Product assurance technology for custom LSI/VLSI electronics

The technology for obtaining custom integrated circuits from CMOS-bulk silicon foundries using a universal set of layout rules is presented. The technical efforts were guided by the requirement to develop a 3 micron CMOS test chip for the Combined Release and Radiation Effects Satellite (CRRES). This chip contains both analog and digital circuits. The development employed all the elements required to obtain custom circuits from silicon foundries, including circuit design, foundry interfacing, circuit test, and circuit qualification

The use of statistics in understanding pharmaceutical manufacturing processes

D.Eng.Industrial manufacturing processes for pharmaceutical products require a high level of understanding and control to demonstrate that the final product will be of the required quality to be taken by the patient. A large amount of data is typically collected throughout manufacture from sensors located around reaction vessels. This data has the potential to provide a significant amount of information about the variation inherent within the process and how it impacts on product quality. However to make use of the data, appropriate statistical methods are required to extract the information that is contained. Industrial process data presents a number of challenges, including large quantities, variable sampling rates, process noise and non-linear relationships. The aim of this thesis is to investigate, develop and apply statistical methodologies to data collected from the manufacture of active pharmaceutical ingredients (API), to increase the level of process and product understanding and to identify potential areas for improvement. Individual case studies are presented of investigations into API manufacture. The first considers prediction methods to estimate the drying times of a batch process using data collected early in the process. Good predictions were achieved by selecting a small number of variables as inputs, rather than data collected throughout the process. A further study considers the particle size distribution (PSD) of a product. Multivariate analysis techniques proved efficient at summarising the PSD data, to provide an understanding of the sources of variation and highlight the difference between two processing plants. Process capability indices (PCIs) are an informative tool to estimate the risk of a process failing a specification limit. PCIs are assessed and developed to be applied to data that does not follow a standard normal distribution. Calculating the capability from the percentiles of the data or the proportion of data outside of the specification limits has the potential to generate information about the capability of the process. Finally, the application of Bayesian statistical methods in pharmaceutical process development are investigated, including experimental design, process validation and process capability. A novel Bayesian method is developed to sequentially calculate the process capability when data is collected in blocks over time, thereby reducing the level of noise caused by small sample sizes

Cost-conscious manufacturing – Models and methods for analyzing present and future performance from a cost perspective

Manufacturing is an industry in which the effects of globalization are obvious. Manufacturing costs are a key factor in this respect and affect, for example, decisions about offshoring, i.e., moving production abroad. If Sweden and other Western countries are to maintain large manufacturing sectors, they must be competitive, making cost one of the most critical parameters. The work presented here seeks to develop tools for cost-conscious manufacturing. These tools should provide insight into how well a manufacturing system is performing and support the analysis and prioritization of manufacturing development activities. To achieve this objective, two research questions were formulated. The first research question (RQ1) concerns how a general cost model should be designed to take into consideration the most important process-near parameters influencing manufacturing system performance. A cost model developed in accordance with this research question includes critical parameters affecting performance, such as cycle time, setup time, and performance loss parameters. The model is centered on the processing steps involved in processing a part. The losses occurring in the processing steps are important in the model, so the links between structured, detailed monitoring of the loss causes and their impacts on costs are emphasized. Modified versions of the model to analyze volume flexibility and downtime variability are also presented. The second research question (RQ2) concerns how such a cost model can be used in practice, i.e., the requirements and conditions for industrial use. Implementation in an automotive company indicated that the model was applicable in this context and that interesting insights into manufacturing costs could be gained from using the model. A study of the industrial conditions for applying the cost model identified software products for collecting manufacturing loss data that support the level of detail needed for model input, but found that manufacturing companies do not necessarily collect such detailed data. A demonstration program developed based on the databases available in a collaborating company indicated how the cost model could be used practically in a company. The somewhat deficient detail in the collected loss data, found in the above study, led to an inquiry in another company into the pros and cons of collecting highly detailed performance loss data. The results identified more advantages than disadvantages with collecting more detailed data: the operators responsible for data collection did not perceive any particular difficulties with the increased detail and the production manager believed that the increased detail led to better knowledge of performance losses