Bibliographic record
Abstract
ABSTRACT With today's trend in the electronics industry towards smaller and smaller components (0402 and 0201 discretes) and finer and finer lead pitches (μBGAs, Chip Scale Packages, etc.), having a tightly controlled manufacturing process is more important than ever. A key element of this is the ability to characterize the accuracy and repeatability of the Surface Mount placement equipment used in manufacturing and to be able to correct equipment that is performing poorly This paper discusses the development and deployment of a placement equipment characterization system and how it is used within an electronics manufacturing environment as a quality assurance tool. The author of this paper participated in the development of IPC-9850 Surface Mount Equipment Performance Characterization specification over the past two years. The system described within this paper has many common elements with the IPC specification and thus can serve as an example to engineers considering developing an IPC compliant system. The system that has been developed and is currently in use within Celestica Inc. makes use of a non-contact optical Coordinate Measurement Machine (CMM). Key elements of this system are described in this paper including a methodology for verifying its accuracy and repeatability. Real-life examples are also presented to illustrate this system's value in acceptance testing, root–cause problem solving and machine trouble-shooting.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".