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Record W4243419681 · doi:10.1007/978-3-319-41190-3_11

Infrared Detectors

2016· book-chapter· en· W4243419681 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsCollege of the North Atlantic
Fundersnot available
KeywordsMercury cadmium tellurideInfraredTelluriumOptoelectronicsBand gapMaterials scienceSemiconductorNarrow-gap semiconductorElectron mobilityInfrared detectorOpticsPhysics

Abstract

fetched live from OpenAlex

Tellurium-based compounds such as cadmium telluride (CdTe) and mercury cadmium telluride (HgCdTe) have been used as infrared (IR) detectors for over half a century. These versatile narrow gap semiconducting materials are characterized by a direct energy gap and have the ability to obtain both high and low carrier concentrations, high electron mobility of electrons, and low dielectric constant. Nanophotosensors with cadmium chalcogenide (Te, Se, and S) semiconductor nanocrystals are considered to be best candidates to detect spacecraft cracks without increasing payload or changing the thermal properties of heat-shielding of spacecraft. Hg1−x Cd x Te (MCT) is the most widely used infrared (IR) detector material in military applications, compared to other IR detector materials, primarily because of two key features: it is a direct energy band gap semiconductor and its band gap can be engineered by varying the Cd composition to cover a broad range of wavelengths. A small change of lattice constant with composition makes it possible to grow high-quality layers and heterostructures. These can thus be used for detectors operated at various modes, and can be optimized for operation spanning the wide range of the IR spectrum (short-wave infrared (SWIR): 1–3 μm, middle wavelength IR (MWIR: 3–5 μm; long-wavelength IR: 8–14 μm) to very long-wave infrared (VLWIR): 14–30 μm, and at temperatures ranging from that of liquid helium to room temperature. Other specific advantages include a direct energy gap, ability to obtain both low and high carrier concentrations, high mobility of electrons, and low dielectric constant. However, in spite of the various advantages, the material suffers from technological disadvantages partly due to the presence of a weak Hg–Cd bond, which results in bulk, surface, and interface instabilities. Uniformity and yield are still issues especially in the long-wavelength infrared (LWIR) region. Nevertheless, these are leading candidates for IR photoconductive and photovoltaic detector materials in particular for military and space applications. This chapter reviews the development and applications of these materials and competitive technologies for IR detection.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.815
Threshold uncertainty score1.000

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)0.0090.001

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.

Opus teacher head0.012
GPT teacher head0.198
Teacher spread0.186 · 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