A method comprising using at least one hardware processor for: running a Monte Carlo simulation of possible integrated circuit (IC) process variations of each of a plurality of IC cell types, wherein each of the plurality of IC cell types is defined by multiple specific transistors and multiple specific interconnects; based on the results of the Monte Carlo simulation, creating a library of IC cell types and their corresponding behavioral values for each of the possible IC process variations, and storing the library in a non-transient memory; receiving an IC design embodied as a digital file; correlating the received IC design with the library; and predicting a frequency distribution and a power distribution of ICs manufactured according to the IC design.

A method and a system for predicting a gas content in transformer oil based on a joint model are provided and belong a field of transformer failure prediction. The method includes the following: determining a type and a time series of gas to be predicted related to a failure, processing an original series by adopting empirical mode decomposition (EMD) and local mean decomposition (LMD) for a non-stationarity characteristic of a dissolved gas concentration series in oil; performing normalization on each sub-series component, dividing a training sample and a test sample; and establishing a deep belief network (DBN) prediction model for each of the sub-series components for training, performing superposition and reconstruction on the established DBN prediction model to perform characteristic extraction and classification on multi-dimensional data of the failure, evaluating prediction performance of the prediction model through calculating an error index.

Data characterizing an assembly/structure containing a first stage and a second stage are received in a computer system. The first stage can contain a first cyclic symmetry and the second stage can contain a second cyclic symmetry. The first cyclic symmetry and the second cyclic symmetry are different from each other. Each stage may be a 360-degree stage. The received data includes a first mesh representing the first stage and a second mesh representing the second stage. Multiple simulation results are obtained using corresponding models in respective dynamic analyses of the assembly. Each model includes a set of constraints for coupling the first mesh and the second mesh. The set of constraints is associated with a group of distinct linked harmonic indices. Physical behaviors of the assembly are then calculated using one or more sets of the simulation results. Constraints are in forms of constraint equations.

According to some embodiments, the present disclosure provides a method for determining wafer inspection parameters. The method includes identifying an area of interest in an IC design layout, performing an inspection simulation on the area of interest by generating a plurality of simulated optical images from the area of interest using a plurality of optical modes, and selecting, based on the simulated optical images, at least one of the optical modes to use for inspecting an area of a wafer that is fabricated based on the area of interest in the IC design layout.

The present disclosure provides a strip flatness prediction method considering lateral spread during rolling. The method includes: step 1: acquiring strip parameters, roll parameters and rolling process parameters; step 2: introducing a change factor of a lateral thickness difference before and after rolling and a lateral spread factor by considering lateral metal flow, and constructing a strip flatness prediction model based on the coupling of flatness, crown and lateral spread; step 3: constructing a three-dimensional (3D) finite element model (FEM) of a rolling mill and a strip, simulating strip rolling by the 3D FEM, extracting lateral displacement and thickness data of the strip during a stable rolling stage, calculating parameters of the strip flatness prediction model based on the coupling of flatness, crown and lateral spread; and step 4: predicting the flatness of the strip by the strip flatness prediction model based on the coupling of flatness, crown and lateral spread.

A method and system for an accelerated design of a virtual product formulation based on an expert-enhanced quantitative formulation network includes sourcing qualitative expert formulation; creating a qualitative formulation network; extracting qualitative network-expansion data based on a category associated with a target product associated with the qualitative formulation network, creating a second set of network components including formulation variable nodes and formulation edge connections; integrating the second set of network components into the qualitative formulation network; transforming the qualitative formulation network integrated with the second set of network components to a quantitative formulation network; designing at least part of a virtual product formulation based on the quantitative formulation network; and generating a target formulation proposal that likely satisfies the target formulation objective based on executing the virtual product formulation as initialized.

Disclosed are a method and an apparatus for string connecting photovoltaic modules. The method includes: acquiring position information of n photovoltaic modules to be connected; categorizing the n photovoltaic modules into m partitions based on the position information of the n photovoltaic modules; generating k candidate connection solutions of an i.sup.th partition in the m partitions, wherein the i.sup.th partition includes m photovoltaic modules, and each of the k candidate connection solutions uses one photovoltaic module in the m photovoltaic modules as a starting point, and obtaining at least one string of photovoltaic modules by simulating connection of the m photovoltaic modules according to a preset connection solution; and selecting a target connection solution from the k candidate connection solutions based on an estimated cable use amount corresponding to each of the k candidate connection solutions.

According to some embodiments, the present disclosure provides a method for determining wafer inspection parameters. The method includes identifying an area of interest in an IC design layout, performing an inspection simulation on the area of interest by generating a plurality of simulated optical images from the area of interest using a plurality of optical modes, and selecting, based on the simulated optical images, at least one of the optical modes to use for inspecting an area of a wafer that is fabricated based on the area of interest in the IC design layout.

A method and system for an accelerated design of a virtual product formulation based on an expert-enhanced quantitative formulation network includes sourcing qualitative expert formulation; creating a qualitative formulation network; extracting qualitative network-expansion data based on a category associated with a target product associated with the qualitative formulation network, creating a second set of network components including formulation variable nodes and formulation edge connections; integrating the second set of network components into the qualitative formulation network; transforming the qualitative formulation network integrated with the second set of network components to a quantitative formulation network; designing at least part of a virtual product formulation based on the quantitative formulation network; and generating a target formulation proposal that likely satisfies the target formulation objective based on executing the virtual product formulation as initialized.

A method for manufacturing-aware editing of circuit layouts driven by predictions regarding predicted manufactured wafer contours generated by a machine-trained network. The method allows for fast edit loops in interactive editing timeframes, in which the predicted manufactured wafer contours corresponding to design edits are presented within seconds of the edits themselves. In some embodiments, the wafer contours take mask OPC/ILT and lithography effects into account, as determined by the machine trained network.