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Record W2512791684 · doi:10.1149/07508.0091ecst

(Invited) SiGe Applications in Automotive Radars

2016· article· en· W2512791684 on OpenAlexaff
W. Liebl, Josef Boeck, Klaus Aufinger, D. Manger, Walter Hartner, B. Heinemann, Rudolf Lachner

Bibliographic record

VenueECS Transactions · 2016
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsInfineon Technologies (Canada)
FundersEuropean Commission
KeywordsHeterojunction bipolar transistorBiCMOSBipolar junction transistorRadarBall grid arrayCMOSElectrical engineeringTransistorEngineeringElectronic engineeringMaterials scienceTelecommunications

Abstract

fetched live from OpenAlex

An overview of the SiGe technologies used at Infineon Technologies for radar applications will be given. The production of bare-die chips started in 2009 using the bipolar technology B7HF200. Since 2012 packaged MMICs in an embedded wafer level ball grid array (eWLB) are available. Process challenges and solutions for radar chips in an eWLB package are presented. Examples of commercial SiGe radar chips in production are shown. Furthermore Infineon’s next generation BiCMOS technology B11HFC with f T of 250 GHz and f max of 370 GHz is described. In addition, significant improvements of the cut-off frequencies can be achieved by replacing the currently used double-poly self-aligned configuration by a more advanced SiGe HBT architecture like IHP’s transistor module with selective base link epitaxy. The capability of this transistor cell for future BiCMOS generations was demonstrated by integrating it into Infineon’s 130 nm CMOS process. A transistor performance with f max of 500 GHz was achieved.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0360.025

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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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 designNot applicable
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

Citations8
Published2016
Admission routes1
Has abstractyes

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