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Record W3113994180 · doi:10.1149/ma2020-02171487mtgabs

Electroless Deposition for Nanoscale Applications: Challenges and Opportunities

2020· article· en· W3113994180 on OpenAlexaffabout
E. J. O’Sullivan, C Camagong, C. Lavoie, Jean Jordan‐Sweet, David Muir, Marinus Hopstaken, Mahadevaiyer Krishnan

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsCanadian Light Source (Canada)
Fundersnot available
KeywordsMaterials scienceElectroless depositionWaferDeposition (geology)CoatingMetallurgyNanotechnologyOptoelectronicsCopper

Abstract

fetched live from OpenAlex

We recently developed a maskless, electroless, high-P-content, Ni(P) process to protect the final Cu bitline wiring level in our STTM MRAM test vehicles to enable functional testing in an air atmosphere at elevated temperatures for evaluation of MRAM device memory state retention. The process was developed as a replacement for the final aluminum level, and drastically shortened wafer processing cycle time. We demonstrated an electroless Ni(P) that selectively deposited on Cu bit lines with effective spacing approaching 200 nm without shorting, for coating thicknesses up to ca. 50 - 60 nm, with excellent bitline coverage (no pinholes). This was achieved through optimization of Cu surface cleanliness, surface catalyzation using an acidic Pd solution, and finally electroless Ni(P) deposition (1). Testing (electrical resistance and magnetoresistance (R & MR)) of Ni(P)-coated wafers, showed virtually unchanged R & MR for MRAM 4Kb arrays encompassing a large range of device critical dimensions (CDs). Figure 1 shows a topdown SEM image of a region of electroless Ni(P)-coated Cu bitlines. While electroless deposition is capable of depositing only a limited number of metals and alloys compared to electrodeposition, it is often easier to obtain coatings of uniform thickness and composition, since one does not have the current density uniformity problem of electrodeposition. Although it is not always possible to obtain pure materials using electroless deposition, nevertheless, materials with unique properties, such as Ni(P) (corrosion resistance), and Co(P) (magnetic properties) and related alloys e.g., Co(W)(P), are readily obtained by electroless deposition methods. These materials may be crystalline or amorphous, and the crystallization kinetics of the latter type may be strongly dependent on film thickness, and have a two-dimensional character due to interfacial effects. It is important to understand how such materials transform upon heating, given that back-end-of-line (BEOL) processing may involve temperature excursions to 400 ⁰C. We will discuss in detail the kinetics of crystallization of our high-P-content, amorphous, electroless Ni(P) films on Cu thin films using synchrotron X-Ray diffraction analysis (2). For 1 um thick Ni-P films deposited on Cu/Ta/TaN underlayer, as we increase the rates at which the temperature is raised during ramp anneals, crystallization temperatures vary from about 360C to 400C from which we extract an activation energy of 3.4 eV for this crystallization. The in-situ data also shows a gradual reduction in lattice constant for the underlying Ta layer. We will complement the synchrotron X-Ray analysis study results with TEM, SIMS and X-ray fluorescence (XRF) results of Ni(P) film composition for unpatterned and patterned (sub-micron) features. In addition to the subtlety of its mechanisms and range of solution formulations, electroless deposition has much to offer in terms of niche applications, which explains why it is often turned to first by researchers seeking creative deposition methods in the still emerging field of nanofabrication. Given the wide range of materials involved in electroless deposition, whether it is the final product, or the choice of a reasonable reducing agent, the mechanism of electroless deposition tends to be complex. This talk will include a discussion of the mechanism of electroless deposition, and whether the sought after, one-mechanism-fits-all approach, is tenable. [1]. E. J. O'Sullivan et al 2019 Meet. Abstr. MA2019-02 916; https://doi.org/10.1149/MA2019-02/15/916 [2]. Canadian Light Source Inc., 44 Innovation Boulevard Saskatoon, SK S7N 2V3, Canada. Acknowledgements The authors gratefully acknowledge the efforts of the staff of the Microelectronics Research Laboratory (MRL) at the IBM T. J. Watson Research Center, where some of the fabrication work described in this talk was carried out. Part of the research described in this paper was performed at the Canadian Light Source, a national research facility of the University of Saskatchewan, which is supported by the Canada Foundation for Innovation (CFI), the Natural Sciences and Engineering Research Council (NSERC), the National Research Council (NRC), the Canadian Institutes of Health Research (CIHR), the Government of Saskatchewan, and the University of Saskatchewan.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.031
GPT teacher head0.224
Teacher spread0.194 · 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 designNot applicable
Domainnot available
GenreReview

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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Citations0
Published2020
Admission routes2
Has abstractyes

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