Design and Fabrication Considerations for Thermo-Resistive Pixel Detectors for Room Temperature Directional Long Wavelength IR Sensing
Bibliographic record
Abstract
Rapid growth in application demands for smart sensing has fostered the development of micro-electromechanical-based devices with unique capabilities. Recent developments have led to complex autonomous systems that require a variety of intelligent sensing tasks to render human-machine interactions safe and efficient in workplace environments comprising both a human and robotic workforce. Here we report the design and fabrication of a novel thermo-resistive LWIR detector, for sensing and recalculating the direction of incident LWIR radiation for potential robotic applications. The work employs standard silicon micromachining processes along with sol-gel chemistry to fabricate three-dimensional pixel detectors to approximate an LWIR source's direction of incidence using thermo-resistive material. There have been no prior reports of the thermo-resistive effect in conjunction with directionality in LWIR sensors. In addition to the fabrication process flow, we describe the mathematical framework for sensor operation and present a photoresist deposition process for large 3-D topographic surface structures. We discuss the advantages and challenges of the currently designed and fabricated prototypes and highlight issues that limit their angular LWIR response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".