Application of a pulse programmable fiber laser to a broad range of micro-processing applications
Bibliographic record
Abstract
Spatial beam shaping has long been utilized to improve laser processes and has often generated spectacular improvements in the end results. Until recently the temporal shape of laser pulses has been limited by the design parameters of the laser cavities and shaping in the temporal domain has remained relatively unexplored. The advent of the MOPA fiber laser has opened the door to creating arbitrary temporal waveforms with shape, energy, and duration being entirely independent from the laser repetition rate and changeable “on the fly.” This new degree of freedom in the laser processing parameter space has not only enabled new and improved laser processes but provided a new tool to study the dynamics of the laser material interaction itself which can greatly speed process development. Furthermore having this flexibility allows a single laser to cover a range of process parameters that heretofore normally required using several separate laser systems. This flexibility has proven to be especially useful in the processing of materials for Photo-Voltaic (PV) applications. In this work we report on the application of temporal pulse shaping to CIGS P2 & P3 processing, CIGS P1 processing (molybdenum on glass), a-Silicon P1 processing (ZnO on glass), c-Silicon via hole drilling for emitter wrap through (EWT) and other processes using the PyroFlex 25 pulse programmable fiber laser. The temporal pulse shaping feature of the laser is demonstrated as a tool to probe the process dynamics and speed the determination of optimal process parameters. When applicable, results between the pulse shape of a traditional laser and an optimized laser pulse shapes are compared.
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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".