A system includes a memory and a processing device, operatively coupled to the memory, to perform operations including receiving, as input to a trained machine learning model for identifying defect impact with respect to at least one type defect type, data associated with a process related to electronic device manufacturing. The data associated with the process comprises at least one of: an input set of recipe settings for processing a component, a set of desired characteristics to be achieved by processing the component, or a set of constraints specifying an allowable range for each setting of the set of recipe settings. The operations further include obtaining an output by applying the data associated with the process to the trained machine learning model. The output is representative of the defect impact with respect to the at least one defect type.

Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N. The method includes generating a set of approximated system variables corresponding to the set of measurements based on the numerical model.

A method for producing an experimental output satisfying an objective includes conducting an experimental execution process including applying a selection criterion to select an approach to determining a set of parameters for a set of experiments, and determining a first set of parameters for a first experiment according to the selected approach based on one or more of (i) a predicted relationship between a set of parameters and a characteristic of a corresponding experimental output, (ii) the measured characteristic of a second experimental output from a second experiment executed according to a second set of parameters, (iii) the objective, and (iv) a parameter selection rule. Conducting an experimental execution process includes controlling execution of the first set of experiments according to the first set of parameters, where execution of each first experiment includes conducting the experiment according to the first set of parameters to produce a first experimental output; and measuring the characteristic of the first experimental output. The method includes determining whether the objective is satisfied by the experimental execution process, and, when the objective is not satisfied by the experimental execution process, conducting a subsequent experimental execution process.

Predictive multi-stage modelling for complex semiconductor device manufacturing process control is provided. In one aspect, a method of predictive multi-stage modelling for controlling a complex semiconductor device manufacturing process includes: collecting geometrical data from metrology measurements made at select stages of the manufacturing process; and making an outcome probability prediction at each of the select stages using a multiplicative kernel Gaussian process, wherein the outcome probability prediction is a function of a current stage and all prior stages. Machine-learning models can be trained for each of the select stages of the manufacturing process using the multiplicative kernel Gaussian process. The machine-learning models can be used to provide probabilistic predictions for a final outcome in real-time for production wafers. The probabilistic predictions can then be used to select production wafers for rework, sort, scrap or disposition.

The present disclosure belongs to the field of petroleum engineering, and specifically relates to a subsea Christmas tree re-prediction method integrating Kalman filter and Bayesian network. The subsea Christmas tree re-prediction method integrating Kalman filter and Bayesian network includes three steps: digital twin model establishment, degradation process re-prediction model establishment, and remaining useful life calculation model establishment. The subsea Christmas tree re-prediction system integrating Kalman filter and Bayesian network includes a subsea distribution unit information acquisition subsystem mounted on an subsea distribution unit, a subsea control module information acquisition subsystem mounted on a subsea control module, a subsea valve bank information acquisition subsystem mounted on a subsea valve bank, a wellhead mechanical module information acquisition subsystem mounted on a wellhead mechanical module, a subsea environmental information acquisition module mounted on a subsea control module, and a subsea Christmas tree digital twin subsystem mounted in an overwater control station.

A battery model construction method includes: a step ST2 for constructing a battery model; steps ST3 and ST4 for evaluating, for each sample battery, the prediction error between a measured value of the SOH and a predicted value according to the battery model, and determining whether there is inherent bias in the prediction error for each sample battery; steps ST5 and ST6 for constructing a first error prediction model associating explanatory variables defined on the basis of usage history parameters with an objective variable, and determining whether a first correlation exists between the measured value of the average prediction error acquired in steps ST3 and ST4 and the predicted value according to the first error prediction model; and a step ST7 for reconstructing the battery model in the case where it is determined that there is bias and that the first correlation exists in steps ST5 and ST6.

A system and method for simulating an electronic circuit is disclosed. The method includes creating a finite set of circuit or device parameter points selected from within an n-dimensional parameter space. The method includes determining, for each circuit or device parameter point of the set, a corresponding response value of the performance metric and a corresponding probability of occurrence. The method includes determining, for a predetermined value of the performance metric, the total probability of occurrence.

A method and a system are disclosed for solving a mixed-integer programming problem, the method comprising obtaining an indication of a mixed-integer programming optimization problem; until a performance criterion is met: providing the mixed-integer programming optimization problem to an optimization oracle adapted for solving the mixed-integer programming optimization problem using a feasibility pump technique and comprising an optimization solver, initializing parameters of an optimization oracle and an initial solution pair, the parameters comprising Monte-Carlo simulation parameters, a list of neighborhood functions and a measure of fractionality, and performing iterative calls to the optimization solver until a stopping condition is met; and providing at least one corresponding solution obtained from the optimization solver.

The present disclosure provides a nuclear detection simulation device based on a nanosecond light source and a nuclear signal inversion technology. Electronic circuits and nuclear pulse current signals are used to drive blue LEDs to emit nuclear pulse optical signals, so as to simulate a scintillator to receive γ radiation to emit light, and can simulate point sources and area sources, organic scintillator detectors and inorganic scintillators, scintillation efficiency and detection efficiency, radioactive sources, fast components and slow components, multi-type nuclear pulse signals, a statistical fluctuation phenomenon of nuclear pulses, the electron pair effect, the Compton effect, the photoelectric effect, and self-radiation of the scintillator, generate single or piled-up pulse signals, corresponding energy spectrum curves, and an environmental background spectral line. 3D visualization configuration and a nuclear signal detection process can be subjected to animated demonstration.

Some implementations relate to a method for parallelizing, by a geological data system, operations of a geostatistical simulation for a well data set via a plurality of processing elements (PEs). The method may include determining a reservoir area for the well data set. The method may include determining a set of turning band lines for the reservoir area. The method may include dividing the reservoir area into a plurality of tiles, each tile including a respective subset of the set of turning band lines. The method may include assigning at least one of the tiles to each of the PEs. The method may include determining, in parallel for each tile, intermediate results with respect to each respective subset of turning band lines. The method may include aggregating the intermediate results to form a final result of the geostatistical simulation.