Detection of Counterfeit Accelerometer ICs Using Clustering and Unsupervised Machine Learning of Allan Variance
Bibliographic record
Abstract
This paper presents a method for detecting counterfeit accelerometer integrated circuits (ICs) by analyzing their noise signatures. The method utilizes Gaussian Mixture Model (GMM) clustering, a probabilistic machine learning algorithm, to group accelerometers based on their Allan Variance characteristics and identify anomalous clusters that may contain counterfeit components. The potential of this technique is explored using experimental data from two types of consumer-level accelerometers. Presented results suggest that the proposed approach can accurately differentiate between accelerometer parts with high probability, based solely on noise properties of the parts. The proposed method can be included in the manufacturing process flow as an additional non-destructive testing method to probabilistically identify non-authentic parts. The cut-off threshold for detection of counterfeit parts is a user-chosen probability value. Lastly, the benefits and drawbacks of the method, as well as potential avenues for future research, are discussed.
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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 | 0.001 |
| 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) | 0.000 | 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".