Evaluating the Manufacturability and Operational Costs for New Conformal Coating Processes
Bibliographic record
Abstract
ABSTRACT There are test vehicles to address SMT assembly process development and optimization, but none to address Conformal Coating operations. To fill this gap, Celestica has designed the “CC-Tango” test vehicle. The continued migration in the electronics industry to higher density components and smaller footprint layouts makes the Conformal Coating process more challenging in terms of achieving acceptable first pass process yields and cycle times that are cost effective. The “CC-Tango” test vehicle can be used to assess the assembly processes for cleaning, masking and inspection/rework requirements and their effect on Conformal Coating applications. These are some of the main features that require further investigation for manufacturability optimization. This information is critical for Aerospace, Military, Industrial customers and any other products that may be exposed to harsh environmental conditions with either lead free or mixed conditions requiring Conformal Coating. This paper describes how we have used this test vehicle to evaluate Conformal Coating materials, compare application equipment along with providing site enablement and optimization methods for the conversion to high reliability/minimized cost Conformal Coating processes. Seven assembly process variables were investigated. The three most critical variables were identified and their impacts will be discussed. Practical manufacturing techniques that maximize production “Return on Invested Capital” ROIC will also be discussed. IPC-CC-830 [1] and ASTM D3359 [2] standards were used to execute the test plan.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".