Thin film uncooled microbolometers based on plasma deposited materials
Bibliographic record
Abstract
We make a summary of our research and development efforts made about microbolometers (MBs) based on plasma-enhanced chemical vapor deposition (PECVD) materials, like noncrystalline semiconductors that provide high temperature coefficient of resistivity values, in conjunction with SiOx and SiNx dielectrics used for thermoisolation, which, together with micromachining, are paving new ways for fabrication of MBs, making them promising for 2D imagers in both infrared and tera-Hertz regions. We studied a-Si:H(B), a-Ge:H, a-GeSi:H, and polymorphous p-Ge:H, p-SiGe:H as thermosensing materials (TSMs) for MBs in “bridge” configuration with “planar” and “sandwich” electrodes. This allows placing the read-out circuitry under the bridge, improving use of pixel area. PECVD SiNx films were used as both a support layer and as a coating for improving the response for λ = 8–12 μm. 2D modeling revealed both linear and super linear response to IR intensity. Voltage responsivity RU = (1.2–7) × 105 V/W is observed in both “planar” and “sandwich” MBs. The latter shows current responsivity RI = 0.3–14 A/W higher by about three orders of magnitude than the former. A key issue for any detector is the detectivity. Different TSMs show different noise characteristics. Noise in “sandwich” MBs is several orders of magnitude higher than that in “planar” structures. The best parameters observed with TSM Ge-Si:H are: RU = 7.2 × 105 V/W, RI = 14 A/W voltage and current detectivities [Formula: see text] = 8 × 109 cm Hz1/2W−1 and [Formula: see text] = 4 × 109 cm Hz1/2W−1. Junction structures on top of the “bridge” are also discussed. Finally we describe some reported applications of MBs.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".