Exploring the Patent Landscape of Sintered Metal Technologies: An Analysis Using LLM-Based AI Patent Search

This study leveraged a proprietary Large Language Model (LLM) via app.amplified.ai, focusing on sintered metal technology patents for die-bonding uses in electronics packaging. This AI platform analyzed patent complexities, particularly examining 11 pre-selected patents with known activities on uses of silver or copper paste formulations as die-attach materials from companies like Infineon Technologies, Siemens, Nihon Superior Tanaka Kikinzoku, Heraeus, Hitachi and Alphametals. Following similarity assessments with these 11 pre-selected patents, 243 core patents were identified from the app.amplified.ai patent databases (of more than 150 million), retrieved and analyzed, resulting in six key clusters: 1) conductive adhesive bonding 2) metal particle bonding 3) porous metal bonding 4) sintered silver bonding, 5) substrate bonding methods 6) terminal management bonding. These clusters match the dominant Cooperative Patent Cooperation codes as follows: H01L24/83, H01L24/29 and H01L2224/8384; all methods related to connecting semiconductor using layer connector, and mainly with sintering technology. Finally, these clusters helped to outline the interconnections among patents, their legal statuses, and assignees from 1994 to 2024, offering valuable insights for patent analytics regarding significant patents and the main technology players for this sintered bonding

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Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1109/iemt61324.2024.10875250
Citations
1
Primary Topic
Metallurgy and Material Forming
Type
article
Field-Weighted Citation Impact
0.27

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Exploring the Patent Landscape of Sintered Metal Technologies: An Analysis Using LLM-Based AI Patent Search

Long Xu, Kim Shyong Siow, Raihana Bahru, Hing Wah Lee et al.
1 citations
Metallurgy and Material Forming
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article

Exploring the Patent Landscape of Sintered Metal Technologies: An Analysis Using LLM-Based AI Patent Search

Long Xu, Kim Shyong Siow, Raihana Bahru, Hing Wah Lee, Weijie Wang
article en
1 citations

Abstract

This study leveraged a proprietary Large Language Model (LLM) via app.amplified.ai, focusing on sintered metal technology patents for die-bonding uses in electronics packaging. This AI platform analyzed patent complexities, particularly examining 11 pre-selected patents with known activities on uses of silver or copper paste formulations as die-attach materials from companies like Infineon Technologies, Siemens, Nihon Superior Tanaka Kikinzoku, Heraeus, Hitachi and Alphametals. Following similarity assessments with these 11 pre-selected patents, 243 core patents were identified from the app.amplified.ai patent databases (of more than 150 million), retrieved and analyzed, resulting in six key clusters: 1) conductive adhesive bonding 2) metal particle bonding 3) porous metal bonding 4) sintered silver bonding, 5) substrate bonding methods 6) terminal management bonding. These clusters match the dominant Cooperative Patent Cooperation codes as follows: H01L24/83, H01L24/29 and H01L2224/8384; all methods related to connecting semiconductor using layer connector, and mainly with sintering technology. Finally, these clusters helped to outline the interconnections among patents, their legal statuses, and assignees from 1994 to 2024, offering valuable insights for patent analytics regarding significant patents and the main technology players for this sintered bonding

Northwestern Polytechnical University (CN), National University of Malaysia (MY), MIMOS (Malaysia) (MY)
Universiti Kebangsaan Malaysia
Industry, innovation and infrastructure
Openalex Percentile: Top 46%
Metallurgy and Material Forming
0.27
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Exploring the Patent Landscape of Sintered Metal Technologies: An Analysis Using LLM-Based AI Patent Search — Long Xu, Kim Shyong Siow, et al. · (2024) | TGRS Research Map | TGRS