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Record W2512791684 · doi:10.1149/07508.0091ecst

(Invited) SiGe Applications in Automotive Radars

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

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

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
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