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Record W2587509391 · doi:10.1109/asscc.2016.7844128

An area-efficient wideband CMOS hall sensor system for camera autofocus systems

2016· article· en· W2587509391 on OpenAlexfundno aff
Chih-Chan Tu, Kuan‐Chung Chen, Wu Tsung-Yu, Tsung‐Hsien Lin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsnot available
FundersCanadian Food Inspection Agency
KeywordsCMOSAmplifierCapacitorElectrical engineeringAutofocusWidebandHall effect sensorChipComputer scienceElectronic engineeringPhysicsEngineeringVoltageOptics

Abstract

fetched live from OpenAlex

This paper presents an area-efficient and fast-response CMOS Hall sensor system for a camera autofocus system. The prototype comprises of a Hall sensor and a capacitively-coupled instrumentation amplifier (CCIA). The Hall sensor adopts the spinning current technique to mitigate the Hall element offset. In the CCIA, the T-capacitor network is employed considering the capacitor matching requirement and the chip area. The amplifier offset is further suppressed by a ripple-reduction loop. This chip is measured with Helmholtz coil, Solenoid, and NMR system. Implemented in a 0.18-μm CMOS process, it achieves 564 μTrmsin 180-kHz BW. The linearity error is2.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.246
Teacher spread0.232 · 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
GenreEmpirical

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

Citations3
Published2016
Admission routes1
Has abstractyes

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