An Optimization Driven Approach for Designing Touch Sensor Panels for Integration With Antennas
Bibliographic record
Abstract
Objective: We present a systematic method of integrating 5G printed antennas with bezel-less capacitive touch sensor panels (TSPs). Need: With these displays, only the space underneath the TSP is available for antenna integration. But, the electrodes of contemporary TSPs are so closely packed that they reflect impinging electromagnetic (EM) waves, distorting the radiation patterns of the antenna placed underneath. Thus, the system requires more power to compensate for these reflections and maintain high quality data transfer. Method: We utilize pattern search optimization to design the touch sensing electrodes as frequency selective surfaces with a pass-band at the operating frequency of the antenna. We carry out the optimization initially at 4.7GHz to demonstrate its advantages with respect to antenna integration. We fabricated the optimized TSP and measured its transmission and reflection coefficients for a normally-incident plane wave. The designed TSP is broadband and has a measured bandwidth of 8.4% at 4.76GHz. We explore the optimization process further for other frequencies in the 5G-NR frequency range 1 spectrum (4GHz, and 5.5GHz) to show its robustness. Touch performance: We evaluate the touch sensing response of the optimized TSP using quasielectrostatic simulations. Results: We designed, optimized and realized a TSP for a 4.7GHz antenna. We integrated the optimized and conventional TSPs with a printed dipole antenna and measured the radiation patterns of the antenna underneath the TSP at 4.7 GHz. The measured radiation pattern for the optimized TSP case was nearly identical to the free-space pattern of the dipole. With our proposed solution, antennas can claim the space underneath the TSP which is paramount for 5G wireless standards that require multiple antennas for intelligent radios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".