Various examples of integrated circuit layouts with line-end extensions are disclosed herein. In an example, a method includes receiving an integrated circuit layout that contains: a first and second set of shapes extending in parallel in a first direction, wherein a pitch of the first set of shapes is different from a pitch of the second set of shapes. A cross-member shape is inserted into the integrated circuit layout that extends in a second direction perpendicular to the first direction, and a set of line-end extensions is inserted into the integrated circuit layout that extend from each shape of the first set of shapes and the second set of shapes to the cross-member shape. The integrated circuit layout containing the first set of shapes, the second set of shapes, the cross-member shape, and the set of line-end extensions is provided for fabricating an integrated circuit.

A method includes acquiring a vector data signal associated with a circuit design, performing a timing update to determine timing information for the circuit design, and identifying a glitch in the circuit design based on a shifted vector waveform. The timing information includes a signal delay associated with a cell of the circuit design. The shifted vector waveform is generated by shifting the vector data signal based on the timing information.

The present invention belongs to the technical field of simulation of power semiconductor modules, and discloses a multi-physics co-simulation method of a power semiconductor module. The multi-physics co-simulation method of the power semiconductor module comprises: adopting professional circuit simulation software PSpice supporting a spice model to be imported into a device, and by designing a specific collaborative analysis method and performing secondary development of a software data exchange interface, i.e. constructing a coupling interface of co-simulation, performing electricity-heat-force co-simulation of two types of software PSpice and COMSOL by adopting an indirect coupling manner. The simulation time is greatly shortened, and the simulation efficiency is improved.

The present invention discloses a calculation method of eddy current loss in magnetic materials based on magnetic-inductance. The present invention proposes a vector model of a magnetic circuit, an eddy current reaction is equivalent to a magnetic-inductance component in the magnetic circuit, and the eddy current loss can be fast calculated by the vector model of the magnetic circuit. When the frequency is high, the eddy current loss dominates an iron loss and can be estimated as an entire iron loss. The present invention proposes the vector model of the magnetic circuit based on which the calculation method of eddy current loss in magnetic materials is proposed as well. Through the proposed method the eddy current loss in magnetic materials can be directly calculated by using the magnetic-inductance and the magnetic flux in the magnetic circuit, which can provide guidance for design and performance evaluation of high-frequency electrical equipment from a brand new viewpoint.

A simulation test system and a simulation test method are provided. The simulation test system includes a control device, a power setting device, and a data capture device. The control device generates a context control signal corresponding to one of a plurality of operating contexts. The power setting device generates at least one of a simulated charging power and a simulated load in response to the context control signal and provides at least one of the simulated charging power and the simulated load to a device under test to configure the device under test to generate test data in response to at least one of the simulated charging power and the simulated load. The data capture device captures the test data and provides the test data to the control device.

To generate a deep learning model, basic training data corresponding to a combination of device data and simulation result data is generated using a compact model that generates the simulation result data indicating characteristics of a semiconductor device corresponding to the device data by performing simulation based on the device data. A deep learning model is trained based on the basic training data such that the deep learning model outputs prediction data indicating the characteristics of the semiconductor device and uncertainty data indicating uncertainty of the prediction data. The deep learning model is retrained based on the uncertainty data. The deep learning model may precisely predict the characteristics of the semiconductor device by training the deep learning model to output the prediction data and the uncertainty data and retraining the deep learning model based on the uncertainty data.

In an approach utilizing static analysis, a processor receives a netlist for an integrated circuit. For at least one node of the integrated circuit in the netlist, a processor calculates (i) a total capacitive load of the respective node and (ii) a minimum required driver size. For a driver of the respective node, a processor (i) determines an effective driver size of the driver based on at least a number of fins of the driver and (ii) determines that the effective driver size exceeds the minimum required driver size multiplied by a predefined sizing margin. A processor, responsive to determining that the effective driver size exceeds the minimum required driver size multiplied by the predefined sizing margin, generates a report, where the report includes at least the driver and a suggestion to reduce the effective size of the driver.

Methods for analyzing electromigration (EM) in an integrated circuit (IC) are provided. A layout of the IC is obtained. A metal segment is selected from the layout according to a current simulation result of the IC. Two first vias are formed over and in contact with the metal segment in the layout. EM rule is kept on the metal segment when a distance between the two first vias is greater than a threshold distance. The EM rule is relaxed on the metal segment when the distance between the two first vias is less than or equal to the threshold distance.

Various embodiments of the teachings herein include a method for simulating an integrated energy system including nonlinear devices. The method may include: receiving a simulation task; building a system of nonlinear equations on the basis of the simulation task and a simulation model of the integrated energy system; and solving a system of nonlinear equations using a linear programming algorithm to obtain a simulation result. The process of establishing the simulation model may include: determining the topological structure of an integrated energy system, the topological structure comprising the devices of the integrated energy system and the connection attributes between the devices; determining general models of the devices and a connector model corresponding to the connection attributes; connecting the general models by means of the connector model to form a simulation model of the integrated energy system; and training the simulation model.

Various embodiments of the teachings herein include a method for optimizing an integrated energy system comprising nonlinear devices. The method may include: receiving an optimization task containing an optimization objective; building a system of nonlinear equations on the basis of the optimization objective and a simulation model of the integrated energy system; solving the system of nonlinear equations using a linear programming algorithm to obtain an optimization result. Establishing the simulation model comprises: determining a topological structure of the integrated energy system, the topological structure comprising the devices of the integrated energy system and the connection attributes between the devices; determining general models of the devices and a connector model corresponding to the connection attributes; connecting the general models using the connector model to form a simulation model of the integrated energy system; and training the simulation model.