Anomaly detection and remedial recommendation techniques for improving the quality and yield of microelectronic products are provided. In one aspect, a method for quality and yield improvement via anomaly detection includes: collecting time series sensor data during individual steps of a semiconductor manufacturing process; calculating anomaly scores for each of the individual steps using a predictive model; and implementing changes to the semiconductor manufacturing process based on the anomaly scores. A system for quality and yield improvement via anomaly detection is also provided.

A method includes receiving part data associated with a corresponding part of substrate processing equipment, sensor data associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce one or more corresponding substrates, and metrology data associated with the one or more corresponding substrates produced by the one or more corresponding substrate processing operations performed by the substrate processing equipment that includes the corresponding part. The method further includes generating sets of aggregated part-sensor-metrology data including a corresponding set of part data, a corresponding set of sensor data, and a corresponding set of metrology data. The method further includes causing analysis of the sets of aggregated part-sensor-metrology data to generate one or more outputs to perform a corrective action associated with the corresponding part of the substrate processing equipment.

A quality determination device includes an acquisition unit for acquiring a drive current during tapping, from a motor provided in a machine tool that performs tapping, and a determination unit for determining acceptance or rejection of a screw hole formed by the tapping, based on the drive current, an electric power of the motor obtained from the drive current, or a torque of the motor obtained from the drive current.

Embodiments disclosed herein generally relate to a method, system, and non-transitory computer readable medium for classifying an outlier in time series data collected by a sensor positioned in a substrate processing chamber. The client device receives time series data from the sensor positioned in the substrate processing chamber. The client device converts the time series data to a bounded uniform signal. The client device identifies signal sub-segments that do not match an expected behavior. The client device classifies the identified sub-segments that do not match the expected behavior.

The invention relates to a monitoring method (100) for monitoring a winding process on a winding device (1), comprising: providing (104) a correlation of an irregularity (12) of a web material (11) to a running parameter (13) of the reel (10) for an unwinding process. The invention further relates to a control method (200) for performing an unwinding process on an unwinding device, comprising: obtaining (201) a correlation of an irregularity (12) of a web material (11) to at least one running parameter (13) of a reel (10) from a winding process. Furthermore, the invention relates to a processing method (300) for performing a process of processing a web material (11). Furthermore, the invention relates to a winding device (1) for performing a winding process of a web material (11) to a reel (10), an unwinding device (2) for performing an unwinding process of a web material (11) of a reel (10) and a processing system (3) for processing a web material (11).

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

In order to realize stable quality control, provided is a quality control device (1) having an input device which receives data such as an operation condition of each device (21) to (26) in a production system (20) for producing a product; a calculation unit which assigns a value of the operation condition to a correlation formula calculated in advance and calculates a value derived from the correlation formula; and a determination unit which performs good or bad determination on a quality of a workpiece in each process, on the basis of a result calculated by the calculation unit. Further, when “bad” is determined as a result of the good or bad determination, the quality control device (1) calculates an appropriate value of the operation condition and sets the value to each device (21) to (26).

ontainer-handling facility for handling containers such as cans or bottles or the like in the beverage-processing industry, the container-handling facility compris at least one transport device for transporting the containers and a drone can be brought into the transport device, wherein the drone comprises an acceleration sensor for measuring an acceleration acting on the drone and a pressure sensor for measuring a pressure acting on the drone in the transport device, wherein the drone can transmit data corresponding to a measured acceleration and a measured pressure to a control unit, and wherein the control unit is ed to regulate at least one operating parameter influenc the acceleration of the drone and/or the pressure acting on the drone, ding o a comparison between the data received the drone and nominal values for the operating parameter and/or the acceleration and/or the pressure, stored in a memory.

A method for generating a digital production quality certificate for a component carrying a machine readable unique identifier undergoing a plurality of manufacturing steps is provided. The method comprises obtaining parameters indicative of the manufacturing steps from one or more sensors for a plurality of monitored manufacturing steps. The obtained parameters are linked with the machine readable unique identifier and one or more quality analyses are performed for a selected monitored manufacturing step based on the obtained parameters for said manufacturing step. A digital quality certificate is generated for the component comprising data related to the results of the quality analyses and the parameters obtained for the component are stored. The disclosure also provides examples of methods and systems of monitoring a manufacturing process.

An ADDON for a networked sensor solution that provides a new innovative way for enabling the integration with legacy industrial control systems by connecting the ADDON in the same way as a sensor is connected to the base unit; wired or wireless; direct or indirect. The ADDON makes the data from the sensors directly available to ICS without having to connect to a network first, through industrial wired protocols such as but not limited to, Modbus RTU and BACnet MSTP.