A quality determination method includes: in a quality determination space, mapping a quality of a package product in which a plurality of devices are assembled in accordance with a predetermined design condition, with use of each test result of each of the plurality of devices; and determining a quality of the package product, on a basis of mapping result of the mapping.

A method evaluates a sample of measurement data from measuring multiple workpieces by at least one coordinate measuring machine. A system of statistical distributions describes a frequency of measurement data values. The distributions are distinguishable based on skewness and kurtosis. The method includes defining a set of statistical distributions that are able to describe a frequency of measurement data values in the entire value interval from the system of statistical distributions for a value interval of the measurement data, which is a specified value interval or a value interval of the measurement data actually arising in the sample. The method includes ascertaining the skewness and the kurtosis from the sample of measurement data corresponding to a first statistical distribution. The method includes checking, using the ascertained moment values, whether the defined set contains a statistical distribution that has the ascertained skewness and kurtosis, and producing a corresponding test result.

An inspection system includes machine learning circuitry configured to determine whether each of objects belongs to a predetermined attribute based on feature data of each of the objects, feature data acquisition circuitry configured to acquire feature data of reevaluated objects which are determined to belong to the predetermined attribute without using the machine learning circuitry among excluded objects which are determined not to belong to the predetermined attribute by the machine learning circuitry, and parameter update circuitry configured to update a learning parameter of the machine learning circuitry based on teaching data including the acquired feature data acquired by the feature data acquisition circuitry.

Methods and systems for calibrating metrology tool offset values to match measurement results across a fleet of metrology tools are presented herein. The calibration of offset values is based on measurements of inline, production wafers and does not require the use of specially fabricated and characterized quality control (QC) wafers. In this manner, the entire process flow to calibrate metrology tool offset values is automated and fully integrated within a high volume semiconductor fabrication process flow. In a further aspect, the implementation of a new offset value is regulated by one or more predetermined control limit values. In another further aspect, the measured values of a parameter of interest are adjusted to compensate for the effects of measurement time on the wafer under measurement.

To prevent operation of an analysis system from being stopped every time an error occurs in an analysis device of a monitoring system. A monitoring system includes an analysis device provided with a plurality of sensors, an error detector configured to detect occurrence of an error in the analysis device based on an output signal of a plurality of the sensors, and to identify a type of the detected error, an information storage part storing levels set in advance for each of a plurality of types of errors that may occur in the analysis device, and an error level identification part configured to identify a level of an error detected by the error detector based on information stored in the information storage part Where an error occurs in the analysis device, a measure according to the level of the error identified is executed in the analysis device.

Various embodiments enable batch inspection of a plurality of workpieces by and inspection instrument such as a coordinate measuring machine. Some embodiments present user interfaces, including graphical user interfaces, to enable an operator to configure a batch inspection system and a batch inspection job, and to monitor and control execution of a batch inspection job.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing material processing. In one aspect, a method includes collecting, from a set of sensors, a set of current manufacturing conditions. Based on the set of current manufacturing conditions collected from the sensors, a set of current qualities of a material currently being processed by manufacturing equipment is determined. A baseline production measure for processing the material according to the set of current qualities is obtained. A candidate set of manufacturing conditions that provide an improved production measure relative to the baseline production measure is determined. A set of candidate qualities for the material produced under the candidate set of manufacturing conditions is determined. A visualization that presents both of the set of candidate qualities of the material and the set of current qualities of the material currently being processed is generated.

A manufacturing step management system includes: a support member configured to come into contact with a workpiece that moves in a state in which tension is applied and to support the workpiece; an acquirer configured to acquire information about variance of a mechanical change on the basis of the mechanical change in the support member; and an estimator configured to estimate the tension applied to the workpiece.

Disclosed is a method for applying a control reference value regarding a quality control sample to a specimen analyzer by a computer, the method comprising: generating the control reference value on the basis of a measurement result of the quality control sample measured by the specimen analyzer; and applying the control reference value to the specimen analyzer.

In one embodiment, a multi-purpose sensor may couple to a machine operating in an industrial environment and include numerous sensors disposed within the multi-purpose sensor to acquire sets of data associated with the machine or an environment surrounding the machine. A first portion of the sets of data may include historical sensor measurements over time for each of the sensors, and a second portion of the sets of data may include sensor measurements subsequent to when the first portion is acquired for each of the sensors. A processor of the multi-purpose sensor may determine a baseline collective signature based on the first portion, determine a subsequent collective signature based on the second portion, determine whether the collective signatures vary, and generate signals when a variance exists. The signals may cause a computing device, a cloud-based computing system, and/or a control/monitoring device to perform various actions.