Common-mode-rejection demodulation lock-in technique for high-resolution characterization of ion implantation in silicon wafers
Bibliographic record
Abstract
In this article, we present the use of frequency-scan and lock-in common-mode-rejection demodulation (CMRD) laser photothermal radiometry to the study of B+, P+, and As+, ion implanted silicon wafers, with and without surface-grown oxides. The implantation energy of the wafers was 100 keV in all the wafers and doses ranged between 1×1011–1×1013 ions/cm2. The CMRD technique is a new demodulation method that was tested after a theoretical study and its implementation in hardened Zr–2.5Nb samples. This technique is applied to silicon ion-implantation monitoring and we report a superior signal resolution in dose range where the conventional frequency scans essentially overlapped: B+ implants in the dose range 1×1012–1×1013 ions/cm2, and P+ implants in the 1×1011–1013 ions/cm2 range. In all other cases where conventional frequency scans could resolve implantation doses, CMRD did not present any significant resolution advantages. It was further established that the pulse separation increment δΔ is the critical CMRD wave form parameter, which controls dose resolution through substantial signal background and noise suppression. The dose resolution improvements afforded by the CMRD technique may be important toward better control of the ion-implantation process in electronic devices, in a dose range which has traditionally been difficult to monitor optically owing to the effects introduced by the early stages of the amorphization process in the implanted layer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".