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Record W3092052110 · doi:10.1109/jsen.2020.3029433

Design and Fabrication Considerations for Thermo-Resistive Pixel Detectors for Room Temperature Directional Long Wavelength IR Sensing

2020· article· en· W3092052110 on OpenAlexafffund
Siamack Vosoogh-Grayli, Gary W. Leach, Behraad Bahreyni

Bibliographic record

VenueIEEE Sensors Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResistive touchscreenFabricationDetectorMaterials scienceSurface micromachiningOptoelectronicsBolometerPhotoresistNanotechnologyComputer scienceEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.233
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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