Chapter 5 General Discussion
5.2 Research Contributions
From the beginning of the 1990s, the business world has been talking about knowledge. Being a learning organization and driving knowledge management is the power of competition nowadays. Knowledge is cumulative experience, together with information gathered from outside sources, constituting one of a firm’s critical re-sources. Companies have been trying to find ways to gain knowledge from years of experience in such things as manufacturing, engineering and sales. They need to lo-cate, organize, transfer and leverage the knowledge throughout their entire organiza-tion.
Knowledge, as primarily tacit, is something not easily visible or expressible.
Tacit knowledge is highly personal and hard to formalize, making it difficult to com-municate or share with others (Polanyi, 1962; Winter, 1987; Hamel, 1991; Nonaka, 1994; Von Hippel, 1994; Stein and Zwass, 1995; Civi, 2000). Externalization of trait knowledge to explicit knowledge, i.e. knowledge management, is the fundamental way to approach effective organizational learning. Knowledge management is essen-tial for organizations. However, in practice, knowledge management is not always ef-fective or easy to access. In general, identification of clear and understandable goals and objectives and what is the root cause of the externalization of knowledge is diffi-cult to approach and is thus often ignored.
The contributions of this research are: first, the consolidation of talent and knowledge is essential for accessing effective organizational learning. In Part I, this study has supported the argument that individual talent is the hallmark of an organiza-tion, and that blooming industries have more resources to support organizational learning. Secondly, it was verified that the “bottle up trouble shooting” approach is a feasible way to identify clear and understandable goals and objectives. Traditionally, problem solving uses theory to identify problems first, and then finds a method to solve the problems. When the problem is too complex this kind of approach may not work. Part II of this study supports the empirical model, the “bottle up trouble shooting” method, as a feasible way to identify clear and understandable goals and objectives for semiconductor R&D. In Part II, the model proposed by this research is shown to be effective in the externalization of knowledge. Management is easier to talk about in theory than to put into practice, but in practice there is a great deal of work to be faced in the area of semiconductor manufacturing, especially in building an expert system. How to integrate expert engineers’ experience and IT system engi-neers’ specialization to compose an effective system is an important issue for consid-eration. However, in Part II of this study, the researcher successfully composes a sen-sitive model for effective externalization of knowledge and direction for R&D de-partments in semiconductor manufacturing.
5.3 Implications of Knowledge Management
Organizational learning has become an increasingly important concept and practice in today’s knowledge economy business world. Thus, learning and knowl-edge management are two key aspects of judging a successful company (Civi, 2002).
Knowledge management as a competitive asset is one of the strategies of driving or-ganizational learning. Consistent with previous research, this current study proves that the externalization of trait knowledge from explicit knowledge may enhance organ-izational learning systems and allow for the extraction of more valuable knowledge.
Moreover, applying multivariable statistics is a feasible method of fault detection and classification (FDC).
This application is one of the supportive R&D activities, and is an essential ac-tivity in R&D development. The applications and categories of using multivariable statistics depend on the input of different data segments or parameter types. In this study, the data of the trait employed can be applied as a predictor or an analyzer of semiconductor equipment. FDC is a typical application to find faults and address their attribution. It provides clear and exact information to engineers.
During processing, plasma status can be treated as a black box in a chamber. It is difficult to apply real-time metrology to understand the dynamic status of plasma.
In contrast, real-time information via applying multivariable statistical monitoring can determine deviate parameters (dimension-reduction) and classify attributions (attrib-ute-classification) to contribute a concise result. This assists R&D engineers in know-ing the details of the whole process and developknow-ing optimal process recipes.
5.4 Study Limitations and Future Research
Several limitations to this study exist. First, the sample is unrepresentative of the general population. Due to time and financial constraints, the researcher selected a convenient sample of individuals within certain companies. Thus, the results must be interpreted with considerable caution. Second, this study is based on cross-sectional
data; thus, no causal relationship should be inferred. More longitudinal studies across organizations are needed. Third, the experimental results might be restricted to extend to an extensive semiconductor manufacturing process.
Fault diagnosis and prediction of semiconductor equipment are more difficult than that of other traditional equipment due to their more complex structure. However, applying multivariable statistical monitoring can execute a tool health report in an as-signed period of time. Evaluating the optimum equipment maintenance within the process revolution can make the best use of the periodic maintenance time (PM). That is the best equilibrium of cost and time.
However, in practice, most parameters will be thrown into the model and will result in a data jam. Also, interactions within parameters can not be easily identified.
The characteristics of independent variables become ambiguous and affect the accu-racy of the model. Finally for future research, first, different methods have to be tried to fit empirical data; therefore researchers have to pay more attention to the funda-mentals of methodological theory. Second, the process segment axis identifies which process segments are selected for specific application. The parameter axis shows what parameters appear in selected process segments. This can make the model more con-cise and enhance its reliability and the validity of the analysis results.
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