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Production and Verification of RFID MM2 Microchip with Bump and On-Chip Antenna

2019· article· en· W2960535157 on OpenAlexaff
Azmi Hassan, İmran Ali

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsImpact
Fundersnot available
KeywordsProduction lineProduction (economics)ChipAntenna (radio)Computer scienceElectronicsBottleneckRadio-frequency identificationProduction managerReliability (semiconductor)EngineeringElectrical engineeringEmbedded systemTelecommunicationsMechanical engineeringComputer security

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.187
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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