SYSTEMS, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR TRANSISTOR COMPACT MODELING USING ARTIFICIAL NEURAL NETWORKS

    公开(公告)号:US20220114317A1

    公开(公告)日:2022-04-14

    申请号:US17465361

    申请日:2021-09-02

    Abstract: A computer implemented method for determining performance of a semiconductor device is provided. The method includes providing training data comprising input state values and training capacitance values to a neural network executing on a computer system; processing the input state values through the neural network to generate modeled charge values; converting the modeled charge values to modeled capacitance values; determining, by the computer system, whether the training capacitance values of the training data are within a threshold value of the modeled capacitance values utilizing a loss function that omits the modeled charge values; and in response to determining that the training capacitance values of the training data are within the threshold value of the modeled capacitance values, converting, by the computer system, the neural network to a circuit simulation code to generate a converted neural network.

    SYSTEM AND METHOD FOR COMPACT NEURAL NETWORK MODELING OF TRANSISTORS

    公开(公告)号:US20200320366A1

    公开(公告)日:2020-10-08

    申请号:US16430219

    申请日:2019-06-03

    Abstract: A method for generating a model of a transistor includes: initializing hyper-parameters; training the neural network in accordance with the hyper-parameters and training data relating transistor input state values to transistor output state values to compute neural network parameters; determining whether the transistor output state values of the training data match an output of the neural network; porting the neural network to a circuit simulation code to generate a ported neural network; simulating a test circuit using the ported neural network to simulate behavior of a transistor of the test circuit to generate simulation output; determining whether a turnaround time of the generation of the simulation output is satisfactory; in response to determining that the turnaround time is unsatisfactory, re-training the neural network based on updated hyper-parameters; and in response to determining that the turnaround time is satisfactory, outputting the ported neural network as the model of the transistor.

    IMPORTANCE SAMPLING METHOD FOR MULTIPLE FAILURE REGIONS

    公开(公告)号:US20190265296A1

    公开(公告)日:2019-08-29

    申请号:US16406868

    申请日:2019-05-08

    Abstract: In a method of circuit yield analysis, the method includes: detecting a plurality of failed samples respectively located at a plurality of failure regions in a multi-dimensional parametric space; clustering the failed samples to identify the failure regions; filtering features of the failed samples to determine a parameter component that is a non-principal component in affecting circuit yield; applying a dimensional reduction method on a dimension corresponding to the parameter component; and constructing a final importance sampling (IS) distribution function using a mixed Gaussian (mGaussian) function corresponding to all of the failure regions containing a rare failure event.

    IMPORTANCE SAMPLING METHOD FOR MULTIPLE FAILURE REGIONS

    公开(公告)号:US20180074124A1

    公开(公告)日:2018-03-15

    申请号:US15365808

    申请日:2016-11-30

    CPC classification number: G01R31/31718 G01R31/2894 G01R31/3177

    Abstract: A method of circuit yield analysis for evaluating rare failure events existing in multiple disjoint failure regions defined by a multi-dimensional parametric space, the method including performing initial sampling to detect failed samples respectively located at multiple failure regions in the multi-dimensional parametric space, performing clustering to identify the failure regions, performing feature filtering to determine which parameter component is a non-principal component in affecting circuit yield, applying a dimensional reduction method on a dimension corresponding to the parameter component, optimizing an importance sampling (IS) distribution function corresponding to each of the failure regions, and constructing a final importance sampling (IS) distribution function using a mixed Gaussian (mGaussian) function corresponding to all of the failure regions.

    Importance sampling method for multiple failure regions

    公开(公告)号:US10330727B2

    公开(公告)日:2019-06-25

    申请号:US15365808

    申请日:2016-11-30

    Abstract: A method of circuit yield analysis for evaluating rare failure events existing in multiple disjoint failure regions defined by a multi-dimensional parametric space, the method including performing initial sampling to detect failed samples respectively located at multiple failure regions in the multi-dimensional parametric space, performing clustering to identify the failure regions, performing feature filtering to determine which parameter component is a non-principal component in affecting circuit yield, applying a dimensional reduction method on a dimension corresponding to the parameter component, optimizing an importance sampling (IS) distribution function corresponding to each of the failure regions, and constructing a final importance sampling (IS) distribution function using a mixed Gaussian (mGaussian) function corresponding to all of the failure regions.

    DEVICE DISLOCATION STRESS SIMULATION
    18.
    发明申请

    公开(公告)号:US20190155971A1

    公开(公告)日:2019-05-23

    申请号:US15875916

    申请日:2018-01-19

    Abstract: A system and method for calculating stress in a device includes receiving an analytic solution domain and calculating initial analytic values for displacement and stress for a dislocation in the domain and creating a stress profile using the initial displacement and the initial stress as initial values of a stress equilibration equation.

    SYSTEM AND METHOD FOR CIRCUIT SIMULATION BASED ON RECURRENT NEURAL NETWORKS

    公开(公告)号:US20190138897A1

    公开(公告)日:2019-05-09

    申请号:US15951052

    申请日:2018-04-11

    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.

    Systems, methods and computer program products for analyzing performance of semiconductor devices

    公开(公告)号:US10204188B2

    公开(公告)日:2019-02-12

    申请号:US14991124

    申请日:2016-01-08

    Abstract: A computer implemented method for determining performance of a semiconductor device is provided. The method includes providing a technology computer aided design data set corresponding to nominal performance of the semiconductor device, identifying a plurality of process variation sources that correspond to process variations that occur during the manufacturing of the semiconductor device, generating a nominal value look-up table of electrical parameters of the semiconductor device using nominal values of each of the plurality of process variation sources, and generating a plurality of process variation look-up tables of electrical parameters of the semiconductor device using variation values corresponding to each of the plurality of process variation sources that are identified as corresponding to the semiconductor device.

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