A process for detecting foreign particle defects and scratch defects on semiconductor products including detecting foreign particle and scratch defects on the semiconductor products; placing the semiconductor products in a first wafer carrier and docking to a first load port of a semiconductor processing tool; opening a door of the first wafer carrier; transferring the semiconductor products from the first wafer carrier through the first load port to and through an interior of the semiconductor processing tool to a second load port of the semiconductor processing tool; transferring the semiconductor products from the second load port to a second wafer carrier; closing a door of the second wafer carrier and undocking from the second load port; and detecting foreign particle and scratch defects on the semiconductor products and comparing to the foreign particle defects on the semiconductor products prior to placing the semiconductor products in the first wafer carrier.

A method includes receiving, into a measurement tool, a substrate having a material feature, wherein the material feature is formed on the substrate according to a design feature. The method further includes applying a source signal on the material feature by using a source in the measurement tool having a tool setting parameter, collecting a response signal from the material feature by using a detector in the measurement tool to obtain measurement data, and with a computer connected to the measurement tool, calculating a simulated response signal from the design feature using the tool setting parameter. The method further includes, with the computer, in response to determining that a difference between the collected response signal and the simulated response signal exceeds a predetermined value, causing the measurement tool to re-measure the material feature.

A method of manufacturing semiconductor devices includes defining a sampling plan that contains position information about metrology sites on process wafers. A first property of the process wafers is measured to obtain measurement values at measurement points, wherein a quantity of the measurement points per process wafer is at least tenfold a quantity of the metrology sites. A sampling model that includes at least a wafer model is updated on the basis of the measurement values. The sampling plan is updated on the basis of an assessment of deviations of the measurement values from a current sampling model.

A method, a computer system and an apparatus are disclosed for inspection recipe generation for the automated inspection of semiconductor devices. In order to generate the inspection recipe a reference data set is used. Automatic inspection is carried out with an initial recipe on images of dies of the reference data set (reference wafermap). The detected inspection results from the automatic inspection are classified and the classified inspection results are compared with an expert classification of defects in dies. Overkill and underkill numbers are automatically generated. According to the overkill and underkill numbers the inspection recipe parameters are modified. Automatic inspection is repeated if the detection and/or the classification are below a predefined threshold.

A method of manufacturing a semiconductor device includes defining a sampling plan in a process control system. Measurement values are obtained at the first number N of the sample points. The first number of measurement values are modelled using a wafer model to generate a first set of coefficients according to a reference model. A second number M of the first number N of sample points is randomly selected. The second number M of measurement values obtained at the second number M of sample points is modelled using the wafer model to generate a second set of coefficients according to a phase_1 model. One of the M sample points is randomly replaced by one of the N−M sample points to obtain a subsample. The measurement values of the subsample are modelled using the wafer model to generate a third set of coefficients according to a phase_2 model.

Multicomponent module assembly by identifying a failed site on a laminate comprising a plurality of sites, adding a machine discernible mark associated with the failed site, placing an electrically good element at a successful site; and providing an MCM comprising the laminate, and the electrically good element.

Method and machine utilizes the real-time recipe to perform weak point inspection on a series of wafers during the fabrication of integrated circuits. Each real-time recipe essentially corresponds to a practical fabrication history of a wafer to be examined and/or the examination results of at least one examined wafer of same “lot”. Therefore, different wafers can be examined by using different recipes where each recipe corresponds to a specific condition of a wafer to be examined, even these wafers are received by a machine for examining at the same time.

A data collection system for semiconductor manufacturing includes: T substrate processing tools, where each of the T substrate processing tools includes: N processing chambers, where each of the N processing chambers includes a processing chamber controller configured to receive a plurality of different types of data during operating of the corresponding one of the N processing chambers, where the plurality of different types of data have different formats, where the processing chamber controller is further configured to format the plurality of different types of data into formatted data, and where T and N are integers; and a data diagnostic services computer configured to: receive and store the formatted data as categories in a common file having a table-like data structure including rows with contextual data; and in response to a request, generate an output file including a subset of the data from the common file.

A method for configuring a semiconductor manufacturing process, the method including: providing an initial prediction model including a plurality of model parameters to one or more remote locations; receiving at least one updated model parameter from the one or more remote locations, the at least one model parameter is updated by training the initial prediction model with local data at the one or more remote locations; determining aggregated model parameters based on the at least one updated model parameter received from the one or more remote locations; and adjusting the initial prediction model based on the aggregated model parameters, the adjusted prediction model being operable to configure the semiconductor manufacturing process.

A method for generating a sampling scheme for a device manufacturing process, the method including: obtaining a measurement data time series of a plurality of processed substrates; transforming the measurement data time series to obtain frequency domain data; determining, using the frequency domain data, a temporal sampling scheme; determining an error offset introduced by the temporal sampling scheme on the basis of measurements on substrates performed according to the temporal sampling scheme; and determining an improved temporal sampling scheme to compensate the error offset.