations of two-dimensional reduced graphene oxide (rGO) sensing surfaces and most of the instability at the solution interface. Also, critical mechanistic factors behind the electrochemical instability of rGO such as severe drift and hysteresis were identified through extensive studies on rGO–solution interfaces varied by rGO thickness, coverage, and reduction temperature. rGO surfaces in our RFGFET structure displayed a Nernstian response of 54 mV/pH (from pH 2 to 11) with a 90% yield (9 samples out of total 10), coefficient of variation (CV) < 3%, and a low drift rate of 2%, all of which were calculated from the absolute measurement values. As proof-of-concept, we demonstrated highly reliable, reproducible, and label-free detection of spike proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a saliva-relevant media with concentrations ranging from 500 fg/mL to 5 μg/mL, with an R 2 value of 0.984 and CV < 3%, and a guaranteed limit of detection at a few pg/mL. Taken together, this new platform may have an immense effect on positioning FET bioelectronics in a clinical setting for detecting SARS-CoV-2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Hybrid stochastic synapses enabled by scaled ferroelectric field-effect transistors

Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling nanodevices results in reduction of programming resolution and emergence of stochastic non-idealities. While prior work has mainly focused on binary transitions, in this work, we leverage the stochastic switching of a three-state ferroelectric field-effect transistor to implement a long-term and short-term two-tier stochastic synaptic memory with a single device. Experimental measurements are performed on a scaled 28 nm high-k metal gate technology-based device to develop a probabilistic model of the hybrid stochastic synapse. In addition to the advantage of ultra-low programming energies afforded by scaling, our hardware–algorithm co-design analysis reveals the efficacy of the two-tier memory in comparison to binary stochastic synapses in on-chip learning tasks—paving the way for algorithms exploiting multi-state devices with probabilistic transitions beyond deterministic ones.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Dendritic Computing with Multigate Ferroelectric Field-Effect Transistors

Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer computational complexity far less than that of their biological counterparts. Neurons have dendritic arbors that connect to different sets of synapses and offer local nonlinear accumulation – playing a pivotal role in processing and learning. Inspired by this, we propose a novel neuron design based on a multigate ferroelectric field-effect transistor that mimics dendrites. It leverages ferroelectric nonlinearity for local computations within dendritic branches while utilizing the transistor action to generate the neuronal output. The branched architecture enables smaller crossbar arrays in hardware integration, improving efficiency. Using an experimentally calibrated device-circuit-algorithm cosimulation framework, we demonstrate that networks incorporating our dendritic neurons achieve superior performance compared to much larger networks without dendrites (∼ 17× fewer trainable weight parameters). These findings suggest that dendritic hardware can significantly improve computational efficiency and learning capacity of neuromorphic systems optimized for edge applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Selective solid-state isolation of NMR circuit elements using back-to-back field effect transistors

Nuclear Magnetic Resonance (NMR) electronics that employ selective solid-state isolation of circuit elements can include solid-state switches, such as back-to-back Field Effect Transistor (FET) pairs, and isolated gate drive electronics adapted to operate the solid-state switches in order to selectively decouple induction coils from receive electronics. The solid-state switches can be placed in series to achieve higher standoff voltages, and can be configured for low on resistance and short switching times. The gate drive electronics can include electrical isolation components adapted to enhance standoff voltages and reduce electrical noise at the selectively isolated receive electronics.

Walsh, David O.↗
NASA NTRS2013

Extended Characterization of the Common-Source and Common-Gate Amplifiers using a Metal-Ferroelectric-Semiconductor Field Effect Transistor

Collected data for both common-source and common-gate amplifiers is presented in this paper. Characterizations of the two amplifier circuits using metal-ferroelectric-semiconductor field effect transistors (MFSFETs) are developed with wider input frequency ranges and varying device sizes compared to earlier characterizations. The effects of the ferroelectric layer's capacitance and variation load, quiescent point, or input signal on each circuit are discussed. Comparisons between the MFSFET and MOSFET circuit operation and performance are discussed at length as well as applications and advantages for the MFSFETs.

Hunt, Mitchell↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗