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Cultural simulation model for modeling of agent behavioral expression and simulation data visualization methods

A computer simulation method is provided for modeling the behavioral expression of one or more agents in an environment to be simulated, then running a simulation of the modeled agent(s) against real-world information as input data reflecting changing conditions of the environment being simulated, and obtaining an output based on the modeled agent(s) response(s). The simulation method models the underlying cultural, social, and behavioral characteristics on which agent behaviors and actions are based, rather than modeling fixed rules for the agent's actions. The input data driving the simulation are constituted by real-world information reflecting the changing conditions of the environment being simulated, rather than an artificial set of predefined initial conditions which do not change over time. As a result, the simulation output of the modeled agent's responses to the input information can indicate more accurately how that type of participant in the simulated environment might respond under real-world conditions. Simulations can be run on global networks for agent types of different cultures, societies, and behaviors, with global sources of information. Simulation environments can include problems and situations in a wide range of human activity. Robust new visual tools are provided for discerning patterns and trends in the simulation data, including waveform charts, star charts, grid charts, and pole chart series.
Owner:SEASEER R&D

Industrial process fault detection using principal component analysis

A method and system for use in monitoring/evaluating industrial process such as, for example, plasma processes useful in the fabrication of semiconductor chips, microelectromechanical devices, and the like on semiconductor wafers and the like are provided. In one embodiment, a plasma process fault detection module (100) includes a data selection sub-module (101), a model building/updating sub-module (102), a principal component analysis (PCA) analysis sub-module (103), a model maintenance sub-module (104), a wafer categorization sub-module (105), and a data output sub-module (106). The data selection sub-module (101) obtains selected optical emissions spectra (OES) data for each wafer that is processed. The model building/updating sub-module (102) constructs multiple models from the selected OES data for a number of wafers. The PCA analysis sub-module (103) utilizes PCA techniques to determine whether the selected OES data for a particular wafer differs significantly from that expected for a normal wafer as represented by the models. The model maintenance sub-module (104) saves and retrieves models for different processes, associating the current wafer with the correct process. The wafer categorization sub-module (105) categorizes each wafer based on a scalar metric characterizing the residual spectrum vector. The data output sub-module (106) outputs the results that are obtained to a user.
Owner:PEAK SENSOR SYST
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