A process innovation mode of semiconductor integrated circuit products based on the integration of Internet of things and sensing big data

Abstract

OBJECTIVE:

At present, Internet of things (IoT) technologies such as radio frequency identification devices (RFID) have been widely used. In such context, manufacturing industries of semiconductor integrated circuits have gradually realised that it is necessary to integrate IoT technologies such as RFID with other systems of technologies. The research provides a new idea for the deep integration of IoT technologies including RFID with new-generation information technologies (IT) such as big data and deep learning and drives IoT technologies to develop with multiple functions and diverse types.

DESIGN, METHODOLOGY, and APPROACH:

The research takes the manufacturing enterprises of semiconductor integrated circuits as the objects and integrates IoT technologies involving RFID and intelligent sensors with new-generation IT technologies such as big data, sensing database, deep learning, knowledge discovery, etc. On this basis, a process innovation mode based on the integration of IoT and sensing big data is established. Based on the characteristics of sensing big data of enterprises manufacturing semiconductor integrated circuits, the innovation mode is designed in three stages including: data integration and fusion, data optimisation and modelling, and data application. Moreover, the system architecture, key technologies, modelling process, and algorithm design are also analysed.

FINDINGS:

A process innovation mode of semiconductor integrated circuit products based on the integration of IoT and sensing big data is provided.

Keywords

Process innovation mode semiconductor integrated circuit product sensing big data internet of things

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References

Adrien,

, Joseph,

, Donald,

, Liles. (2000). A soft-systems methodology approach for product and process innovation, IEEE Transactions on Engineering Management, 47(3), 379–92.

Bello-Orgaz,

, Jung,

J.J.

, Camacho,

(2016). Social big data: Recent achievements and new challenges, Information Fusion, 28, 45–59.

Bevilacqua,

, Ciarapica,

F.E.

, Diamantini,

, Potena,

Domenico

(2017). Big data analytics methodologies applied at energy management in industrial sector: A case study, International Journal of RF Technologies, 8(3), 105–22.

Bottani,

, Montanari,

, Romagnoli,

(2016). Improving sales turnover in fashion retailing by means of an rfid-based replenishment policy, International Journal of Rf Technologies Research & Applications, 7(1), 65–86.

Chen,

, Mao,

, Liu

(2014). Big data: A survey, Mobile Networks & Applications, 19(2), 171–209.

Gao,

, Bi,

K. X.

(2011). Study on the forming path of process innovation capability in manufacturing enterprise based on informatization level, Science of Science and Management of S & T, 32(10), 42–9.

Huang,

,et al. (2016). Current status, future developments and recent patents on big data technology in process control systems, Recent patents on Mechanical Engineering, 9(2), 112–24.

Jian,

W. T.

, Guo,

, Zhang,

Q.H.

(2016). Quality reliability and innovation in semiconductor manufacturing. Beijing: Publishing House of Electronics Industry.

Lee,

, Ni,

, Wang,

Z. A.

(2016). From big data to intelligent manufacturing. Shanghai: Shanghai Jiaotong University Press.

10.

Lee,

, Lapira,

, Bagheri,

, et al. (2013). Recent advances and trends in predictive manufacturing systems in big data environment, Manufacturing Letters, 1(1), 38–41.

11.

Lee,

, Siegel,

, Lapira,

E. R.

(2013). Development of a predictive and preventive maintenance demonstration system for a semiconductor etching tool, Ecs Transactions, 52(1), 913–927.

12.

Li,

, Song,

, Huang,

(2016). A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics, International Journal of Advanced Manufacturing Technology, 84(1-4), 119–131.

13.

Liu,

S. L.

(2012). Application of RFID technology in traceability and risk warning for food safety. Beijing: Science Press.

14.

Lu,

, et al. (2016). Study on yield improvement of semiconductor process based on big data analysis, Journal of Nantong University (Natural Science Edition), 15(4), 17–21.

15.

Rizzi,

, Romagnoli,

, Thiesse,

(2016). A new framework for RFID use cases in fashion and apparel retailing, International Journal of Rf Technologies Research & Applications, 7(2-3), 105–29.

16.

Scalia,

G. L.

, et al. (2016). Predictive shelf life model based on RF technology for improving the management of food supply chain: A case study, International Journal of Rf Technologies Research & Applications, 7(1), 31–42.

17.

Tan,

J. R.

, Liu,

Z. Y.

(2017). Intelligent manufacturing: Key technology and application Beijing: China Machine Press.

18.

Tao,

, Zuo,

, Xu,

L. D.

, et al. (2014). IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing, IEEE Transactions on Industrial Informatics, 10(2), 1547–57.

19.

Wang,

G. L.

, et al. (2017). IoT-based big data processing technology and practice. Beijing: Publishing House of Electronics Industry.

20.

Wang,

et al. (2016). Product quality management based on complex networks theory, Complex Systems and Complexity Science, 13(2), 44–52.

21.

Xu,

H. W.

, et al. (2019). Wafer yield prediction method based on improved continuous deep belief network. Computer Integrated Manufacturing Systems. http://kns.cnki.net/kcms/detail/11.5946tp.20190315.0936.028.html

22.

Zhang,

, et al. (2016). Manufacturing big data. Shanghai: Shanghai science & technology press.