An electrical device comprising a reconfigurable integrated circuit that includes paired top electrodes and bottom electrodes separated from each other by an active layer.

An electrical device comprising a reconfigurable integrated circuit that includes paired top electrodes and bottom electrodes separated from each other by an active layer.

Systems or methods of the present disclosure may provide an integrated circuit system. The integrated circuit system may implement a circuit design. Design software models a circuit design for the integrated circuit system and the circuit design is agnostic of physical layer circuitry of the integrated circuit system. The design software may generate configuration data based on the circuit design and transfer the configuration data to the integrated circuit system to cause programmable logic of the integrated circuit system to implement the circuit design.

Systems or methods of the present disclosure may provide an integrated circuit system. The integrated circuit system may implement a circuit design. Design software models a circuit design for the integrated circuit system and the circuit design is agnostic of physical layer circuitry of the integrated circuit system. The design software may generate configuration data based on the circuit design and transfer the configuration data to the integrated circuit system to cause programmable logic of the integrated circuit system to implement the circuit design.

The present disclosure relates to a method for use with an electronic design. Embodiments may include receiving one or more user defined processor configurations at a processor generator. Embodiments may also include generating a customized testbench based upon, at least in part, the user defined processor configurations and generating an RTL model while the customized testbench is generating.

The present disclosure relates to a method for use with an electronic design. Embodiments may include receiving one or more user defined processor configurations at a processor generator. Embodiments may also include generating a customized testbench based upon, at least in part, the user defined processor configurations and generating an RTL model while the customized testbench is generating.

A method of designing a microwave filter using a computerized filter optimizer, comprises generating a filter circuit design in process (DIP) comprising a plurality of circuit elements having a plurality of resonant elements and one or more non-resonant elements, optimizing the DIP by inputting the DIP into the computerized filter optimizer, determining that one of the plurality of circuit elements in the DIP is insignificant, removing the one insignificant circuit element from the DIP, deriving a final filter circuit design from the DIP, and manufacturing the microwave filter based on the final filter circuit design.

A method of identifying, in a circuit design of an electronic circuit, a source of side channel leakage of the electronic circuit. The method comprises: a) simulating over a leakage time interval an operation of the circuit in response to at least one stimulus, thereby deriving for each one of the at least one stimulus per circuit part of the electronic circuit a respective simulated leakage quantity circuit part response over the leakage time interval; b) obtaining for each one of the at least one stimulus an expected leakage quantity response over the leakage time interval from a processing of each one of the at least one stimulus by a leakage model, the leakage model modelling a leak-quantity at a processing of a secure asset; c) determining respective circuit part correlations over the leakage time interval between the respective simulated leakage quantity circuit part responses and the expected leakage quantity responses; d) ranking the circuit parts based on the circuit part correlations between the respective simulated leakage quantity circuit part responses and the expected leakage quantity responses and e) identifying as the source of side channel leakage the circuit part for which a highest one of the circuit correlations has been determined between the expected leakage quantity responses and the respective simulated leakage quantity circuit part responses.

A parallel analog circuit automatic optimization method based on genetic algorithm and machine learning comprises global optimization based on genetic algorithm and local optimization based on machine learning, with the global optimization and the local optimization performed alternately. The global optimization based on genetic algorithm utilizes parallel SPICE simulations to improve the optimization efficiency while guaranteeing the optimization accuracy, combined with parallel computing. The local optimization based on machine learning establishes a machine learning model near the global optimal point obtained by the global optimization, and uses the machine learning model to replace the SPICE simulator, thus reducing the time costs brought by a large number of simulations.

A parallel analog circuit automatic optimization method based on genetic algorithm and machine learning comprises global optimization based on genetic algorithm and local optimization based on machine learning, with the global optimization and the local optimization performed alternately. The global optimization based on genetic algorithm utilizes parallel SPICE simulations to improve the optimization efficiency while guaranteeing the optimization accuracy, combined with parallel computing. The local optimization based on machine learning establishes a machine learning model near the global optimal point obtained by the global optimization, and uses the machine learning model to replace the SPICE simulator, thus reducing the time costs brought by a large number of simulations.