A system for controlling the non-product wafer includes the following: a monitoring module, configured to monitor the state of the non-product wafer; a statistics module, configured to obtain usage information of the non-product wafer; and a control module, configured to receive a production instruction and control the non-product wafer according to the state and the usage information of the non-product wafer. The disclosure implements the purpose of automatic control and management of the non-product wafer.

A system for controlling a quality of a vehicle includes: a communication device that communicates with a plurality of terminals respectively located on vehicle product lines; and a controller that monitors the part assembly state of the worker, and displays an official assembly video of a defectively assembled part through a terminal on a product line where the worker is located when detecting the defective assembly of the worker.

A method for providing output values with associated uncertainties for a flexible modular plant or machine comprising an arrangement of modular entities, wherein uncertainty information associated with an operation of the modular entity is assigned to a plurality of modular entities and input values are provided based on an operation of the modular entities, where a computing unit calculates an output value based on said input values, calculates an input value uncertainty for each input value based on the uncertainty information of the modular entity, and calculates at least one output value uncertainty associated with the output value based on propagation of uncertainty and using the input value uncertainties, and where the output value and the at least one output value uncertainty are output.

It is provided a method (and corresponding apparatuses, computer programs, and medium) for determining whether an item being processed is defective or non-defective, the method including a step of determining, by a local neural network and on the basis of sensing measurements performed on an item while the item is being processed, a local classification result indicating whether the item is defective or non-defective. It is determined a confidence index indicating a level of confidence that the local classification result is correct. Then, in response to the confidence index being below a given threshold, it is determined, by a central neural network and on the basis of the sensing measurements, a central classification result indicating whether the item is defective or non-defective, wherein the local neural network has less computational resources than the central neural network.

A substrate processing system comprises an edge computing device including processor that executes instructions stored in a memory to process an image or video captured by camera(s) of at least one of a substrate and a component of the substrate processing system. The component is associated with a robot transporting the substrate between processing chambers of the substrate processing system or between the substrate processing system and a second substrate processing system. The cameras are located along a travel path of the substrate. The instructions configure the processor to transmit first data from the image to a remote server via a network and to receive second data from the remote server via the network in response to transmitting the first data to the remote server. The instructions configure the processor to operate the substrate processing system according to the second data in an automated or autonomous manner.

A method for forming a fully self-aligned via is provided. A workpiece having a pattern of features in a dielectric layer is received into a common manufacturing platform. Metal caps are deposited on the metal features, and a barrier layer is deposited on the metal caps. A first dielectric layer is added to exposed dielectric material. The barrier layer is removed and an etch stop layer is added on the exposed surfaces of the first dielectric layer and the metal caps. Additional dielectric material is added on top of the etch stop layer, then both the additional dielectric material and a portion of the etch stop layer are etched to form a feature to be filled with metal material. An integrated sequence of processing steps is executed within one or more common manufacturing platforms to provide controlled environments. Transfer modules transfer the workpiece between processing modules within and between controlled environments.

A method and system may generate a quality control profile to indicate an expertise level and one or more skills of a teleoperator(s). The control center evaluates optimization criteria for a workflow to assign performance of microtasks of the workflow to select teleoperators from a pool of teleoperators. Each teleoperator accesses teleoperation functionality for remote control of a plurality of types of equipment at one or more defined geographic areas and each teleoperator is remotely located from the defined geographic areas. The control center generates queues for each of the select teleoperators that include corresponding assigned microtasks.

An information processing apparatus detects an abnormality sign in a semiconductor manufacturing apparatus. The apparatus includes: a sensor data collector configured to acquire sensor waveform data with respect to a sensor value axis and a time axis measured by a semiconductor manufacturing apparatus that is executing a process according to a same recipe; a monitoring band calculator configured to calculate each monitoring band for the sensor value axis and the time axis used in a waveform monitoring method from a predetermined number or more of the sensor waveform data; and an abnormality sign detector configured to monitor a waveform of the sensor waveform data using each monitoring band for the sensor value axis and the time axis and detect an abnormality sign of the semiconductor manufacturing apparatus.

Techniques are presented for the application of neural networks to the fabrication of integrated circuits and electronic devices, where example are given for the fabrication of non-volatile memory circuits and the mounting of circuit components on the printed circuit board of a solid state drive (SSD). The techniques include the generation of high precision masks suitable for analyzing electron microscope images of feature of integrated circuits and of handling the training of the neural network when the available training data set is sparse through use of a generative adversary network (GAN).

A method for determining a root cause affecting yield in a process for manufacturing devices on a substrate, the method including: obtaining yield distribution data including a distribution of a yield parameter across the substrate or part thereof; obtaining sets of metrology data, each set including a spatial variation of a process parameter over the substrate or part thereof corresponding to a different layer of the substrate; comparing the yield distribution data and metrology data based on a similarity metric describing a spatial similarity between the yield distribution data and an individual set out of the sets of the metrology data; and determining a first similar set of metrology data out of the sets of metrology data, being the first set of metrology data in terms of processing order for the corresponding layers, which is determined to be similar to the yield distribution data.