MétaCan
Menu
Back to cohort
Record W4416289484 · doi:10.1117/12.3077114

Interferometric measurement of ultra-smooth vacuum chucks for high-precision optical manufacturing

2025· article· W4416289484 on OpenAlexaff
Ying Yang, Sen Han, Yuhang Shen, Jincheng Zhuang, Xueyuan Li, Liwei Zhang, Jie Zhang, Hao Sun

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInterferometryWaferPlanarProfilometerPhase (matter)Enhanced Data Rates for GSM EvolutionSample (material)Coordinate-measuring machine

Abstract

fetched live from OpenAlex

The precise measurement of discontinuous precise surfaces with numerous isolated features, such as the array of planar pads on a vacuum chuck for wafer handling, remains a significant challenge in optical metrology. Traditional phase-shifting interferometry (PSI) fails due to the phase unwrapping dilemma at the numerous discontinuous boundaries surrounding each protrusion, while other methods suffer from limited resolution, environmental sensitivity, or low efficiency. This paper presents a novel Dual-State Interferometric Profilometry (DSIP) method to overcome these limitations. The core of DSIP involves the acquisition of two interferograms: an in-focus state for capturing high-resolution XY coordinate information of the protruding pad array, and a deliberately defocused state for robust retrieval of Z-height information, bypassing the need for global phase unwrapping. Preliminary experiments on a vacuum chuck sample with an array of raised circular pads demonstrated that the DSIP method successfully reconstructed the three-dimensional topography with enhanced edge resolution and environmental robustness. This approach therefore offers an efficient and practical solution for quality control in precision manufacturing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.290
Teacher spread0.241 · 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 teacher head, not a consensus.

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
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicOptical measurement and interference techniquesFrench-language works237,207