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Record W4248533458 · doi:10.1049/el.2016.2880

noise limiting

2016· article· en· W4248533458 on OpenAlexaboutno aff

Bibliographic record

VenueElectronics Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsnot available
Fundersnot available
KeywordsCMOSElectronic engineeringElectrical engineeringInverterTransistorEngineeringAmplifierComputer scienceIntegrated injection logicElectronic circuitPass transistor logicDigital electronicsVoltage

Abstract

fetched live from OpenAlex

A straightforward relationship between signal-to-noise ration (SNR) and power consumption in CMOS inverters has been developed at the University of Toronto. It indicates that the fundamental limits in time-domain signal processing are set by transistor threshold and supply voltage, rather than technology-scaling, or the approach used to process the signals. Time to digital converter: CMOS inverters are used as delay elements to resolve pico-second time difference between two events CMOS is among the most important technologies used in integrated circuits. Its use spans from digital processors to analogue front-ends for both wireless and wireline communications. CMOS inverters are the simplest CMOS digital elements. They can be used for logic operations, or as a delay element, or even as an analogue voltage amplifier. There is great interest in making inverters faster and less power hungry, as this has a direct impact in on the power dissipation of almost all our everyday electronic devices, such as mobile phones and laptops. CMOS technology is used so widely because of its very low cost compared to alternative IC technologies, as well as its low power dissipation when used for digital operations. The CMOS inverter structure is composed of two types of transistor (n-type and p-type). When the transistors operate as switches, the structure works like a digital element whose output is the inverse (or complement) of the input. As a digital element, the inverter can also be used as a delay stage, one of the most important building blocks for circuits based on time-domain signal processing. When the transistors operate as voltage-to current amplifiers, the structure acts as an inverting voltage amplifier, producing an amplified replica of the input signal with opposite phase. “The CMOS inverter is only one of the numerous structures that can be created with CMOS technology,” said Toronto team member Dr Antonio Liscidini. “However, the study of the limits of performance of the CMOS inverter can be useful to understand the overall limitations of such technology and what benefit an evolution of CMOS technology could realise.” At the end of the last century Enz and Vittot demonstrated that there is a precise correlation between the signal-to-noise ratio achievable by the CMOS structure when used as an amplifier, and the power dissipated. “Such limit, evaluated only for the CMOS amplifier was found to be the same for even more complex circuits, such as analogue-to-digital converters and switched capacitors filters,” Liscidini explained. “Our aim was to analyse the performance of the CMOS inverter used as a delay element, to find a straightforward relationship between signal-to-noise ratio and power consumption'. Like the one found by Enz and Vittot for voltage mode operation”. In their Letter the Toronto researchers report their success in this respect, but also go on to demonstrate that regardless of the mode of operation, the fundamental limit is substantially the same. In time-domain signal processing this limit does not improve with technology scaling, but is mainly dominated by the ratio between the transistor thresholds and the voltage supply adopted. “The importance of this result,” said Lisicidini, “is that, despite the approach chosen to process the signals (voltage-domain or time-domain), similar boundaries will be found. Regardless of technology scaling, time-domain signal processing cannot offer a privileged path to reach lower power consumption for a targeted signal-to-noise ratio.” Dr Antonio Liscidini at the University of Toronto The authors also hope this understanding of the fundamental limitations will also be useful in comparing different designs based on time-domain signal processing; offering an additional instrument to highlight the benefits of the different techniques adopted in literature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.028
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0280.017

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.171
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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