IC testing using thermal image based on intelligent classification methods
Bibliographic record
Abstract
The goal of this thesis is to propose an algorithm which would can locate the defect IC on the PCB during their manufacturing phase based on a thermal image. A 3-dimensional PCB finite-element model is developed to estimate the temperature profile of stacked ICs. Image processing by noise removing and region of interest segmentation are applied. Two sets of feature extraction are presented; first-order histogram features and Gray Level Co-occurrence Matrix (GLCM) features. The Principle Component Analysis (PCA) method is applied to decrease the feature's extractions into smallest uncorrelated input. Three main intelligent techniques; Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to classify the thermal conditions of ICs into normal and faulty status. On validation, the proposed approach applies to do thermal testing on Arduino UNO. The experimental evaluation is performed to detect the fault condition on the real time operating PCB.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".