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Record W3126335553 · doi:10.1109/tcsii.2021.3054739

An Active Bandpass Filter for LTE/WLAN Applications Using Robust Active Inductors in Gallium Nitride

2021· article· en· W3126335553 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsCarleton University
FundersCMC Microsystems
KeywordsInductorBand-pass filterActive filterMaterials scienceGallium nitrideFilter (signal processing)OptoelectronicsElectronic engineeringInductanceElectrical engineeringEngineeringNanotechnology

Abstract

fetched live from OpenAlex

This brief demonstrates the performance of the first active bandpass filter to implement an active inductor in gallium nitride (GaN) technology. Fabrication of the filter and inductor was done using a 0.5 μm pHEMT GaN process with the system implemented on one 2 mm by 2 mm die. The tuning range of the active filter was measured to be 749 MHz at a centre frequency of 3.39 GHz with separate amplitude and quality factor tuning. An amplitude range for S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> was measured to be from -12.2 dB to 13.7 dB within the operating frequency range. Taking advantage of the active inductor's negative impedance the filter was able to produce a quality factor of 138. On-chip, the active inductor occupies an area of 350 μm by 175 μm compared to approximately 350 μm by 350 μm for their passive counterparts of similar inductance (a 50% decrease in size). The filter was designed for LTE/WLAN applications to demonstrate the capabilities of the active inductor.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.039
GPT teacher head0.275
Teacher spread0.236 · 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