Abstract: A system for reconstructing wafer maps of semiconductor wafers includes: a processor; and memory having instructions stored thereon that, when executed by the processor, cause the processor to: receive test data of a wafer at sparse sampling locations of the wafer, the sparse sampling locations being selected based on a probing mask; and compute a reconstructed wafer map by performing compressed sensing with Zernike polynomials on the test data at sparse locations of the wafer.

Abstract: The present disclosure relates to a computer-implemented method for electronic design is provided. Embodiments may include receiving, using at least one processor, an electronic design having one or more unoptimized nets. Embodiments may further include applying a genetic algorithm to the electronic design, wherein the genetic algorithm includes a multi-stage routing analysis. A first stage analysis may apply a device-level global routing analysis, a second stage analysis may include an intra-row routing analysis, a third stage may include an inter-row routing analysis, and a fourth stage may include a post-routing optimization analysis. Embodiments may also include generating an optimized routing of the one or more unoptimized nets and displaying the optimized routing at a graphical user interface.

Abstract: A standard cell design system is provided. The standard cell design system includes at least one processor configured to implement: a control engine that determines planar parameters and vertical parameters of a target standard cell, a three-dimensional structure generating engine that generates a three-dimensional structure of the target standard cell based on the planar parameters and the vertical parameters, an extraction engine that extracts a standard cell model of the target standard cell from the three-dimensional structure, an assessment engine that performs a plurality of assessment operations based on the standard cell model, and an auto-optimizing engine that adjusts, based on a machine learning algorithm, the planar parameters and the vertical parameters based on results of the plurality of assessment operations.

Abstract: According to one embodiment of the present invention a circuit simulator configured to simulate a degraded output of a circuit including a plurality of transistors includes: a behavioral recurrent neural network configured to receive an input waveform and to compute a circuit output waveform; a feature engine configured to model one or more degraded circuit elements in accordance with an aging time, to receive the circuit output waveform and to output a plurality of degraded features; and a physics recurrent neural network configured to receive the plurality of degraded features from the feature engine and to simulate the degraded output of the circuit.

Abstract: A standard cell design system is provided. The standard cell design system includes at least one processor configured to implement: a control engine that determines planar parameters and vertical parameters of a target standard cell, a three-dimensional structure generating engine that generates a three-dimensional structure of the target standard cell based on the planar parameters and the vertical parameters, an extraction engine that extracts a standard cell model of the target standard cell from the three-dimensional structure, an assessment engine that performs a plurality of assessment operations based on the standard cell model, and an auto-optimizing engine that adjusts, based on a machine learning algorithm, the planar parameters and the vertical parameters based on results of the plurality of assessment operations.

Abstract: According to one embodiment of the present invention a circuit simulator configured to simulate a degraded output of a circuit including a plurality of transistors includes: a behavioral recurrent neural network configured to receive an input waveform and to compute a circuit output waveform; a feature engine configured to model one or more degraded circuit elements in accordance with an aging time, to receive the circuit output waveform and to output a plurality of degraded features; and a physics recurrent neural network configured to receive the plurality of degraded features from the feature engine and to simulate the degraded output of the circuit.

Abstract: A system for reconstructing wafer maps of semiconductor wafers includes: a processor; and memory having instructions stored thereon that, when executed by the processor, cause the processor to: receive test data of a wafer at sparse sampling locations of the wafer, the sparse sampling locations being selected based on a probing mask; and compute a reconstructed wafer map by performing compressed sensing with Zernike polynomials on the test data at sparse locations of the wafer.