Dynamic beam-stabilized, additive-printed flexible antenna arrays with on-chip rapid insight generation
Bibliographic record
Abstract
Conformal phased arrays promise shape-changing properties, multiple degrees of freedom in the scan angle, and applications for edge computing, including devices for wearable, airborne, and seaborne platforms. However, they have suffered from two critical limitations. (1) Although most applications require on-the-move communication and sensing, prior conformal arrays have suffered from dynamic deformation-induced beam pointing errors. This work introduces a dynamic beam-stabilized processor capable of beam adaptation through on-chip real-time control of fundamental gain, phase, and delay for each element. (2) Prior conformal arrays have leveraged additive printing to enhance flexibility, but conventional printable inks based on silver are expensive, and those based on copper suffer from spontaneous metal oxidation that alters trace impedance and degrades beamforming performance. Instead, we leverage a low-cost copper molecular decomposition ink with < 0.1% variation per °C across temperature and strain, and corrects any residual deformity in real-time using the dynamic beam-stabilized processor. Demonstrating unified material and physical deformation correction, our silicon-integrated dynamic beam-stabilized processor is low-power, low-area, and easily scalable due to tile-based architecture, thereby ideal for on-device implementations. Authors present a flexible antenna array with a processor that stabilizes beams in real-time, addressing errors from dynamic deformation. It uses a cost-effective copper ink for printing, ensuring stable performance under strain and temperature changes, suitable for wearable and portable devices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".