A hybrid bacteria and microparticle detection platform on a CMOS chip: Design, simulation and testing considerations
Bibliographic record
Abstract
This paper presents a hybrid bacteria and microparticles detection platform based on a CMOS technology. Vertical face to face microelectrode arrays are implemented onto CMOS chips by connecting the metal and via layers together. A CMOS post-processing procedure based on Deep Reactive Ion Etching (DRIE) is used to release the microelectrodes and to construct microchannels in between. With medium flow of the fluid, Bacteria and microparticles are allowed to pass through the microchannels, where impedance variations are measured using a microelectrode pair on the wall, and then detected by electronic circuits embedded on the same chip. This microelectronic/microfluidic hybrid system targets screening individual bacterium or microparticle with high throughput and accuracy. The system architecture is presented first, followed by the detailed model, design, simulation and parameters of the prototype. The CMOS post-processing, specific packaging and testing procedures are also introduced in this paper. Finite element analysis method (FEM) and circuit simulations confirm that a single microparticle, 5 μm in diameter, can be detected by the proposed microsystem. Based on preliminary etching results, pairs of released electrodes 10 μm *2 μm *8 μm (L x W x H), also contribute to validate the feasibility of the CMOS post-processing procedure.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".