Bulk growth and surface characterization of epitaxy ready cadmium zinc telluride substrates for use in IR imaging applications
Bibliographic record
Abstract
Cadmium Zinc Telluride (Cd<sub>1-x</sub>Zn<sub>x</sub>Te or CZT) is a compound semiconductor substrate material that has been used for infrared detector (IR) applications for many years. CZT is a perfect substrate for the epitaxial growth of Mercury Cadmium Telluride (Hg<sub>1-x</sub>Cd<sub>x</sub>Te or MCT) epitaxial layers and remains the material of choice for many high performance IR detectors and focal plane arrays that are used to detect across wide IR spectral bands. Critical to the fabrication of high performance MCT IR detectors is a high quality starting CZT substrate, this being a key determinant of epitaxial layer crystallinity, defectivity and ultimately device electro-optical performance. In this work we report on a new source of substrates suitable for IR detector applications, grown using the Travelling Heater Method (THM). This proven method of crystal growth has been used to manufacture high quality IR specification CZT substrates where industry requirements for IR transmission, dislocations, tellurium precipitates and copper impurity levels have been met. Results will be presented for the chemo-mechanical (CMP) polishing of CZT substrates using production tool sets that are identical to those that are used to produce epitaxy-ready surface finishes on related IR compound semiconductor materials such as GaSb and InSb. We will also discuss the requirements to scale CZT substrate manufacture and how with a new III-V like approach to both CZT crystal growth and substrate polishing, we can move towards a more standardized product and one that can ultimately deliver a standard round CZT substrate, as is the case for competing IR materials such as GaSb, InSb and InP.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".