A Novel Hybrid Optical Imaging Sensor for Early Stage Short-Circuit Fault Diagnosis in Printed Circuit Boards
Bibliographic record
Abstract
The communication between the lines and contacts on the printed circuit boards (PCBs) is provided by the applied current flow.Due to thermal stress occurring in PCBs exposed to high currents, short-circuit faults (SCF) occur in PCB paths.During a quality PCB inspection before mass production, the initial occurrence time (IOT) of faults should be determined to intervene them at the earliest stage.PCBs are technological wastes that are difficult to recycle due to the diversity of material components and their difficulty of separation.By detecting the IOT of SCF at an early stage, the PCBs production can become recyclable without scrapping.Thus, the amount of PCB waste due to faulty production will be reduced.This paper proposes to diagnose the IOT of SCF that occur when currents (i.e., 8, 11.5, 13.5 Ampere) are applied to PCB paths.This process is performed using a hybrid optical imaging sensor (HOIS) in which lateral shearing digital holographic microscopy (LSDHM) is adapted to microscopic fringe projection profilometry (MFPP).In fault detection with MFPP, which is a surface detection method, the required illumination is provided by LSDHM.In thermal-based SCF diagnosis, a minimum of 36 seconds is required to reach the desired temperature (thermal saturation) for imaging while in optical inspection methods; additional time is required for the polarization process.In conventional methods, faults detection can be performed after only a visible PCB damage is occurred.In contrast, we detect the IOT of SCF in a short time of 1.1 seconds, eliminating the requirement of thermal saturation or polarization.Thanks to the HOIS, since faults are detected at an early stage, damage to the entire PCB will be prevented by repairing the faulty area before mass production.
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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 | 0.000 |
| 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 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".