A method and computing system operable for displaying a first routing diagram on a display. The first routing diagram is a member of a first set of routing diagrams that includes multiple different routing diagrams. Displaying the first routing diagram includes displaying a user-selectable control and a first portion of a particular routable component within the first routing diagram. The user-selectable control corresponds to a location of the display where a first terminal connected to the particular routable component or a second terminal connected or connectable to the first terminal is displayed. An additional function include determining, by a processor, a selection of the user-selectable control occurs while the first routing diagram is displayed on the display. A further function includes displaying, on the display in response to determining the selection of the user-selectable control occurs, the second routing diagram with a second portion of the particular routable component highlighted.

A method for designing a system on a target device includes identifying portions in the system to preserve based on comparing structural characteristics of the system with another system. Design results from the another system are reused for portions in the system that are preserved.

A method for designing a system on a target device includes identifying portions in the system to preserve based on comparing structural characteristics of the system with another system. Design results from the another system are reused for portions in the system that are preserved.

A method for detecting defects in a semiconductor device includes pre-training a pre-trained convolutional neural network (CNN) model using a sampled clean data set extracted from a first data set; training a normal convolutional neural network model and a label-noise convolutional neural network model using first data of the first data set and the pre-trained convolutional neural network model. The method also includes outputting a first prediction result on whether second data of a second data set is good or bad using the second data and the normal convolutional neural network model; and outputting a second prediction result on whether second data is good or bad using the second data and the label-noise convolutional neural network model. The first prediction result is compared with the second prediction result to perform noise correction when there is a label difference. Third data created as results of the noise correction is added to the sampled clean data set. The normal convolutional neural network model and the label-noise convolutional neural network model are additionally using the sampled clean data set with the third data added.

A method for detecting defects in a semiconductor device includes pre-training a pre-trained convolutional neural network (CNN) model using a sampled clean data set extracted from a first data set; training a normal convolutional neural network model and a label-noise convolutional neural network model using first data of the first data set and the pre-trained convolutional neural network model. The method also includes outputting a first prediction result on whether second data of a second data set is good or bad using the second data and the normal convolutional neural network model; and outputting a second prediction result on whether second data is good or bad using the second data and the label-noise convolutional neural network model. The first prediction result is compared with the second prediction result to perform noise correction when there is a label difference. Third data created as results of the noise correction is added to the sampled clean data set. The normal convolutional neural network model and the label-noise convolutional neural network model are additionally using the sampled clean data set with the third data added.

This disclosure relates to signal observability rating. In an example, a method can include propagating a clock signal through a respective module of a circuit design in a forward and backward direction, evaluating clock signal propagation results for the respective module based on a forward and backward clock signal propagation of the clock signal to compute an observability rating for a data signal to be processed by the respective module during formal verification, and updating a current observability rating of the respective property for the data signal to the computed observability rating.

One example of the present disclosure is an integrated circuit (IC). The IC includes an inverter with an input and an output, a clock transmission gate coupled to the output of the inverter; and a plurality of storage cells. The clock transmission gate is coupled to each of the plurality of storage cells, wherein each of the plurality of storage cells comprises a plurality of nodes arranged based on a minimum spacing.

One example of the present disclosure is an integrated circuit (IC). The IC includes an inverter with an input and an output, a clock transmission gate coupled to the output of the inverter; and a plurality of storage cells. The clock transmission gate is coupled to each of the plurality of storage cells, wherein each of the plurality of storage cells comprises a plurality of nodes arranged based on a minimum spacing.

Existing techniques of managing Electronic Design Automation (EDA) on cloud are based on pre-defined policies which result in costly burst patterns and server farm tilt. Embodiments of present disclosure overcomes these drawbacks by a method and system for managing EDA on cloud which employ machine learning to predict optimal resource configurations for deploying EDA jobs and configuration circuit on cloud that holds resources required by the optimal resource configuration. Further, different Cloud Service Providers (CSP) are evaluated to determine least cost CSP which has the desired configuration circuit. Completion time of jobs and time required to burst the jobs on cloud are calculated based on which a wait time is determined. The jobs are retained in the queue for corresponding wait time before deploying them on the cloud. The jobs are deployed on the on-prem infrastructure if resources are freed up before the wait time.

Techniques regarding the development and/or analysis of one or more quantum computing programs are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a circuit component, operatively coupled to the processor, that can create a quantum computing program over a period of time. The computer executable components can also comprise a visualization component, operatively coupled to the processor, that can generates a quantum state visualization that depicts a characterization of the quantum computing program over the period of time.