Design for modular testing of a multilayer flexible wireless multisensor platform
Bibliographic record
Abstract
Smart wireless sensor systems that incorporate multiple sensors often cannot be implemented on a single chip. Advanced packaging and assembly type integrations allow for a more complex conjugation and configuration of multiple system modules implemented under different technologies together in a small tiny package. In tiny sensor systems such as these, a common challenge seen across various unique assemblies is the limited test access during assembly, allowing system verification only after completion of assembly and packaging. Conventional test point access is too large to be suitable and cost effective for testing in tiny systems, while microprobing is only appropriate for a small number of test points and prototypes. We introduce a concept of direct test points access through printed microconnectors for a system that consists of multiple stacked layers of electronics disposed on a flexible polymer carrier. The printed microconnectors provide test access to various modules of the system during assembly such that progressive verification can be performed prior to completion of the entire system. This allows early identification of failed modules leading to production cost savings. The architecture of the flexible wireless multisensor platform and design of the microconnectors are discussed. The methodology and configuration for modular testing is explained from the system and subsystem perspective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".