Small and Medium Sized Enterprises (SMEs) were founded to improve the activity and effectiveness of small industries, to provide economic and social needs of the country, to increase the competitive level of the country, and to establish integration in the industry. In today&rsquo / s competition conditions, SMEs should continuously improve themselves / otherwise, they could lose their market shares. One of the major problems encountered in Turkish SMEs is poor quality activities / especially, not being able to exploit the Statistical Process Control (SPC) techniques. Production runs become shorter and shorter, and the product variety seems to be ever increasing, which cause short production runs. Using traditional control charts for short production runs can yield wrong and costly results. Instead of traditional control charts, short run charts such as Difference Charts (DNOM), Zed Charts, and Zed-Star Charts should be preferred.For this purpose, software that not only constructs short run control charts but also implements charts by tests to solve the problems of SMEs is developed. A Control Chart Selection Wizard, which is capable of emulating human expertise in finding a suitable control chart according to the user response for different cases is developed and added as a subprogram. Software was tested at Ar&ccedil / elik Dishwasher Plant in Ankara. The overall evaluation of the developed software, as regards the user, was satisfactory. The software can meet some requirements of the SMEs.

Online process monitoring and feedback control are two widely researched aspects that can impact the performance of a myriad of process applications. Semiconductor manufacturing is one such application that due to the ever increasing demands placed on its quality and speed holds tremendous potentials for further research and development in the areas of monitoring and control. One of the key areas of semiconductor manufacturing that has received significant attention among researchers and practitioners in recent years is the online sensor based monitoring and feedback control of its nanoscale wafer fabrication process. Monitoring and feedback control strategies of nanomanufacturing processes often require a combination of monitoring using nonstationary and multiscale signals, and a robust feedback control using complex process models. It is also essential for the monitoring and feedback control strategies to possess stringent properties such as high speed of execution, low cost of operation, ease of implementation, high accuracy, and capability for online implementation. Due to the above requirement, a need is being felt to develop state-of-the-art sensor data processing algorithms that can perform far superior to those that are currently available both in the literature and commercially in the form of softwares.The contributions of this dissertation are three fold. It first focuses on the development of an efficient online scheme for process monitoring. The scheme combines the potentials of wavelet based multiresolution analysis and sequential probability ratio test to develop a very sensitive strategy to detect changes in nonstationary signals. Secondly, the dissertation presents a novel online feedback control scheme. The control problem is cast in the framework of probabilistic dynamic decision making, and the control scheme is built on the mathematical foundations of wavelet based multiresolution analysis, dynamic programming, and machine learning. Analysis of convergence of the control scheme is also presented. Finally, the monitoring and the control schemes are tested on a nanoscale manufacturing process (chemical mechanical planarization, CMP) used in silicon wafer fabrication. The results obtained from experimental data clearly indicate that the approaches developed outperform the existing approaches. The novelty of the research in this dissertation stems from the fact that they further the science of sensor based process monitoring and control by uniting sophisticated concepts from signal processing, statistics, stochastic processes, and artificial intelligence, and yet remain versatile to many real world process applications.