Laser Package Singulation: A Promising Singulation Method for Better Package Integrity and Quality
Bibliographic record
Abstract
As the semiconductor market grows, the industry is pushing for cheaper and better process that could ensure product integrity and quality. While blade saw singulation is the dominant method in package singulation, laser has proven through various trials to be a better alternative by enhancing the quality in terms of package integrity and improving lead frame density. This technical paper looks into laser package singulation and examines its feasibility in providing a better solution in singulation. The major goal in this project is defined to maintain the rigidity and integrity of the encapsulated lead frame after singulation as required by electroplating process afterwards, while addressing the problems induced by blade saw using laser. Trials are done on DFNs using UV laser, a relatively “cold” laser which provides excellent results of devices free of delamination, burr and smear for both CSAM checking and cross-sectioning examination. Lead frame density can also be increased as the saw lane width is significantly reduced. Moreover, laser package singulation could also be a new cost-down opportunity, as consumable materials such as tape and blade which is prone to wear and tear are not required in this method. Nevertheless, the key challenges ahead in this approach are the carbonization problem due to the intense heat from laser, which can hinder the electroplating process, shorted leads and a low singulation speed. After analysis stage, these problems are found to be solved by changing the parameters of laser and by chemical surface cleaning. It is important to note that the results are interrelated to several parameters simultaneously, and thus the settings have to be optimized for the best result. The application of laser singulation can be extended to all packages. And this paper illustrates the possibility and feasibility of laser package singulation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".