Laser-Induced Resistance Fine Tuning of Integrated Polysilicon Thin-Film Resistors
Bibliographic record
Abstract
In this brief, we present a novel polysilicon resistor trimming technique using a pulsed focused nanosecond laser at a fluence slightly lower than the melting threshold for polysilicon. Using this technique, we were able to trim a 4 μm ×40 μm Taiwan Semiconductor Manufacturing Company 180-nm n-doped polysilicon resistors with a 200-ppm precision. Much better precision is possible by using larger structures. The method can be applied to any CMOS process without any extra layer deposition or specific design restriction beside the fact that the laser beam must be able to reach the polysilicon structure. The high repeatability of the process allows an open-loop calibration. A complete characterization of the trimmed devices, including transverse electromagnetic and atomic force microscopy imaging as well as Raman spectroscopy, has been conducted, leading to the conclusion that a material restructuration in the grain boundaries of polysilicon, following laser irradiation, is responsible for the thin-film resistivity lowering. The stability of the polysilicon thin film, as tested by heating the device at 150°C during 1000 h, is about 1.3%, which is slightly higher than the 0.7% resistance variation for untrimmed thin films.
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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 | 0.000 | 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) | 0.001 | 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".