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SiMiC: Context-aware silicon microstructure characterization using attention-based convolutional neural networks for field-emission tip analysis.

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Title: SiMiC: Context-aware silicon microstructure characterization using attention-based convolutional neural networks for field-emission tip analysis.
Authors: Tan, Jing Jie1,2, tanjingjie@1utar.my, Schreiner, Rupert2, Hausladen, Matthias2, Asgharzade, Ali2, Edler, Simon3, Bartsch, Julian3, Bachmann, Michael3, Schels, Andreas3, Kwan, Ban-Hoe1, Ng, Danny Wee-Kiat1, Hum, Yan-Chai1
Source: Journal of Vacuum Science & Technology: Part B-Nanotechnology & Microelectronics; Nov2026, Vol. 43 Issue 6, p1-9, 9p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 190463456
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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      – Type: doi
        Value: 10.1116/6.0005068
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      – Code: eng
        Text: English
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      – TitleFull: SiMiC: Context-aware silicon microstructure characterization using attention-based convolutional neural networks for field-emission tip analysis.
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            NameFull: Tan, Jing Jie
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            – D: 01
              M: 11
              Text: Nov2026
              Type: published
              Y: 2025
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              Value: 43
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            – TitleFull: Journal of Vacuum Science & Technology: Part B-Nanotechnology & Microelectronics
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