Reducing The Environmental Impact Of Cleaning Electronic Assemblies: A Case Study
Bibliographic record
Abstract
ABSTRACT Modern factories are constantly striving to find ways to manufacture electronics in an environment where world wide demand is increasing and resource availability is diminishing. For these and other reasons “environmental citizenship” has become a stated goal of many governments and corporations. The US government has introduced many laws regulating and controlling environmental issues and more legislation is expected. Starting with ratification of the Montreal Protocol, the electronics industry began to shift away from cleaning as a standard part of the assembly process. A no-clean assembly process became the standard in the late 1990’s. In the recent decade, product performance and reliability concerns have increased the need to clean electronic packages being manufactured. Cleaning is now making a comeback. Today, the cleaning agent of choice is an aqueous-based solvent blend as opposed to vapor degreasing solvent of the 1970’s and 1980’s. Cleaning equipment is often one of the largest consumers of power, water, chemicals, and over-all floor space on electronics manufacturing lines. This paper focuses on cleaning with aqueous-based chemical mixtures. These aqueous-based mixtures usually have an alkaline reactive constituent commonly called a saponifier. These mixtures require heat and are used in air spray batch and inline cleaning systems. Current realities and anticipated new regulations regarding use and availability of power, water, chemicals and waste disposal need a new evaluation. These systematic changes will not happen just because industry is legislated to do so. Cooperation and compliance will happen quickly if industry can see a payback. In this case the payback comes primarily from power and water savings. This saving quickly pays for the hardware necessary to accomplish this transition. The payback time can be calculated from the developed cost model developed in this study. Anticipated production environmental challenges in the next decade will likely include; power shortages, water shortages, new regulations on waste water disposal and tighter restriction of VOC usage to name a few.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".