Design and Fabrication of Vertically-Integrated CMOS Image Sensors
Bibliographic record
Abstract
Technologies to fabricate integrated circuits (IC) with 3D structures are an emerging trend in IC design. They are based on vertical stacking of active components to form heterogeneous microsystems. Electronic image sensors will benefit from these technologies because they allow increased pixel-level data processing and device optimization. This paper covers general principles in the design of vertically-integrated (VI) CMOS image sensors that are fabricated by flip-chip bonding. These sensors are composed of a CMOS die and a photodetector die. As a specific example, the paper presents a VI-CMOS image sensor that was designed at the University of Alberta, and fabricated with the help of CMC Microsystems and Micralyne Inc. To realize prototypes, CMOS dies with logarithmic active pixels were prepared in a commercial process, and photodetector dies with metal-semiconductor-metal devices were prepared in a custom process using hydrogenated amorphous silicon. The paper also describes a digital camera that was developed to test the prototype. In this camera, scenes captured by the image sensor are read using an FPGA board, and sent in real time to a PC over USB for data processing and display. Experimental results show that the VI-CMOS prototype has a higher dynamic range and a lower dark limit than conventional electronic image sensors.
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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) | 0.001 | 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".