Electro-Optical Characterization of Amorphous Germanium-Tin (Ge1-XSnx) Microbolometer
Bibliographic record
Abstract
<title>Abstract</title> The utilization of amorphous germanium-tin (Ge<sub>1 − x</sub>Sn<sub>x</sub>) semiconducting thin films as temperature sensing layers in microbolometers was recently presented and patented. The work in this paper started by extending the latest study to acquire better characteristics of the Sn concentrations % for microbolometer applications. In this work, Ge1-xSnx thin films with various Sn concentrations %, <italic>x</italic>, where 0.31 ≤ <italic>x</italic> ≤ 0.48 we sputter deposited. Elemental composition was evaluated using Energy Dispersive X-ray (EDX) spectroscopy. Surface morphology was evaluated using Atomic Force Microscopy (AFM) revealing average roughness values between ~ 0.2–0.8 nm. Sheet resistance versus temperature measurements was performed and analyzed revealing temperature coefficients of resistances, TCRs, ranging from − 3.11%/K to -2.52%/K for x ranging from 0.31 to 0.40. The Ge1-xSnx thin film was found to depart the semiconducting behavior at 0.40 < x ≤ 0.48. Empirical relationships are derived relating resistivity, TCR, and Sn concentration % for amorphous Ge1-xSnx thin films. One of the films with 31% Sn concentration (Ge<sub>0.69</sub>Sn<sub>0.31</sub>) was used to fabricate 10×10 µm2 microbolometer prototypes using electron-beam lithography and liftoff techniques and the microbolometer is fabricated on top of oxidized silicon substrates with no air gap between them. The noise behavior and the maximum detected signal of the fabricated microbolometer were measured. The signal-to-noise ratio, voltage responsivity, and noise equivalent power values of the prototypes were calculated. Finally, the expected performance of the microbolometer when fabricated in an air bridge is calculated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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 teacher head, 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".