IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3060-d370937.html
   My bibliography  Save this article

Patent Analysis of High Efficiency Tunneling Oxide Passivated Contact Solar Cells

Author

Listed:
  • Chieh-Wa Tsai

    (College of Engineering, National Kaohsiung University of Science and Technology, No.1, University Rd., Kaohsiung 824, Taiwan
    Metal Industries Research & Development Centre, No.1001, Kaonan Highway, Kaohsiung 811, Taiwan)

  • Tung-Kuan Liu

    (College of Engineering, National Kaohsiung University of Science and Technology, No.1, University Rd., Kaohsiung 824, Taiwan)

  • Po-Wen Hsueh

    (College of Engineering, National Kaohsiung University of Science and Technology, No.1, University Rd., Kaohsiung 824, Taiwan)

Abstract

High efficiency tunneling oxide passivated contact (TOPCon) solar cell is the traditional PN junction structure, combined the advantages of using a thin film of the passivated silicon surface to separate the metal from the silicon wafer. In this study, the patent analysis of high efficiency TOPCon solar cell is presented. The structure and process technology of TOPCon solar cell were analyzed first, which is used as the basis for the key words of the patent search. The patent management chart analysis is provided, and then the patent portfolio of the main research countries and important manufacturers on the research subject can be recognized. Moreover, the technology-function matrix analysis is used to comprehend the technical development trend of the research topic. The results indicate the TOPCon solar cell technology currently entered into the maturity stage in 2019, and the companies with the top three number of patents are LG Electronics, SunPower, and SolarCity (which was acquired by Tesla in 2016). SunPowern is the earliest patent assignee, and LG Electronics is the follower, while its patent outputs are heavily concentrated after 2014. Patent technology-function matrix found the development focus of the device-related technologies are tunneling oxide and polycrystalline silicon, with a total of 21 patents, and the development focus of process-related technologies are the process of tunneling oxide layers and the process of polysilicon film. Based on the analysis results, the future development prospects of the research topic and the direction of patent portfolio are evaluated.

Suggested Citation

  • Chieh-Wa Tsai & Tung-Kuan Liu & Po-Wen Hsueh, 2020. "Patent Analysis of High Efficiency Tunneling Oxide Passivated Contact Solar Cells," Energies, MDPI, vol. 13(12), pages 1-16, June.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3060-:d:370937
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3060/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/12/3060/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    2. Karvonen, Matti & Kässi, Tuomo, 2013. "Patent citations as a tool for analysing the early stages of convergence," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1094-1107.
    3. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    4. Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
    5. Jennifer H. Chen & Show-Ling Jang & Sonya H. Wen, 2010. "Measuring technological diversification: identifying the effects of patent scale and patent scope," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(1), pages 265-275, July.
    6. Kim, Gabjo & Bae, Jinwoo, 2017. "A novel approach to forecast promising technology through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 228-237.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin-Yun Huang & Jian-Feng Cai & Tien-Chen Lee & Min-Hang Weng, 2020. "A Study on the Development Trends of the Energy System with Blockchain Technology Using Patent Analysis," Sustainability, MDPI, vol. 12(5), pages 1-19, March.
    2. Yong-Jae Lee & Young Jae Han & Sang-Soo Kim & Chulung Lee, 2022. "Patent Data Analytics for Technology Forecasting of the Railway Main Transformer," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    3. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    4. Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    5. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Stylos, Nikolaos, 2018. "Knowledge Push Curve (KPC) in retailing: Evidence from patented innovations analysis affecting retailers' competitiveness," Journal of Retailing and Consumer Services, Elsevier, vol. 44(C), pages 150-160.
    6. Eunsuk Chun & Sungchan Jun & Chulung Lee, 2021. "Identification of Promising Smart Farm Technologies and Development of Technology Roadmap Using Patent Map Analysis," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
    7. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    8. Song, Kisik & Kim, Kyuwoong & Lee, Sungjoo, 2018. "Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 118-132.
    9. Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
    10. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    11. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.
    12. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    13. Sampaio, Priscila Gonçalves Vasconcelos & González, Mario Orestes Aguirre & de Vasconcelos, Rafael Monteiro & dos Santos, Marllen Aylla Teixeira & de Toledo, José Carlos & Pereira, Jonathan Paulo Pinh, 2018. "Photovoltaic technologies: Mapping from patent analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 215-224.
    14. Wooseok Jang & Yongtae Park & Hyeonju Seol, 2021. "Identifying emerging technologies using expert opinions on the future: A topic modeling and fuzzy clustering approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6505-6532, August.
    15. Parraguez, Pedro & Škec, Stanko & e Carmo, Duarte Oliveira & Maier, Anja, 2020. "Quantifying technological change as a combinatorial process," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    16. Zhu, Lin & Cunningham, Scott W., 2022. "Unveiling the knowledge structure of technological forecasting and social change (1969–2020) through an NMF-based hierarchical topic model," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    17. Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2019. "Identifying emerging Research and Business Development (R&BD) areas based on topic modeling and visualization with intellectual property right data," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 655-672.
    18. Seongkyoon Jeong & Jong-Chan Kim & Jae Young Choi, 2015. "Technology convergence: What developmental stage are we in?," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 841-871, September.
    19. Dziallas, Marisa & Blind, Knut, 2019. "Innovation indicators throughout the innovation process: An extensive literature analysis," Technovation, Elsevier, vol. 80, pages 3-29.
    20. Burmaoglu, Serhat & Sartenaer, Olivier & Porter, Alan, 2019. "Conceptual definition of technology emergence: A long journey from philosophy of science to science policy," Technology in Society, Elsevier, vol. 59(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3060-:d:370937. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.