Hybrid Integration of an Active Pixel Sensor and Microfluidics for Cytometry on a Chip
Bibliographic record
Abstract
Reported are motivations and approaches for the integration of custom sensors with microfluidic devices for cytometry on a chip and related fluid metering applications. To demonstrate, details of a digital 16-element mixed-signal CMOS active pixel optical sensor with adaptive spatial filtering is first described. The 0.18-mum CMOS fabricated sensor is then shown coupled to a microfluidic channel via a polymer encapsulated chip-on-board approach as well as a preferred flip-chip-on-glass hybrid integration approach. However, both approaches discussed possess attributes that are well suited for reliable high-volume production. Utilizing these two disparate assembly topologies, the intelligent sensor's general behavior, optical input dynamic range, and near-field sensitivity to polymer beads being transported in a microfluidic channel is explored. The findings suggest that discrete near-field sensor integration with microfluidics is a well-positioned integration approach for helping to obviate the need for precision analog-to-digital conversion, optical fiber microchannel coupling, and conventional microscopy for a set of relevant micro total analysis system applications. By opting instead for a hybrid multichip module approach to system integration, this study marks a slight departure in strategy relative to many common monolithic system-on-chip integration efforts
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".