In a method of verifying a semiconductor device, input data defining the semiconductor device including a plurality of blocks is received. A first simulation environment is generated for a top module and at least one target block of the plurality of blocks in the top module. The first simulation environment includes power wiring information and additional power-related information. The top module represents an entire structure of the semiconductor device. A second simulation environment is generated for non-target blocks of the plurality of blocks other than the at least one target block. The second simulation environment is different from the first simulation environment. A verification operation is performed on the semiconductor device based on a hybrid simulation environment in which the first simulation environment and the second simulation environment are combined.

In a method of verifying a semiconductor device, input data defining the semiconductor device including a plurality of blocks is received. A first simulation environment is generated for a top module and at least one target block of the plurality of blocks in the top module. The first simulation environment includes power wiring information and additional power-related information. The top module represents an entire structure of the semiconductor device. A second simulation environment is generated for non-target blocks of the plurality of blocks other than the at least one target block. The second simulation environment is different from the first simulation environment. A verification operation is performed on the semiconductor device based on a hybrid simulation environment in which the first simulation environment and the second simulation environment are combined.

Training of a machine learning model used to infer estimated delays of circuit routes during placement and routing of a circuit design. Training can include selecting sample pairs of source pins and destination pins of an integrated circuit (IC) device, and determining respective delays of shortest paths that connect the source pins to the destination pins of the sample pairs based on a resistance-capacitance model of wires that form the shortest paths on the IC device. Respective sets of features are determined for the shortest paths, and the model is trained using the respective sets of features and the respective delays as labels. The machine learning model can be provided to an electronic design automation tool for estimating delays.

Disclosed in the present invention is a flexible modeling method for a timing constraint of a register. Simulation ranges of input terminal transition time, clock terminal transition time, and output load capacitance of a register are determined first, simulation is performed under each combination of input terminal transition time, clock terminal transition time, and output load capacitance to obtain a timing constraint range, then setup slack and hold slack are extracted in this constraint range with a particular interval, and then simulation is performed to obtain a clock terminal-to-output terminal delay. Finally, a mutually independent timing model of the register is established by using an artificial neural network, where the clock terminal-to-output terminal delay is modeled as a function of the input terminal transition time, the clock terminal transition time, the output load capacitance, the setup slack, the hold slack, and an output terminal state. A flexible timing constraint model in the present invention has advantages of low simulation overheads and high prediction precision, and is of great significance for static timing analysis timing signoff of a digital integrated circuit.

Disclosed in the present invention is a flexible modeling method for a timing constraint of a register. Simulation ranges of input terminal transition time, clock terminal transition time, and output load capacitance of a register are determined first, simulation is performed under each combination of input terminal transition time, clock terminal transition time, and output load capacitance to obtain a timing constraint range, then setup slack and hold slack are extracted in this constraint range with a particular interval, and then simulation is performed to obtain a clock terminal-to-output terminal delay. Finally, a mutually independent timing model of the register is established by using an artificial neural network, where the clock terminal-to-output terminal delay is modeled as a function of the input terminal transition time, the clock terminal transition time, the output load capacitance, the setup slack, the hold slack, and an output terminal state. A flexible timing constraint model in the present invention has advantages of low simulation overheads and high prediction precision, and is of great significance for static timing analysis timing signoff of a digital integrated circuit.

A multi-bit flip-flop includes a first flip-flop, a second flip-flop and a first inverter. The first flip-flop has a first driving capability. The second flip-flop has a second driving capability different from the first driving capability. The first inverter is configured to receive a first clock signal on a first clock pin, and is configured to generate a second clock signal inverted from the first clock signal. The first flip-flop and the second flip-flop are configured to share at least the first clock pin.

A multi-bit flip-flop includes a first flip-flop, a second flip-flop and a first inverter. The first flip-flop has a first driving capability. The second flip-flop has a second driving capability different from the first driving capability. The first inverter is configured to receive a first clock signal on a first clock pin, and is configured to generate a second clock signal inverted from the first clock signal. The first flip-flop and the second flip-flop are configured to share at least the first clock pin.

Embodiments of the present disclosure provide a method for forming a memory, including: forming a memory core using a plurality of cells from a library of cells, wherein each cell in the library of cells follows standard cell row placement constraints and includes a static timing model, and wherein the plurality of cells includes a dynamic bitcell; wherein forming the memory core further includes connecting a plurality of the bitcells via abutment to form a rectangular array of bitcells such that bitlines of the bitcells and wordlines of the bitcells connect by abutment and are shared between adjacent bitcells in the array of bitcells.

Embodiments of the present disclosure provide a method for forming a memory, including: forming a memory core using a plurality of cells from a library of cells, wherein each cell in the library of cells follows standard cell row placement constraints and includes a static timing model, and wherein the plurality of cells includes a dynamic bitcell; wherein forming the memory core further includes connecting a plurality of the bitcells via abutment to form a rectangular array of bitcells such that bitlines of the bitcells and wordlines of the bitcells connect by abutment and are shared between adjacent bitcells in the array of bitcells.

Systems and methods are disclosed for generation and testing of integrated circuit designs with clock crossings between clock domains and reset crossings between reset domains. These may allow for the rapid design and testing (e.g. silicon testing) of processors and SoCs. Clock crossings may be automatically generated between modules, inferring the values of design parameters, such as a signaling protocol (e.g. a bus protocol), directionality, and/or a clock crossing type (e.g., synchronous, rational divider, or asynchronous), of a clock crossing. Reset crossings may be automatically generated in a similar manner. For example, implicit classes may be used to generate clock crossings or reset crossings in a flexible manner. For example, these system and methods may be used to rapidly connect a custom processor design, including one or more IP cores, to a standard input/output shell for a SoC design to facilitate rapid silicon testing of the custom processor design.