A Heterogeneously Integrated 256-Element 5G Phased Array: Design, Assembly, Test
Bibliographic record
Abstract
In this paper, we present the design, assembly, and test of a heterogeneously integrated dual-polarized 256-element 5G phased array covering 24$-$30 GHz. The design is based on a 64-element antenna-in-package tile designed using an organic substrate. This work represents one of the earliest examples of a heterogeneously integrated mmWave phased array module where each tile uses chips in three different substrate technologies to perform beamforming, frequency conversion, filtering, combining/splitting, and supply decoupling functions. The paper discusses the several challenges and system trade-offs for 5G mmWave phased arrays and illustrates the advantages of heterogeneous integration at the antenna-in-package level. The paper also covers, in detail, several practical aspects of phased array module design that are not well-described in existing literature, such as power domain modeling, module assembly, antenna feedline design, polarization isolation, and tile spacing. To demonstrate the efficacy of our design choices and techniques, we present exhaustive$\pm 360^\circ$over-the-air beam characterization of the phased array antenna module demonstrating beam scanning over$\pm 70^\circ$and very low cross-polarization leakage in E-/H-planes in both polarizations and in both TX and RX modes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".