1/F Noise in Mosfets

Haartman, Martin von; Östling, Mikael

doi:10.1007/978-1-4020-5910-0_3

Cited by 12 publications

(9 citation statements)

References 78 publications

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“…In contrast, an increase in the empirical factor γ from 1 to 3 in the

1 / f^{γ}

slope is only observed in the nonregrowth samples at low frequencies <100 Hz, indicating the presence of a prominent Lorentzian G–R noise at low frequency. [ 15 ] The corner frequency of the Lorentzian G–R noise lies probably still beyond the measurement limit (<1 Hz). We have tried to characterize this G–R peak by increasing the measurement temperature up to 150 °C which should shift the corner frequency inside the measurement window.…”

Section: Resultsmentioning

confidence: 99%

“…This is strong evidence that the flicker noise is dominated by the number fluctuations (Δ N ) mechanism, [ 11 ] which has been frequently found in GaN‐based D‐mode HEMT [ 12,13 ] and (nonregrowth) E‐mode HEMT. [ 14 ] In this Δ N case, explained by the McWhorter model, [ 15 ] the 1/ f noise is induced by the fluctuations of the interfacial charge, resulting from trapping/detrapping processes of free electrons in border traps close to the channel interface. Therefore, the normalized drain current can be described in proportion to the transconductance g m through

\frac{S_{id}}{I_{ds}^{2}} = S_{Vfb} {(\frac{g_{m}}{I_{ds}})}^{2}

where S Vfb is the input‐referred voltage noise PSD at flat‐band voltage and is a constant factor.…”

Section: Resultsmentioning

confidence: 99%

“…From the first approximation of the McWhorter model, the S Vfb for carrier number fluctuations can be described by [ 15 ]

S_{Vfb} = \frac{q^{2} k_{normalB} T λ N_{normalt}}{W L_{normalg} C_{barrier}^{2} f}

where λ is the tunneling attenuation distance of AlGaN barrier (nm), N t is the volumetric trap density in the barrier (eV −1 cm −3 ), k B is the Boltzmann constant, and f is the frequency.…”

Section: Resultsmentioning

confidence: 99%

“…From the first approximation of the McWhorter model, the S Vfb for carrier number fluctuations can be described by [15]…”

Section: Lfn Measurementsmentioning

confidence: 99%

See 3 more Smart Citations

Defect Characterization in High‐Electron‐Mobility Transistors with Regrown p‐GaN Gate by Low‐Frequency Noise and Deep‐Level Transient Spectroscopy

Hsu

Simoen

Liang

et al. 2021

Physica Status Solidi (a)

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To investigate the defects from the gate regrowth process, samples with and without regrowth p‐GaN process are fabricated by metalorganic chemical vapor deposition (MOCVD). The DC characteristics indicate larger gate leakage (Igs) between the GaN channel and the p‐GaN gate in the regrowth sample than in the nonregrowth counterpart. In addition, significant Si/O impurities are introduced by the regrowth process at the interface between channel and regrown AlGaN barrier. The low‐frequency noise (LFN) measurement and deep‐level transient spectroscopy (DLTS) are further carried out to investigate the defectivity at the AlGaN barrier and channel interface, giving a 2–3 times higher border trap density in the AlGaN barrier (depth ≈5 nm from the channel interface) and a 10 times increase in interface trap density at the channel interface, corresponding with a band of shallow levels E3 = Ec–0.02–0.15 eV. Three additional bulk traps E2/E5 (Ec–0.8 eV, σn=5×10−15 cm2 / Ec–0.17 eV, σn=5×10−22 cm2) and E4 (Ec–0.23 eV, σn=5×10−19 cm2) are also found in the regrowth and nonregrowth samples, respectively. Their possible spatial locations and origins are discussed.

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“…In contrast, an increase in the empirical factor γ from 1 to 3 in the

1 / f^{γ}

Section: Resultsmentioning

confidence: 99%

\frac{S_{id}}{I_{ds}^{2}} = S_{Vfb} {(\frac{g_{m}}{I_{ds}})}^{2}

where S Vfb is the input‐referred voltage noise PSD at flat‐band voltage and is a constant factor.…”

Section: Resultsmentioning

confidence: 99%

“…From the first approximation of the McWhorter model, the S Vfb for carrier number fluctuations can be described by [ 15 ]

S_{Vfb} = \frac{q^{2} k_{normalB} T λ N_{normalt}}{W L_{normalg} C_{barrier}^{2} f}

Section: Resultsmentioning

confidence: 99%

“…From the first approximation of the McWhorter model, the S Vfb for carrier number fluctuations can be described by [15]…”

Section: Lfn Measurementsmentioning

confidence: 99%

See 2 more Smart Citations

Defect Characterization in High‐Electron‐Mobility Transistors with Regrown p‐GaN Gate by Low‐Frequency Noise and Deep‐Level Transient Spectroscopy

Hsu

Simoen

Liang

et al. 2021

Physica Status Solidi (a)

View full text Add to dashboard Cite

show abstract

“…Low-frequency PSDs act as early failure detection to select robust organic light-emitting diodes in mass-production [ 54 ], and

noise properties are studied in layered MoTe

transistors to examine their susceptibility to environmental fluctuations in sensor applications [ 55 ]. The

noise and RTS have been measured in a variety of complementary metal-oxide semiconductor (CMOS) devices [ 56 ], and recently, as device sizes are shrinking down to the nanoscale, complex RTSs with multiple distinct levels of states occur more frequently [ 57 , 58 , 59 ]. Table 1 summarizes notable noise trends in several frequency domains where a certain noise source becomes dominant from internal noise sources in solid-state materials.…”

Section: Noise Processesmentioning

confidence: 99%

Material-Inherent Noise Sources in Quantum Information Architecture

Yang

Kim

2023

Materials

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NISQ is a representative keyword at present as an acronym for “noisy intermediate-scale quantum”, which identifies the current era of quantum information processing (QIP) technologies. QIP science and technologies aim to accomplish unprecedented performance in computation, communications, simulations, and sensing by exploiting the infinite capacity of parallelism, coherence, and entanglement as governing quantum mechanical principles. For the last several decades, quantum computing has reached to the technology readiness level 5, where components are integrated to build mid-sized commercial products. While this is a celebrated and triumphant achievement, we are still a great distance away from quantum-superior, fault-tolerant architecture. To reach this goal, we need to harness technologies that recognize undesirable factors to lower fidelity and induce errors from various sources of noise with controllable correction capabilities. This review surveys noisy processes arising from materials upon which several quantum architectures have been constructed, and it summarizes leading research activities in searching for origins of noise and noise reduction methods to build advanced, large-scale quantum technologies in the near future.

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Proposition of Adaptive Read Bias: A Solution to Overcome Power and Scaling Limitations in Ferroelectric‐Based Neuromorphic System

Koo,

Shin,

Kim

et al. 2023

Advanced Science

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Hardware neuromorphic systems are crucial for the energy‐efficient processing of massive amounts of data. Among various candidates, hafnium oxide ferroelectric tunnel junctions (FTJs) are highly promising for artificial synaptic devices. However, FTJs exhibit non‐ideal characteristics that introduce variations in synaptic weights, presenting a considerable challenge in achieving high‐performance neuromorphic systems. The primary objective of this study is to analyze the origin and impact of these variations in neuromorphic systems. The analysis reveals that the major bottleneck in achieving a high‐performance neuromorphic system is the dynamic variation, primarily caused by the intrinsic 1/f noise of the device. As the device area is reduced and the read bias (VRead) is lowered, the intrinsic noise of the FTJs increases, presenting an inherent limitation for implementing area‐ and power‐efficient neuromorphic systems. To overcome this limitation, an adaptive read‐biasing (ARB) scheme is proposed that applies a different VRead to each layer of the neuromorphic system. By exploiting the different noise sensitivities of each layer, the ARB method demonstrates significant power savings of 61.3% and a scaling effect of 91.9% compared with conventional biasing methods. These findings contribute significantly to the development of more accurate, efficient, and scalable neuromorphic systems.

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1/F Noise in Mosfets

Cited by 12 publications

References 78 publications

Defect Characterization in High‐Electron‐Mobility Transistors with Regrown p‐GaN Gate by Low‐Frequency Noise and Deep‐Level Transient Spectroscopy

Defect Characterization in High‐Electron‐Mobility Transistors with Regrown p‐GaN Gate by Low‐Frequency Noise and Deep‐Level Transient Spectroscopy

Material-Inherent Noise Sources in Quantum Information Architecture

Proposition of Adaptive Read Bias: A Solution to Overcome Power and Scaling Limitations in Ferroelectric‐Based Neuromorphic System

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