An online integrated operational decision support system with predefined knowledge base for semiconductor industry
Bibliographic record
Abstract
We have developed an online predefined knowledge-based decision support system for AMID (Thailand) Ltd. It is a single interactive, computer based decision-making system for a large variety of operations, tasks, and conditions, which don't require special programming skills for maintenance. AMD is dealing in the manufacture of flash memory with a variety of devices. Each device has several common or device specific rules. Each production lot has to pass through a series of different engineering rules to get released. Each rule is defined by an experienced engineer and shared in a centralized common database. Each device has got a different program as per the type of package and the type of booting. If we combine all variables the scenario will become very complicated. For e.g. Millions of flash memory/week, thousands of lots/week, hundreds of test program, devices, packages and a series of common and device specific rules of each device to be applied on each lot. The system has to make several calculations on production data to validate each rule applied on that device. Secondly, the fault diagnosis and the decisions should come online immediately after such an event. The system has proved to be an error free system and the best decision making tool on production environment; so this paper describes a deep-knowledge base management and use of online decision support system shell for diagnosing faults in manufacturing operations of the semiconductor Industry with real world application.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".