System, method, and computer program product for analog and mix-signal circuit placement

    公开(公告)号:US12045730B1

    公开(公告)日:2024-07-23

    申请号:US16238274

    申请日:2019-01-02

    CPC classification number: G06N3/126 G06F30/392

    Abstract: The present disclosure relates to a computer-implemented method for genetic placement of analog and mix-signal circuit components. Embodiments may include receiving an unplaced layout associated with an electronic circuit design and grouping requirements. Embodiments may also include identifying one or more instances that need to be placed in the unplaced layout and areas of the unplaced layout configured to receive the instances. Embodiments may further include analyzing one or more instances that need to be placed in the unplaced layout and the areas of the unplaced layout configured to receive the instances, wherein analyzing is based upon a row-based data structure. Embodiments may also include determining a location and an orientation for each of the one or more instances based upon the genetic algorithm and generating a placed layout based upon the determined location and orientation for each of the instances.

    Failure boundary classification and corner creation for scaled-sigma sampling

    公开(公告)号:US10325056B1

    公开(公告)日:2019-06-18

    申请号:US15179087

    申请日:2016-06-10

    Abstract: A system, methods, and a computer program product for estimating a yield and creating corners of a circuit design with the aid of a failure boundary classification. The system, methods and computer program product provide for determining, based on how many sampling factors have failures, whether data samples are sufficient as input to scaled-sigma sampling. If the data samples are insufficient, the failure boundary classification is usable to determine whether the yield is high enough to meet a yield target. A design corner can be located by applying a binary search to results of scaled-sigma sampling. The failure boundary classification can aid in setting up the search.

    Yield estimation for a post-layout circuit design

    公开(公告)号:US10275555B1

    公开(公告)日:2019-04-30

    申请号:US15280839

    申请日:2016-09-29

    Abstract: Method for estimating a yield of a post-layout circuit design is provided. In one aspect, a method includes obtaining a first pre-layout parameter and a second pre-layout parameter from pre-layout simulation samples of a circuit. The method also modeling a prior distribution of a first post-layout parameter and a second post-layout parameter based on the first pre-layout parameter, the second pre-layout parameter, a first hyper-parameter, and second hyper-parameter. The method further includes calculating the first hyper-parameter and the second hyper-parameter using a cross-validation, obtaining the first post-layout parameter and the second post-layout parameter based on the first hyper-parameter and the second hyper-parameter and estimating the yield of the circuit design using a non-normal distribution parameterized by the obtained first post-layout parameter and second post-layout parameter.

    Efficient extraction of the worst sample in Monte Carlo simulation

    公开(公告)号:US09836564B1

    公开(公告)日:2017-12-05

    申请号:US14683021

    申请日:2015-04-09

    CPC classification number: G06F17/5022 G06F17/5045

    Abstract: A system, method, and computer program product for reducing the number of Monte Carlo simulation samples required to determine if a design meets design specifications. The worst sample for each specification acts as a design corner to substitute for a full design verification. Embodiments determine the maximum number of samples needed, perform an initial performance modeling using an initial set of samples, and estimate the failure probability of each of the remaining samples based on the performance model. Embodiments then simulate remaining samples with a computer-operated Monte Carlo circuit simulation tool in decreasing design specification model accuracy order, wherein the sample predicted most likely to fail each specification is simulated first. Re-use of simulation results progressively improves models. Probability based stop criteria end the simulation early when the worst samples have been confidently found. A potential ten-fold reduction in overall specification verification time may result.

    Efficient extraction of K-sigma corners from Monte Carlo simulation

    公开(公告)号:US09805158B1

    公开(公告)日:2017-10-31

    申请号:US14942956

    申请日:2015-11-16

    CPC classification number: G06F17/5081 G06F17/5009

    Abstract: A system, method, and computer program product for efficiently finding the best Monte Carlo simulation samples for use as design corners for all design specifications to substitute for a full circuit design verification. Embodiments calculate a corner target value matching an input variation level by modeling the circuit performance with verified accuracy, estimate the corner based on a response surface model such that the corner has the highest probability density (or extrapolation from the worst sample if the model is inaccurate), and verify and/or adjust the corner by performing a small number of additional simulations. Embodiments also estimate the probability that a design already meets the design specifications at a specified variation level. Composite multimodal and non-Gaussian probability distribution functions enhance model accuracy. The extracted design corners may be of particular utility during circuit design iterations. A potential twenty-fold reduction in overall design specification verification time may be achieved.

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