Electrostatic discharge (ESD) improvement to reduce customer complaint
Bibliographic record
Abstract
Electrostatic discharge (ESD) / electrical over stress (EOS) factors cannot be avoided when an organization running daily manufacturing activity. ESD problems are increasing in the electronics industry because of the trends toward higher speed and smaller device sizes which normally these devices are easy to be damaged by ESD. One of the manufacturing company in Malaysia which produces car radio is also facing the same problem. The failure trend related to ESD is become an alarming condition at 0 km (the product still at automotive customer assembly - 0 mileages which the vehicle is not yet sell to end user). In this study, the problem related to ESD and improvement to reduce the failure were conducted. These includes improvements in ESD protection and control which minimize yield losses and 0 km failures, and maintain the company reputation as a supplier of high-quality and reliable products. Findings from this study showed that virtually all materials, even conductors, can be triboelectrically charges. The level of charge is affected by material, speed of contact, separation and humidity. Electrostatic discharge can occur throughout the manufacturing test, handling and operational process. The improvement activity consist of two phases which is 3rd quarter 2009 (July – September) and 4th quarter 2009 (October – November). This followed by the continuous improvement activity take place on the 1st quarter 2010 (January – March). With this improvement, failure trend at customer related to ESD showed a reducing pattern.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.012 | 0.003 |
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".