YIELDANALYSIS
Critical diffusion
The diffusion furnace processes in PV manufacturing include doping, drive in and anneal to create the cell emitter junction, these are the most critical steps in the manufacture of a device. Performing statistical process control calculations on machine data can help to prevent process scrap events and ensure that all product performance is within defined limits. The first step is for the equipment to achieve process acceptance and for the data from a number of good runs to be collected into a relational database.
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99.99966% of the products manufactured are expected to be free of defects, this translates to 3.4 defects per million. Adopting the six sigma approach affects everything you do, and is seen by proponents as a way of life not just a statistical calculation. Six sigma sets a target for each and every process in the manufacturing chain, and one way equipment suppliers can contribute towards achieving this goal is by automatically measuring the machine control data that ultimately contributes to the results we measure on the cell.
It is expected that equipment manufacturers will provide software for the collection of equipment control parameters and real time performance data. However, it is less common for the PV device manufacturers to make use of this data to analyse equipment performance and to put SPC control targets on that data. Analysing data across multiple process runs can reveal whether the performance of a tool is changing over time, this is commonly known as ‘Trend Analysis’. Graphical representation of performance data over time can reveal many things,
including how well a system is tracking the process recipe, which in turn will have a direct correlation with
device efficiency of cells in a specific batch and between batches.
The next step is to identify the critical process stages and the variables which are influencing the process at that time. Within SPC these critical periods are referred to as ‘Data Analysis Periods’ or just DP for short. Identifying the correct data periods is crucial. For example, it may be important to identify a data period covering a period of temperature recovery, in order to monitor and identify problems with the control algorithm that supplies power to the heating element.
When data periods have been identified, and there may be a number of data periods for the analysis of different variables, then SPC software can be used to create ‘Trend charts’. The graphical representation of critical parameters across multiple good runs provides an indication of stability and parameter boundaries termed upper and lower control limits.
Based on the upper and lower limits taken from trend chart information, control charts can be created. For example, when a batch of PV cells are processed in a diffusion furnace they are placed in the furnace centre zone which is required to have a stable temperature. In the example below upper and lower limits of +/-1.0°C have been set, these boundaries can be used to generate an alarm, abort or hold situation.
Controlled approach
Control charting is a very simple and powerful tool. Once the critical parameters, data periods and upper and lower limits have been defined, the SPC software can be used to automatically validate the data for subsequent process runs and will usually be programmed to either provide an alarm or hold the processing of subsequent batches if out of an tolerance event is observed. This is a particularly effective way of detecting faulty hardware components, for example a faulty MFC or heating element, by recording and comparing changes in gas flow and power consumption respectively across process runs. A second control chart
www.solar-pv-management.com Issue VII 2011
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