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Record W4413821529 · doi:10.1021/acsaelm.5c00988

Fast and Cost-Effective Fabrication of Ultrathin (20 μm) Silicon Substrates by Melt-Spinning

2025· article· en· W4413821529 on OpenAlex
Hyung Woo Choi, Dhanesh Chandra, Ghassan E. Jabbour

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Electronic Materials · 2025
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsFabricationSpinningSiliconMaterials scienceNanotechnologyComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

Thin silicon wafer fabrication is a crucial aspect of semiconductor manufacturing, offering enhanced material yield and reduced fabrication costs. Traditional techniques for producing thin silicon substrates often involve the use of supporting substrates for bonding/debonding or intricate processes, such as etching and thinning. In this study, we present the fabrication of an ultrathin polycrystalline silicon substrate utilizing a melt-spinner approach. Our approach has yielded a substrate of unprecedented dimensions, characterized by a width of 1 cm, a length of 5 cm, and an approximate thickness of 20 μm, and fabricated at a speed of 35 m s –1 . This development marks a significant progression in the domain of silicon substrate fabrication, as it stands as the thinnest free-standing polycrystalline silicon substrate achieved to date. Our approach presents substantial potential for cost-effective substrate manufacturing, eliminating the need for the current thinning and etching steps that contribute to material waste, excessive processing time, and high electricity consumption for melting raw silicon material as melt-spun silicon substrates require a postprocessing step of polishing for less than 10 min. This advancement is poised to benefit not only silicon photovoltaic applications but also a broad range of applications, including lightweight wearable electronics, ultrathin membrane structures, microelectromechanical systems for sensing, and the development of advanced material processing.

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.

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 score0.033
Threshold uncertainty score0.679

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 science0.0000.000
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 head0.003
GPT teacher head0.206
Teacher spread0.203 · 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