Extracting RLC Parasitics From a Flexible Electronic Hybrid Assembly Using On-Chip ESD Protection Circuits
Bibliographic record
Abstract
The presence of RLC line parasitics in a flexible hybrid electronic assembly can lead to signal integrity issues, and their progression over time can lead to catastrophic failures. A technique for extracting the RLC line parasitics from a flexible hybrid electronics assembly is presented. The proposed extraction method exploits the on-chip ESD protection circuits of an IC chip to extract the parasitics of the printed conductors bonded to the chip. This is performed through a single test access port, i.e., two test points. While the parasitics LC are extractable through one-port reflection-based techniques such as time domain reflectometry; the parasitic R requires a two-port measurement such as Kelvin test, which is extremely difficult to perform for printed conductors bonded to small surface-mount IC package devices. The accuracy of the extracted RLC parameters with the proposed method are verified with a prototype developed on a rigid FR4 substrate. Subsequently, the proposed technique is utilized to track the variation of the RLC parasitics for prototypes developed on Kapton Polyimide substrate subjected to different forms of bending.
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.
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) | 0.000 | 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) | 0.001 | 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 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".