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Texture Control in Manufacturing of ULSI Devices

2005· article· en· W2072548313 on OpenAlexaff
K. J. Kozaczek

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2005
Typearticle
Languageen
FieldMaterials Science
TopicCopper Interconnects and Reliability
Canadian institutionsHyperion Technologies (Canada)
Fundersnot available
KeywordsCopper interconnectMaterials scienceMicrostructureElectromigrationWaferElectroplatingTexture (cosmology)Process controlCopperOptoelectronicsComposite materialMetallurgyProcess (computing)Computer scienceLayer (electronics)

Abstract

fetched live from OpenAlex

The rapid adoption of damascene copper processing has brought about an increased need to understand and control microstructure in the barrier, seed and electroplated copper layers during manufacture. We will discuss an in-line, x-ray diffraction based metrology for rapidly characterizing thin film polycrystalline microstructures on 300 mm silicon wafers in terms of crystallographic texture, phase composition, and film thickness. The microstructure control plays an increasingly important role in improving the performance and reliability of ULSI devices that use the damascene copper technology at 0.13-µm node and below. The problems related to delamination, stress voiding, and electromigration failures could be mitigated by the selection of proper materials, processing methods, and manufacturing tools. The optimum process would result in a tailored microstructure of barrier/seed/electroplated copper aggregate. At the same time, the microstructure could be used as an internal sensor, sensitive to process excursions and providing guidance for the corrective actions. The texture and crystallographic phase data can be used as a direct measure of the deposition process in terms of film quality, reproducibility, and stability over time. The spatial distribution of crystallographic texture and phase can be measured on a single wafer in order to check wafer uniformity. More importantly, the same measurements can be carried out at predetermined intervals on wafers from a single deposition tool, and the results used to create a database that can be applied to trend charting and tool qualification. Examples of microstructure control in damascene copper processing include: process development and qualification, process control and stability, deposition tool qualification, and on-line R&D. Examples of texture control will refer to materials and processes typical of damascene copper technology for ULSI. A typical processing route includes the PVD deposition of a barrier layer and copper seed layer, followed by copper electroplate (EP), anneal, and chemical-mechanical planarization (CMP). All the processing steps affect the texture of annealed copper, and therefore affect directly the performance of interconnects.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.026
GPT teacher head0.298
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations2
Published2005
Admission routes1
Has abstractyes

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