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Record W2766505605 · doi:10.1364/oe.25.028838

Manufacturing with light - micro-assembly of opto-electronic microstructures

2017· article· en· W2766505605 on OpenAlexaff
Shuailong Zhang, Yongpeng Liu, Qian Yang, Weizhen Li, Joan Juvert, Pengfei Tian, Jean-Claude Navarro, Alasdair W. Clark, Erdan Gu, Martin D. Dawson, Jonathan M. Cooper, Steven L. Neale

Bibliographic record

VenueOptics Express · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research Council
KeywordsOpticsMaterials scienceMicrostructureOptoelectronicsPhysicsComposite material

Abstract

fetched live from OpenAlex

Optical micromanipulation allows the movement and patterning of discrete micro-particles within a liquid environment. However, for manufacturing applications it is desirable to remove the liquid, leaving the patterned particles in place. In this work, we have demonstrated the use of optoelectronic tweezers (OET) to manipulate and accurately assemble Sn62Pb36Ag2 solder microspheres into tailored patterns. A technique based on freeze-drying technology was then developed that allows the assembled patterns to be well preserved and fixed in place after the liquid medium in the OET device is removed. After removing the liquid from the OET device and subsequently heating the assembled pattern and melting the solder microspheres, electrical connections between the microspheres were formed, creating a permanent conductive bridge between two isolated metal electrodes. Although this method is demonstrated with 40 mu m diameter solder beads arranged with OET, it could be applied to a great range of discrete components from nanowires to optoelectronic devices, thus overcoming one of the basic hurdles in using optical micromanipulation techniques in a manufacturing micro-assembly setting. Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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.

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

Citations37
Published2017
Admission routes1
Has abstractyes

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