The present invention provides methods and systems for manufacturing process control of photovoltaic products. Some embodiments relate to a method for tracking wafers for photovoltaic products with respect to which production tool processed them and their position within that production tool. Some embodiments relate to measuring and characterizing the critical-to-quality parameters of the partially-finished photovoltaic products emerging from the production tool in question. Some embodiments relate to display and visualization of the measured parameters on a computer screen, such that the parameters of each production unit can be directly observed in the context of which production tools processed them, which location within a specific production tool they were located in during processing, and which batch, or in the case of continuous processing, what time, the unit(s) was/where processed.

Methods, apparatus, systems and articles of manufacture are disclosed to improve boundary excursion detection. An example apparatus to improve boundary excursion detection includes a metadata extractor to parse a first control stream to extract embedded metadata, a metadata label resolver to classify a boundary term of the extracted embedded metadata, a candidate stream selector to identify candidate second control streams that include a boundary term that matches the classified boundary term of the first control stream, and a boundary vector calculator to improve boundary excursion detection by calculating a boundary vector factor based on respective ones of the candidate second control streams that include the classified boundary term.

A semiconductor analysis system includes a machining device that machines semiconductor wafer to prepare a thin film sample for observation, a transmission electron microscope device that acquires a transmission electron microscope image of the thin film sample, and a host control device that controls the machining device and the transmission electron microscope device. The host control device evaluates the thin film sample based on the transmission electron microscope image, updates machining conditions based on an evaluation result of the thin film sample, and outputs the updated machining conditions to the machining device.

A machining device includes: a positional deviation detection unit configured to calculate a correction value for correcting a positional deviation between an ideal contour line and an actual contour line in each of a plurality of angular directions based on a center of a hemispherical shape of a tool; a distance effect coefficient calculation unit configured to calculate a first distance effect coefficient indicating a degree of influence of the positional deviation when machining a second machining point, according to a distance between the tool and the second machining point in a case where a machining point machined by the tool transitions from one-point machining including a first machining point in the workpiece to two-point machining including the first machining point and the second machining point; and a positional deviation correction unit configured to correct the positional deviation of the tool.

To provide more test data during the manufacture of non-volatile memories and other integrated circuits, machine learning is used to generate virtual test values. Virtual test results are interpolated for one set of tests for devices on which the test is not performed based on correlations with other sets of tests. In one example, machine learning determines a correlation study between bad block values determined at die sort and photo-limited yield (PLY) values determined inline during processing. The correlation can be applied to interpolate virtual inline PLY data for all of the memory dies, allowing for more rapid feedback on the processing parameters for manufacturing the memory dies and making the manufacturing process more efficient and accurate. In another set of embodiments, the machine learning is used to extrapolate limited metrology (e.g., critical dimension) test data to all of the memory die through interpolated virtual metrology data values.

An adjusting method for adjusting an imprint apparatus includes a preparation step of preparing a sample for evaluating a state in which a contact region of a test mold is in contact with an imprint material supplied on a substrate; an evaluation step of evaluating the sample; and an adjustment step of adjusting the imprint apparatus based on a result of evaluation obtained in the evaluation step. The contact region includes a flat region which does not include a pattern, the evaluation in the evaluation step includes a first evaluation, which is an evaluation of a state of the imprint material in the flat region, and the imprint apparatus is adjusted based on a result of the first evaluation in the adjustment step.

The prediction system of strip chew collects and stores first data and second data as adaptive model construction data. The first data indicates the occurrence or non-occurrence of the strip chew in an object rolling path and the occurrence point of the strip chew. The second data includes information on a preceding rolling path and attribute information on an object strip. The system constructs an adaptive model using the stored adaptive model construction data, and stores the constructed adaptive model as an adapted model. The system collects prediction data similar to the second data. Then, the system inputs the prediction data to the adapted model to predict the occurrence or non-occurrence of the strip chew in the object rolling path and all or some of the occurrence points of the strip chew before the prediction object strip reaches the object rolling path.

A system for manufacturing a product having predetermined specifications includes a working station for manufacturing the product, which has a plurality of working operating parameters; first sensors for detecting first data regarding the working environment; second sensors for detecting second data regarding the plant where the system is installed; and a first control device operatively connectable to the working station and to the first and the second sensors, which has a storage unit containing a plurality of optimal operating parameters. During operation of the working station, the control device detects the first and the second data and compares the working operating parameters with the corresponding optimal operating parameters so as to detect deviations. A second device determines the optimal operating parameters during operation of the working station.

A method for correcting tool parameters of a machine tool for machining workpieces includes recording measurement values of measured characteristics as actual values of at least one workpiece machined with the machine tool. The measurement values are compared with the default set values of the workpiece. The measurement values of at least two measured characteristics are recorded from at least two parameters of at least one measured characteristic and/or from at least one measured characteristic and from at least one parameter. An average for a tool correction value is calculated from the measurement values and the corresponding set values, with which a correction of the machine tool is performed.

A production process execution system has implementation modules which provide instructions for implementing the production process, recording modules which record the implemented steps of the production process, a control layer which monitors and controls the implementation modules and the recording modules, a cognition layer which monitors the production process, calculates process quality and provides feedback to a user through the implementation modules as to the correctness of the implemented steps. The system is enhanced by improvements in video recording, model training and digital recording of system operation and parameters. These features facilitate the creation of a closed system which provides user monitoring and system generated feedback.