A computing system may include a shadow feature model training engine configured to access a set of integrated circuit (IC) layouts and capacitance values determined for components of the set of IC layouts. The shadow feature model training engine may construct shadow feature training data for the set of IC layouts, including by extracting shadow features for components of the set of IC layouts, combine extracted shadow features and determined capacitance values to form the shadow feature training data, and may further train a machine-learning (ML) model with the shadow feature training data. The computing system may also include a shadow feature application engine configured to extract shadow features for components of an input IC layout and determine capacitance values for the input IC layout via the trained ML model.

A computing system may include a shadow feature model training engine configured to access a set of integrated circuit (IC) layouts and capacitance values determined for components of the set of IC layouts. The shadow feature model training engine may construct shadow feature training data for the set of IC layouts, including by extracting shadow features for components of the set of IC layouts, combine extracted shadow features and determined capacitance values to form the shadow feature training data, and may further train a machine-learning (ML) model with the shadow feature training data. The computing system may also include a shadow feature application engine configured to extract shadow features for components of an input IC layout and determine capacitance values for the input IC layout via the trained ML model.

Method, apparatus and product for modeling of quantum circuits and usages thereof. A method comprises obtaining a model of a quantum circuit that comprises a set of decision variables, corresponding domains, and constraints, wherein the set of decision variables comprise gate assignment decision variables that define an assignment of a gate to a qubit in a cycle in the quantum circuit. The method comprises automatically determining a set of valuations for the set of decision variables. The set of valuations are selected from the corresponding domains and satisfy the constraints. Based on the set of valuations the quantum circuit is synthesized.

Method, apparatus and product for modeling of quantum circuits and usages thereof. A method comprises obtaining a model of a quantum circuit that comprises a set of decision variables, corresponding domains, and constraints, wherein the set of decision variables comprise gate assignment decision variables that define an assignment of a gate to a qubit in a cycle in the quantum circuit. The method comprises automatically determining a set of valuations for the set of decision variables. The set of valuations are selected from the corresponding domains and satisfy the constraints. Based on the set of valuations the quantum circuit is synthesized.

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.

Systems and techniques that facilitate quantum circuit valuation are provided. In various embodiments, a system can comprise an input component that can access a first quantum circuit. In various embodiments, the system can further comprise a valuation component that can appraise the first quantum circuit based on one or more factors (e.g., frequency factor, complexity factor, resource factor, similarity factor), thereby yielding a value score that characterizes the first quantum circuit. In various instances, the system can further comprise an execution component that can recommend deployment of the first quantum circuit based on determining that the value score exceeds a threshold.

Systems and techniques that facilitate quantum circuit valuation are provided. In various embodiments, a system can comprise an input component that can access a first quantum circuit. In various embodiments, the system can further comprise a valuation component that can appraise the first quantum circuit based on one or more factors (e.g., frequency factor, complexity factor, resource factor, similarity factor), thereby yielding a value score that characterizes the first quantum circuit. In various instances, the system can further comprise an execution component that can recommend deployment of the first quantum circuit based on determining that the value score exceeds a threshold.

In a method of optimizing a lithography model in a lithography simulation, a mask is formed in accordance with a given layout, a wafer is printed using the mask, a pattern formed on the printed wafer is measured, a wafer pattern is simulated using a wafer edge bias table and the given mask layout, a difference between the simulated wafer pattern and the measured pattern is obtained, and the wafer edge table is adjusted according to the difference.

In a method of optimizing a lithography model in a lithography simulation, a mask is formed in accordance with a given layout, a wafer is printed using the mask, a pattern formed on the printed wafer is measured, a wafer pattern is simulated using a wafer edge bias table and the given mask layout, a difference between the simulated wafer pattern and the measured pattern is obtained, and the wafer edge table is adjusted according to the difference.