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Enregistrement W2624381531 · doi:10.6028/nist.ir.6595

Materials reliability division, FY 2000 programs and accomplishments

2001· report· en· W2624381531 sur OpenAlexaff
Fred R Fickett, Thomas A. Siewert

Notice bibliographique

Revuenon disponible
Typereport
Langueen
DomaineEngineering
ThématiqueManufacturing Process and Optimization
Établissements canadiensUniversity of Toronto
Organismes subventionnairesNational Institute of Standards and Technology
Mots-clésDivision (mathematics)Reliability (semiconductor)Reliability engineeringComputer scienceEngineeringMathematicsArithmeticPhysics

Résumé

récupéré en direct d'OpenAlex

The Materials Reliability Division develops measurement technologies that enable producers and users of materials to improve the quality and reliability of their products and to meet the ever more stringent materials challenges in the microelectronics market.The metrology devices and concepts, and the associated materials science base, cover the range of materials from metals to polymers to ceramics.Specimen dimensions range from the microscale and nanoscale of electronic packages and their components to the massive structures found in gas pipelines and bridges.Many measurement techniques are brought to bear on the problems, ranging from traditional and advanced ultrasonic testing to advanced transmission electron microscopy, scanned-probe microscopy, and new measurements yet to be named.The Division also provides measurements and standards to support the instruments necessary for assuring the accurate determination of impact resistance of structural steels through the standard reference materials (SRM) program.In FYOO the Division focused its resources on the following research areas: Microscale Measurements: These projects develop measurement techniques for evaluating the mechanical, thermal, electrical and magnetic behavior of thin fdms and coatings at size scales typical of modern electronic chip and package structures.With our industrial partners, we used crystallographic studies with electron microscopy to clarify the mechanisms of electromigration failure.Concurrent development of a electromigration test facility with both variable frequency and variable temperature further increased our capabilities to study this critical failure mode of modern electronics.Scanned-probe microscopy is being developed as a measurement technique offering the promise of moving to even finer scales in determination of acoustic, thermal, and mechanical properties, with successful demonstrations of all modes in FYOO.This year also saw the return from industry of one staff member who had completed a successful term as a NIST Industrial Fellow at Motorola, and the departure of another for a nine-month stay at the Max Planck Institute for Metal Studies.Microstructure Sensing: In this program, ultrasonic measurements are applied to the characterization of materials on a scale extending from atomic dimensions (lattice defects), through microstructures (grains) to macrostructures (pipelines).During FYOO, emphasis shifted from development of ultrasonic techniques applicable to structural steels to similar measurements on the materials used in microelectronic devices.On the nanometer scale, the atomic force microscope was modified to measure the compliance of surfaces at this level of resolution.The laser ultrasonics and acoustic microscope facilities moved into frequencies over 100 MHz, where the acoustic wavelengths better match the dimensions of the structures used in modern microcircuit devices.As a result, we can now measure the elastic moduli of deposited films whose thickness dimensions lie in the range of 0.1 to 10 micrometers and use the results in models to describe the response of the film and substrate to environmental variables such as temperature changes and processing conditions.Also, improvements in our capabilities in acoustic-resonance spectroscopy enabled us to characterize new materials for microelectronic components, such as crystal oscillators, filters and dielectric resonators.Work at the large scale of dimensions continued with development of techniques to detect and measure residual stress and plastic deformation in large structures in the field.Process Sensing and Modeling: The projects in this area develop measurement technology for determining a material's characteristics and/or implementing real-time process control.FYOO was a period of transition, as we expanded our activities into several new directions.We noted that the material-property data for lead-free solders were widely distributed through the literature and so started a database effort.In high-energy x-ray diffraction, the techniques that we had developed to monitor the in-situ solidification of turbine blades were applied to the detection of brittle intermetallic phases in solder joints.In welding, our collaboration with the Intelligent Systems Division in Gaithersburg resulted in demonstration of remote sensing of welding problems over the world wide web for several automobile suppliers.In high-temperature deformation, the techniques developed during our studies of steels were applied to a study of the formability of aluminum, in a joint project with the Metallurgy Division. Division Chiefs Commentary:FYOO completes the first year of operation of the Materials Reliability Division under new management.The focus of the Division has changed, being directed more into the area of electronic materials research while maintaining a presence in a few of the infrastructure support efforts that had been the main activity of the Division for many years.Many staff members have worked successfully to apply their expertise to new types of materials and problems on a significantly different size scale.This report describes these activities in some detail.During the year, new equipment and facilities were procured and put in place to support our developing research areas with increased capabilities on the smaller scales inherent in our new directions.We anticipate significant accomplishments in these areas in the upcoming year.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,045
Score d'incertitude au seuil0,134

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,001
Études des sciences et des technologies0,0030,000
Communication savante0,0030,001
Science ouverte0,0020,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0400,026

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,252
Écart entre enseignants0,230 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2001
Routes d'admission1
Résumé présentoui

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