A CPU unit is a life prediction device for a fan. The CPU unit includes a temperature calculation unit to calculate internal temperature of the CPU unit on the basis of at least one of the utilization of the CPU unit, the temperature a CPU, and the rotation speed of the fan; and a storage unit to store fan life data that indicates the life of the fan relative to temperature. A control unit includes a life prediction unit to calculate remaining life expectancy of the fan on the basis of the fan life data and the internal temperature calculated by the temperature calculation unit.

One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., to account for chamber-to-chamber variability) using machine learning techniques.

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 pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.

There is provided a method of performing an analysis of a defect in a pattern of an imprint material on a substrate that has undergone an imprint process of transferring a pattern of a mold onto the substrate. The method includes obtaining a defect distribution of the pattern on the substrate, obtaining map information indicating an arrangement of the imprint material on the substrate, and determining a type of a defect based on a relationship between a position of the defect in the defect distribution and a position of a gap in the imprint material generated in a process of spreading the imprint material by the imprint process, wherein the position of the gap is predicted based on the map information.

A programmable manufacturing advisor includes an information unit that receives measurements for at least one parameter of each operation of a plurality of operations in the manufacturing process, and an analytics unit that determines a baseline performance metric for the manufacturing process based on the measurements of the at least one parameter. The programmable manufacturing advisor also includes an optimization unit that determines a recommended improvement action by determining a predicted performance metric for the manufacturing process based on an adjusted value of the at least one parameter and comparing the predicted performance metric to the baseline performance metric. The optimization unit also automatically presents the recommended improvement action to the operations manager.

A pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.

Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., to account for chamber-to-chamber variability) using machine learning techniques.

An inspection information prediction apparatus includes an environment-information acquisition unit that acquires environment information of a routing step through which an inspection target has been routed before an inspection step of inspecting the inspection target, a manufacturing-information acquisition unit that acquires manufacturing information of the inspection target, and a prediction unit that predicts inspection information which indicates an inspection result of an inspection portion of the inspection target determined by the manufacturing information and is obtained by applying the environment information, based on the manufacturing information of the inspection target and the environment information of the routing step through which the inspection target has been routed.