Mon, 20 May 2013 19:00:00 GMT
Fri, 17 May 2013 17:28:00 GMT
Mon, 13 May 2013 08:48:00 GMT
One of the most complex as well as expensive automated manufacturing environments is that required for the manufacture of semiconductors. The typical process involves the nearly fully automated application of hundreds of processing steps to lots ("stacks") of silicon wafers, each containing a large number of microchips. Creating a high-yield process, where most (e.g., 90% or more) of all chips pass final acceptance testing is extremely difficult and time consuming. At the same time, the cost of failure in this environment is significant, as each wafer can be many times more valuable than even the most precious metal by weight. Moreover, unexpectedly lengthy ramp-up times (to create a reliable production process) may significantly undercut the commercial value of the final product, hence jeopardizing the huge investment in the semiconductor Fab, which may well reach $2 Billion dollars or more!
The STATISTICA system provides a huge set of tools for engineers, to study processes. First, the STATISTICA system will quickly and seamlessly integrate into the existing information infrastructure, querying directly the relevant databases (practically all industry standard database formats are supported). There is no need to laboriously import the data into, for example, a limited spreadsheet format for further analyses; instead STATISTICA connects directly to your data.
Next, STATISTICA interactive graphics are extremely fast and flexible, so meaningful views and graphical summaries of key processes, variables, measurements, outcomes, etc. can be created very quickly.
Each process is unique, and the techniques for automated manufacturing are constantly evolving in this highly competitive environment. STATISTICA is fully customizable and programmable, down to all aspects of graphs, data handling, and so on. Hence, in addition to providing an extremely sophisticated and flexible off-the-shelf tool, the system also serves as a toolbox that will enable engineers to develop custom analyses and processes quickly, to support the critical ramp-up of new manufacturing processes, and the specialized analytic tools to support them.
STATISTICA Data Miner provides an extremely comprehensive set of knowledge discovery algorithms that can be applied to support the manufacturing process. In addition to commonly used advanced neural network architectures, STATISTICA Data Miner implements the most cutting edge tools in a single integrated platform. For example, the system includes algorithms such as stochastic gradient boosting, random forests, support vector machines, multivariate adaptive regression splines (MARSplines), independent components analysis, to name a few. These techniques can be used to build robust and reliable predictive models for quality or failure, even in high-dimensional environments with large numbers of variables (but few "cases" or "rows") and significant interactions between them (e.g., interactions between tools). All of these methods are implemented in the same efficient and programmable STATISTICA platform, yielding the most advanced set of tools for tackling difficult root-cause analysis and predictive QC problems.
STATISTICA Data Miner and other statistical analysis algorithms are used to provide the core support in KLA-Tencor's yield analysis and management systems. Because StatSoft is the recognized leader in the application of advanced, cutting-edge data mining techniques, KLA-Tencor has chosen STATISTICA and StatSoft as the partner, to provide critical advanced data analysis and data mining support for dedicated yield management solutions for the semiconductor industries. Indeed, the complete programmability and customizability of the STATISTICA system make it the ideal toolkit for these types of custom solution systems.
Manufacturing processes in many areas (including for example pharmaceutical and biopharmaceutical industry) involve a complex sequence of unit operations, with interactions between upstream and downstream processing, raw materials, media, and instrumentation.
Those manufacturing processes require that the quality assurance professionals and managers have access to the Hierarchical Process Cube that contains all information about how the material moved through the sequence of steps, so that meaningful monitoring, qc-charting, reporting, and root cause and other analyses can be performed.
For example, process engineers need to be able to enter batch numbers for any process step (unit operation), and retrieve the relevant data properly aligned and aggregated over all other steps.
In STATISTICA Enterprise, engineers and analysts are empowered to select the relevant process and easily gain access to the necessary data and to review the movement of materials and batches through the production process.
The STATISTICA Product Traceability capabilities of the Enterprise platform provides a natural and straightforward way to automate the data preprocessing required to enable required reporting or data analyses – because it can be easily configured to reflect the nature of the respective manufacturing process.