MétaCan
Menu
Back to cohort
Record W7117671699 · doi:10.1016/j.chip.2025.100186

System-level simulation of antimonide focal plane arrays

2025· article· en· W7117671699 on OpenAlexfundno aff
Zhongxian Wang, Hongyue Hao, Dongmei Li, Zhigang Song, Xiang Li, Ming Liu, Chuanbo Li

Bibliographic record

VenueChip · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaCanadian Anesthesiologists' Society
KeywordsFocal Plane ArraysCardinal pointDetectorNoise (video)Indium antimonideTime delay and integrationMiniaturizationMultiphysicsOptical transfer function

Abstract

fetched live from OpenAlex

A comprehensive multiphysics simulation framework was developed to systematically assess the key performance metrics of antimonide-based infrared detectors across various pixel structures and operating conditions. The study focuses on modulation transfer function (MTF), optical and electrical crosstalk, and noise equivalent temperature difference (NETD). Simulation results reveal that lateral carrier diffusion is the dominant factor contributing to MTF degradation. Moreover, parameters such as fill factor, bias voltage, pixel size, and integration time exhibit complex interdependencies that significantly affect overall device performance. The analysis further highlights the intricate interplay between structural design and thermal noise constraints, which is especially pronounced in dual-band detectors. This work provides a systematic understanding of the intrinsic influence of structural parameters on detector performance and offers theoretical guidance for the miniaturization and high-performance integration of antimonide-based infrared focal plane arrays.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueChipSame topicAdvanced Semiconductor Detectors and MaterialsFrench-language works237,207