特征分析视角下半导体制造产业关键技术分布研究
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摘要
本文从产业关键技术特征分析的视角出发,构建两阶段递进型产业关键技术识别分析框架,利用GN共被引社区发现算法对半导体制造领域基础性技术主题的识别及发展趋势进行分析,并在此基础上借助动态专利组合分析模型进一步对半导体制造领域关键技术的分布进行研究。最终发现,当前半导体制造产业的关键技术主要聚焦在光伏半导体制造技术、存储器制造技术、半导体打印制造工艺以及SIP封装技术;产业内技术创新整体的活跃性呈下降态势,但技术之间相互关联程度逐渐增高,且技术发展方向不断聚焦;此外,半导体制造产业在2007—2011年处于技术变革期,原有产业技术结构出现较大变动,新兴技术逐渐吸引产业更多的关注,而2012—2016年则在整体上进入技术深化期,产业技术结构进一步稳固。
From the perspective of analysis on industry key technology characteristics,a two stage progressive identification analysis framework for industry key technology was proposed in this paper. Based on this framework,the identification and development trend of basic technical topics in semiconductor manufacture industry was analyzed by Girvan-Newman clustering algorithm for Patent co-citation. Then on the basis of these basic technical topics,the key technology distribution of semiconductor manufacture industry was further studied by dynamic patent portfolio analysis model. The result shows that present key technologies of semiconductor manufacture industry mainly focus on" photovoltaic semiconductor manufacture technology"," memory semiconductor manufacture technology ","semiconductor printing manufacture process"and"SIP packaging technology". The overall activity of technological innovation in semiconductor manufacture industry is declining,but on the contrary,the degree of interrelation between technologies in semiconductor manufacture industry is increasing,and the development direction of technology is constantly focusing. In addition,the technology changes of semiconductor manufacture industry are in progress in 2007—2011 period. The technology structure of industry is changed greatly,and emerging technologies attracted more attention from the industry. But in the period of 2012—2016,the semiconductor manufacture industry enters the technical strengthening period on the whole. The technology structure of industry becomes more stable.
From the perspective of analysis on industry key technology characteristics,a two stage progressive identification analysis framework for industry key technology was proposed in this paper. Based on this framework,the identification and development trend of basic technical topics in semiconductor manufacture industry was analyzed by Girvan-Newman clustering algorithm for Patent co-citation. Then on the basis of these basic technical topics,the key technology distribution of semiconductor manufacture industry was further studied by dynamic patent portfolio analysis model. The result shows that present key technologies of semiconductor manufacture industry mainly focus on" photovoltaic semiconductor manufacture technology"," memory semiconductor manufacture technology ","semiconductor printing manufacture process"and"SIP packaging technology". The overall activity of technological innovation in semiconductor manufacture industry is declining,but on the contrary,the degree of interrelation between technologies in semiconductor manufacture industry is increasing,and the development direction of technology is constantly focusing. In addition,the technology changes of semiconductor manufacture industry are in progress in 2007—2011 period. The technology structure of industry is changed greatly,and emerging technologies attracted more attention from the industry. But in the period of 2012—2016,the semiconductor manufacture industry enters the technical strengthening period on the whole. The technology structure of industry becomes more stable.
引文
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[25]ZHANG A H,ZHENG M X,PANG B. Structural diversity effect on hashtag adoption in Twitter[J].Physica A:statistical mechanics and its applications,2018,493:267-275.
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[27]LEYDESDORFF L. On the normalization and visualization of author co-citation data:Salton's cosine versus the Jaccard index[J].Journal of the American society for information science and technology,2008,59(1):77-85.
[28]ERNST H. Patent portfolios for strategic R&D planning[J]. Journal of engineering and technology management,1998,15(4):279-308.
[29]YUE X P. Behavior of inter-enterprises patent portfolio for different market structure[J]. Technological forecasting and social change,2017,120(7):24-31.
[30]HUANG J Y. Patent portfolio analysis of the cloud computing industry[J].Journal of engineering and technology management,2016,39:45-64.
[31]张世玉,王伟,陶成琳,等.企业技术威胁预警模型构建——基于专利组合分析的视角[J].情报杂志,2016,35(11):70-74.
[32]JEONG K H,NOH H Y,SONG Y K,et al. Essential patent portfolios to monitor technology standardization strategies:case of LTE-a technologies[J].Journal of engineering and technology management,2017,45:18-36.
[33]KYEBAMBE M N,CHENG G,HUANG Y Q,et al. Forecasting emerging technologies:a supervised learning approach through patent analysis[J].Technological forecasting and social change,2017,125(12):236-244.
[34]NEFF M W,CORLEY E A. 35 years and 160000 articles:a bibliometric exploration of the evolution of ecology[J].Scientometrics,2009,80(3):657-682.
[2]WANG C T,CHIU C S. Competitive strategies for Taiwan's semiconductor industry in a new world economy[J].Technology in society,2014,36:60-73.
[3]HUNG S W,HE D S,LU W M. Evaluating the dynamic performances of business groups from the carry-over perspective:a case study of Taiwan's semiconductor industry[J].Omega,2014,46:1-10.
[4]WU C H,DING C G,JANE T D,et al. Lessons from the global financial crisis for the semiconductor industry[J].Technological forecasting and social change,2015,99:47-53.
[5]ZHAO X G,ZHANG Y. Technological progress and industrial performance:a case study of solar photovoltaic industry[J].Renewable and sustainable energy reviews,2018,81(1):929-936.
[6]TSAI B H,CHEN H W. Innovation characteristics,industrial clusters,and intra-industry spillover effects in integrated circuit industry[J].International journal of innovation and technology management,2013,10(4):1-20.
[7]WANG C C,SUNG H Y,CHEN D Z,et al. Strong ties and weak ties of the knowledge spillover network in the semiconductor industry[J].Technological forecasting and social change,2017,118(5):114-127.
[8]刘晓燕,阮平南,童彤.专利合作网络知识扩散影响因素分析:以集成电路产业为例[J].中国科技论坛,2013(5):125-130.
[9]LOU C C,LEE T P,GONG S C,et al. Effects of technical innovation on market value of the U. S. semiconductor industry[J].Technological forecasting and social change,2010,77(8):1322-1338.
[10]刘云,闫哲,程旖婕,等.基于专利计量的集成电路制造技术创新能力分布研究[J].研究与发展管理,2016,28(3):47-54.
[11]RAJAH R,RAFAT B S,YAP X S. Institutional support,innovation capabilities and exports:evidence from the semiconductor industry in Taiwan[J].Technological forecasting and social change,2016,109:69-75.
[12]LEVITAS E F,MCFADYEN M A,LOREE D. Survival and the introduction of new technology:a patent analysis in the integrated circuit industry[J].Journal of engineering and technology management,2010,15(3):225-246.
[13]TSAI C H,WU H Y,CHEN I S,et al. Exploring benchmark corporations in the semiconductor industry based on efficiency[J].The journal of high technology management research,2017,28(2):188-207.
[14]RAHUL K,PATIA G M. Unmasking the interplay between technology evolution and R&D collaboration:evidence from the global semiconductor manufacturing industry,1990—2010[J].Research policy,2014,43(3):555-569.
[15]ESMAELIAN M,TAVANA M,CAPRIO D D,et al. A multiple correspondence analysis model for evaluating technology foresight methods[J].Technological forecasting and social change,2017,125(12):188-205.
[16]杨艳萍,董瑜,韩涛.基于专利共被引聚类和组合分析的产业关键技术识别方法研究——以作物育种技术为例[J].图书情报工作,2016,60(19):143-148.
[17]KIM G J,BAE J W. A novel approach to forecast promising technology through patent analysis[J].Technological forecasting and social change,2017,117(4):228-237.
[18]ALTUNTAS S,DERELI T,KUSIAK A. Forecasting technology success based on patent data[J]. Technological forecasting and social change,2015,96(7):202-214.
[19]张丰,刘啦,缪小明.燃料电池研发的热点领域与关键技术:基于专利数据的实证研究[J].情报杂志,2017,36(1):54-58.
[20]BOUGUETTAYA A,YU Q,LIU X M,et al. Efficient agglomerative hierarchical clustering[J].Expert systems with applications,2015,42(5):2785-2797.
[21]GIRVAN M,NEWMAN M E J. Community structure in social and biological networks[J].Proceedings of the national academy of sciences of the United States of America,2002,99(12):7821-7826.
[22]CHEN H W,HU Y C. Finding community structure and evaluating hub road section in urban traffic network[J].Procedia social and behavioral sciences,2013,96:1494-1501.
[23]张维冲,李文绚,杨冠灿,等.基于社会网络分析的高校分类研究——以“211”高校为例[J].情报杂志,2016,35(8):65-70.
[24]苏学能,刘天琪,王彪,等.基于改进GN分裂算法的电磁环网分区及方案评估方法[J].中国电机工程学报,2017,37(6):1686-1695.
[25]ZHANG A H,ZHENG M X,PANG B. Structural diversity effect on hashtag adoption in Twitter[J].Physica A:statistical mechanics and its applications,2018,493:267-275.
[26]NEWMAN M E J,GIRVAN M. Finding and evaluating community structure in networks[J].Physical review E:statistical nonlinear and soft matter physics,2004,69.
[27]LEYDESDORFF L. On the normalization and visualization of author co-citation data:Salton's cosine versus the Jaccard index[J].Journal of the American society for information science and technology,2008,59(1):77-85.
[28]ERNST H. Patent portfolios for strategic R&D planning[J]. Journal of engineering and technology management,1998,15(4):279-308.
[29]YUE X P. Behavior of inter-enterprises patent portfolio for different market structure[J]. Technological forecasting and social change,2017,120(7):24-31.
[30]HUANG J Y. Patent portfolio analysis of the cloud computing industry[J].Journal of engineering and technology management,2016,39:45-64.
[31]张世玉,王伟,陶成琳,等.企业技术威胁预警模型构建——基于专利组合分析的视角[J].情报杂志,2016,35(11):70-74.
[32]JEONG K H,NOH H Y,SONG Y K,et al. Essential patent portfolios to monitor technology standardization strategies:case of LTE-a technologies[J].Journal of engineering and technology management,2017,45:18-36.
[33]KYEBAMBE M N,CHENG G,HUANG Y Q,et al. Forecasting emerging technologies:a supervised learning approach through patent analysis[J].Technological forecasting and social change,2017,125(12):236-244.
[34]NEFF M W,CORLEY E A. 35 years and 160000 articles:a bibliometric exploration of the evolution of ecology[J].Scientometrics,2009,80(3):657-682.