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Record W4231663537 · doi:10.1149/ma2020-02281932mtgabs

(Invited) Improved Copper Electrode Integration for Thin Film Electronics on Glass

2020· article· en· W4231663537 on OpenAlexaff
Hoon Kim, Bin Zhu, Minghuang Huang, Rajesh Vaddi, Robert G. Manley

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCopper Interconnects and Reliability
Canadian institutionsResearch & Development Corporation
Fundersnot available
KeywordsMaterials scienceOptoelectronicsAlloyTransistorDry etchingElectrical resistivity and conductivityEtching (microfabrication)InterconnectionDiffusion barrierNanotechnologyLayer (electronics)Composite materialElectrical engineeringVoltageComputer science

Abstract

fetched live from OpenAlex

Increasing demand for higher resolution and larger displays continues to push research of thin film transistor materials, process, and devices. Especially, as display size become larger and pixel density increase, the signal delay becomes more critical due to high resistance of interconnects due to longer line length and smaller current carrying cross-sections. With high refresh rate and higher definition displays, the decrease of the pulse time for each pixel requires higher conductance of the interconnects to avoid non-uniformity of display performance. Thus, Cu is used as a conductor because it has low resistivity and high reliability. Incumbent Cu interconnect technologies require adhesion layers, such as Ti, Mo, or variant alloys, due to poor adhesion of Cu to glass or SiN x barriers. However, these technologies have limits such as higher resistivity of the interconnect lines due to diffusion of adhesion or barrier metal into Cu. They can also result in under-cut during pattering due to Galvanic effects in etching chemistry. The cost of processing and fabrication is another key consideration for direction of process research. Cu interconnects with the metal adhesion layer requires two step etching process to avoid the under-cut. Thus, single Cu interconnects without the use of a metal adhesion layer is promising not only because of process simplicity, but also low processing cost. A Cu alloy is a promising approach to address the issues. Most of the Cu alloy study results have high resistivity Cu interconnects due to some residue of alloy elements in the Cu lattice or grain boundary. In this study, we demonstrate single layer Cu interconnects using thin CuMn alloy as temporally adhesion layer. After short annealing of a 10 nm CuMn alloy with 500 nm of Cu film, CuMn alloy layer is converted to pure Cu and the Mn is reacted with glass to form MnO x . The formed MnO x serves as adhesion layer, binding the thin film Cu electrode to the glass substrate. After optimization of Mn concentration, alloy thickness, annealing environment, time and temperature, a resistivity lower than 1.8 µOhm·cm of the electrode was achieved. Based on this results, one step Cu etching with low resistivity Cu interconnects is feasible. We also evaluated the effect of glass surface treatment to confirm the sensitivity of this process depending on substrate preparing conditions. This process has been shown to be very stable for different display glasses with treatments. It is also compared with that of Ti/Cu stack as reference.

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)0.000
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: none
Teacher disagreement score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0220.008

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 head0.018
GPT teacher head0.256
Teacher spread0.237 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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