Production and Verification of RFID MM2 Microchip with Bump and On-Chip Antenna
Bibliographic record
Abstract
Abstract This paper describes the special steps taken for the mass production of the Radio Frequency Identification (RFID) MM2 microchip. Since the MM2 is the unique semiconductor product with special on-chip antenna, it requires special production steps that might be quite different from ordinary electronics processes. In order to start the volume production, several preparation steps have to be taken in advance. These tasks are usually not included in the research and development stage. At the initial stage of the production, the production scheduling and quality management are the most important works. Since the availability of the production line varies by the demands, the production sometimes becomes very tight and it will cause the delivery shortage that is critical for customers. In this paper, the steps for the production preparation and production process are described. In order to confirm the proper operation against the process variation by lot, at least three lots (equivalent to 1 million microchips) of the wafers were produced. Very limited number of the microchips were used for the reliability test and the rest were used as the pre-production sample purposes. The uniqueness of the production steps compare to other chip production is the on-chip antenna (OCA) process i.e. build-up very small antenna coil with specified carrier frequency on top of the chips.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".