Dual-illumination NIR system for wafer level defect inspection
Bibliographic record
Abstract
CdZnTe is a high efficiency, room temperature radiation detection material that has attracted great interesting in medical and security applications. CZT crystals can be grown by various methods. Particularly, CZT grown with the Transfer Heater Method (THM) method have been shown to have fewer defects and greater material uniformity. In this work, we developed a proof-of-concept dual lighting NIR imaging system that can be implemented to quickly and nondestructively screen CZT boule and wafers during the manufacturing process. The system works by imaging the defects inside CZT at a shallow depth of focus, taking a stack of images step by step at different depths through the sample. The images are then processed with in-house software, which can locate the defects at different depths, construct the 3D mapping of the defects, and provide statistical defect information. This can help with screening materials for use in detector manufacturing at an early stage, which can significantly reduce the downstream cost of detector fabrication. This inspection method can also be used to help the manufacturer understand the cause of the defect formation and ultimately improve the manufacturing process.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".