Towards heterogeneous microsystems design-for-test in a graduate student environment
Bibliographic record
Abstract
Advances in the microfabrication of heterogeneous microsystems is enabling increasingly complex devices. Modeling, simulation and test methodologies are unable to keep pace. Custom test solutions require significant resources to implement and are often not reusable. Devices not performing as expected are difficult to diagnose. Design-for-testability techniques familiar to silicon microelectronics designers may offer solutions for validating and debugging designs. What is desirable is a system design and operational algorithm optimization in a rapid prototyping environment that incorporates design for testability considerations. The university research setting is particularly well suited for developing such an environment. In this paper, we review some of the generic tests performed on microsystems-based sensor systems by graduate students. Taking a research infrastructure perspective, we then propose improvements to proof of concept environments in universities to facilitate addition of design-for-test features into heterogeneous microsystems.
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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) | 0.000 | 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".