Evidence map›Paper›PMID 42653544›Full record

ArticleMicromachines2026

Hidden Order in the Apparent Chaos of Bias Temperature Instability.

Joseph B Bernstein

Abstract read
In one paragraph

Article in Micromachines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Joseph B BernsteinDepartment of Electrical and Electronic Engineering, Ariel University, Ariel 40700, Israel.ORCID 0000-0002-1878-5921

Funding

the US Office of Naval Research N000142312617
6 · The paper itself

Abstract

Bias Temperature Instability (BTI) remains one of the principal reliability challenges limiting advanced CMOS technologies. Although degradation is commonly described by an empirical power-law relationship, the power-law exponent is generally regarded only as a fitting parameter used for lifetime extrapolation. This Perspective reexamines a previously published Multiple-Temperature Operational Life (MTOL) dataset to investigate whether the measured exponent contains previously overlooked physical information. Individual ring oscillators stressed under identical voltage and temperature conditions exhibit substantially different, yet reproducible, power-law exponents. When these measurements are analyzed over a broader temperature range, the apparent statistical scatter reveals a systematic kinetic dependence that produces a remarkably consistent lifetime relationship after incorporating the experimentally measured exponent into the Arrhenius analysis. The resulting intrinsic activation energy is significantly smaller than values obtained using conventional extrapolation methods, suggesting that part of the apparent activation energy arises from neglecting the temperature dependence of the degradation exponent. A recently proposed thermodynamic formulation based on Gibbs free energy and correlation entropy is presented as one possible physical interpretation of these observations, in which the power-law exponent reflects the correlation between successive degradation events rather than merely an empirical fitting constant. More generally, this Perspective suggests that the power-law exponent should be regarded as a measurable kinetic quantity whose systematic variation may provide additional insight into degradation mechanisms in BTI and other reliability phenomena.

Indexed as

bias temperature instability (BTI)degradation correlationdegradation kineticselectromigrationGibbs free energylifetime predictionpower–law degradationreliability physics

Identifiers

PMID42653544
PMCPMC13515958

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.