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A multi-source heterogeneous data anomaly identification and feature screening method

ActiveCN116245061BRealize automatic generationGood performance parametersConstraint-based CADNeural architecturesAnalog circuit designAlgorithm
This invention relates to the field of analog circuit design technology and discloses a method for anomaly identification and feature selection of multi-source heterogeneous data. It involves generating and optimizing a custom multi-parameter, multi-constraint circuit topology within an analog circuit environment, using an analog integrated circuit netlist as graph theory input. This method uses a multi-constraint custom circuit, taking an initial circuit structure as graph theory input, predicting the size of the analog integrated circuit using maximum likelihood estimation, and updating the simulated netlist as the graph theory input. Through comparison, the size is continuously updated, ultimately achieving automatic generation of the circuit structure from the desired structure to the final structure. Through state observation, a graph neural network model is used to optimize the circuit topology, ultimately outputting a highly feasible circuit topology whose performance parameters can reach or even surpass those of manually designed analog integrated circuits.
Owner:GUANGZHOU UNIVERSITY