Wafer-bonded deep fluidics in BCB with in-plane coupling for lab-on-a-chip applications
Bibliographic record
Abstract
The wafer-scale fabrication of deep channel microfluidics for lab-on-a-chip applications by reactive ion etching and wafer bonding is reported. The microfluidic channels are etched in B-stage bisbenzocyclobutene (BCB) and Cytop with the latter used as a bonding agent. The channels are hermetically sealed between Borofloat glass and Si wafers by wafer bonding. Fluidic coupling is achieved in the plane of the channels via inlets and outlets that are revealed on the end facets of chips after dicing. Process techniques and details are reported for uniform coating and curing of BCB and Cytop, defining the channels with high accuracy using photolithography, dry etching the polymers using a hard mask, and sealing the channels by wafer bonding. Fluidic measurements are carried out at various flow rates and compared with modeling. The low Reynold's numbers of the channels ensure laminar flow conditions. Deep fluidic channels are less difficult to align to fluidic interfaces, they support higher flow rates and are less susceptible to clogging. In-plane fluidic coupling precludes the need to etch holes through the substrate. Our wafer-scale process was applied to 4 in. diameter wafers yielding 195 precision-aligned and hermetically sealed microfluidic chips, but is readily scalable to larger diameter wafers for volume production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".