Part 2: Sample Classification function offers a sophisticated way of classifying measured samples

Series: Acquire Automation XT – FRT´s Powerful Multi Sensor Software

Acquire Automation XT’s Sample Classification function offers a sophisticated way of classifying measured samples, based on the generated measurement and analysis results. Result values can be sorted to user-defined classes and, based on the distribution of the individual results to the various classes, the sample can be assigned to an overall classification. The most simple example would be the classic ‘Good/Bad’ classification. Depending on the number of measurements which are counted as ‘Good’ or ‘Bad’, the overall sample result can be assigned with the ‘Good’ or ‘Bad’ label.

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A classification is defined by a ‘class group’ which consists of at least one class. The classes are defined by specific criteria and the individual classes can be edited. By clicking on one of the classes, the ‘Class Parameter’ section is activated. Here, the priority, behavior and appearance of the class can be defined. The priority of a class defines the impact of the class on the sample classification.

Class criteria are the information used to assign an evaluation result to a specific class. The measuring task, the evaluation task and the output value to classify must be defined individually for each recipe. The classes can be specified by assigning an expected value or value range in the parameter table.

A recipe may contain several class groups both, at the same and at different levels. In order to generate a final classification result which can be shown in the message box at the end of the process, one of the used class groups must be selected as master.

In the datalog section, results and reports can be viewed including the information on the sample classification. Depending on whether the classification has been applied on sample, element or task level, the appearance of the results/reports varies.

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Interested in latest posts of this series? Click below.

Part 1/7: Bearing Curve evaluation offers a clever method to classify and describe the surface texture of an object

Part 3/7: Dual Scan Mode for high-speed measurement of features with a large discontinuous step

Part 4/7: Fit based Defect Inspection find local deviations of a measurement in comparison to a reference

Part 5/7: Individual Execution of Tasks and Evaluations for Recipe Adjustment

Part 6/7: Area Alignment Task - Automatic determination and compensation of the displacement of a measurement area in relation to the die layout

Part 7/7: Access the full capability of FRT multi-sensor tools by using Hybrid Task and Hybrid Evaluation

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