Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.

An inspection management system having a plurality of processes and managing final inspection performed to inspect a completed product and one or more intermediate inspections performed to inspect an intermediate product manufactured in the processes earlier than a final process includes: an inspection content data acquisition unit that acquires inspection content data including an inspection standard for each inspection item of the product; an inspection content setting unit that sets inspection content based on the inspection content data acquired by the inspection content data acquisition unit; a simulation unit that simulates inspection in accordance with assumed inspection content; an inspection standard calculation unit that calculates an inspection standard more appropriate than a current inspection standard based on the simulation; and an output unit that outputs base information indicating that at least the inspection standard calculated by the inspection standard calculation unit is more appropriate than the current inspection standard.

The present disclosure provides systems and methods for controlling a semiconductor manufacturing equipment. The control system includes an inspection unit capturing a set of images of the semiconductor manufacturing equipment, a sensor interface receiving the set of images and generating at least one input signal for a database server, and a control unit. The control unit includes a front end subsystem, a calculation subsystem, and a message and feedback subsystem. The calculation subsystem receives the data signal from the front end subsystem, wherein the calculation subsystem performs an artificial intelligence analytical process to determine, according to the data signal, whether a malfunction has occurred in the semiconductor manufacturing equipment and to generate an output signal. The message and feedback subsystem generates an alert signal and a feedback signal according to the output signal, and the alert signal is transmitted to a user of the semiconductor manufacturing equipment.

Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

A method for carrying out a cyclical manufacturing process produces parts within a predefined quality tolerance. After at least one process adjustment variable is changed, a quality feature of the parts produced with a changed process adjustment variable is checked against the range of the quality tolerance of the produced parts. A process characteristic variable zone is formed in an automated manner using at least one determined process characteristic variable variant that is process-stable and for which the process adjustment variable produces acceptable parts.

An inspection management system that manages inspection of an inspection apparatus in a production line including a manufacturing apparatus of a product and the inspection apparatus includes: an acquisition unit configured to acquire inspection content data including an inspection standard for each component; an inspection content setting unit configured to set inspection content; a setting-related information acquisition unit configured to acquire setting-related information including at least a time at which new inspection content is set when the inspection content setting unit sets the new inspection content; a storage unit configured to retain the setting-related information in association with a history of the setting of the inspection content; a setting-related information reading unit configured to read the setting-related information retained in the storage unit; and an output unit configured to be able to output the setting-related information.

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.

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.

A control method for a processing apparatus includes the steps of suspending a processing operation of a processing unit during the formation of a processed groove and then imaging the processed groove by using an imaging unit to obtain a detected image, inspecting the condition of the processed groove according to the detected image, inputting a selected one of plural parameters into an input area displayed on a touch panel from an operator in adjusting the parameters to optimize inspection conditions, moving an input cursor from the input area according to the input of the selected parameter, and executing the inspection of the processed groove by using the selected parameter input in the input area.

A shape evaluation method includes a shape error calculation step that calculates a shape error, which is an error between a designed shape and a shape to be evaluated, and a visible error detection step that detects visible shape errors on the basis of the shape error and predetermined visual characteristic data.