A method for determining an etch profile is described. The method includes determining a masking layer profile. Loading information can be determined. The loading information indicates dependence of an etch rate for the masking layer profile on a quantity and pattern of material being etched. Flux information can be determined. The flux information indicates dependence of the etch rate on an intensity and a spread angle of radiation incident on the masking layer profile. Re-deposition information can be determined. The re-deposition information indicates dependence of the etch rate on an amount of material removed from the masking layer profile that is re-deposited back on the masking layer profile. An output etch profile for the layer of the wafer is determined based on the loading information, the flux information, and/or the re-deposition information.

A method for operating a magnetic actuator comprises: ascertaining a mathematical model of the actuator which describes a change in a motor constant of the actuator as a function of the electrical drive power supplied; driving the actuator with a first electrical drive power as a function of a predetermined target force; ascertaining the change in the motor constant of the actuator on account of driving the actuator with the first electrical drive power via the mathematical model; ascertaining a correction value for the first electrical drive power as a function of the ascertained change in the motor constant; and driving the actuator with a second electrical drive power as a function of the first electrical drive power and the ascertained correction value.

A system and methods for OCD metrology are provided including receiving reference parameters, receiving multiple sets of measured scatterometric data, and receiving an optical model designed to generate one or more sets of model scatterometric data according to a set of pattern parameters, and training a machine learning model by applying, during the training, target features including the reference parameters, and by applying input features including the sets of measured scatterometric data and the sets of model scatterometric data, such that the trained machine learning model estimates new wafer pattern parameters from subsequently sets of measured scatterometric data.

The present application discloses an OPC model simulation method. The method includes the following steps: step 1, establishing a precision judgment function which is formed by multiplying each square of the difference between a simulation point of an OPC model and an actual point on a wafer, by weight, and then superposing all the squares; step 2, performing random data sampling, comprising forming distributed computing nodes; randomly distributing data to each computing node, and meanwhile distributing a current state value of fitting parameter space composed of all fitting parameters to each computing node; computing a local precision judgment function of each computing node; step 3, performing parallel computing to obtain the gradient of each local precision judgment function, and computing a first derivative and a first order approximate value of the gradient of each local precision judgment function; step 4, performing gradient composition and iteration.

Disclosed is an operating method of an electronic device which includes receiving a design layout for manufacturing the semiconductor device, generating a first layout by performing machine learning-based process proximity correction (PPC), generating a second layout by performing optical proximity correction (OPC), and outputting the second layout for a semiconductor process. The generating of the first layout includes generating a first after cleaning inspection (ACI) layout by executing a machine learning-based process proximity correction module on the design layout, generating a second after cleaning inspection layout by adjusting the design layout based on a difference of the first after cleaning inspection layout and the design layout and executing the process proximity correction module on the adjusted layout, and outputting the adjusted layout as the first layout, when a difference between the second after cleaning inspection layout and the design layout is smaller than or equal to a threshold value.

The invention provides a method for thermo-mechanical control of a heat sensitive element (Ml) subject to a heat load, comprising: -providing a non-linear thermo-mechanical model of the heat sensitive element describing a dynamical relationship between characteristics of the heat load and deformation of the heat sensitive element; -calculating a control signal on the basis of an optimization calculation of the non-linear model, -providing an actuation signal to a heater (HE), wherein the actuation signal is at least partially based on the control signal, -heating the heat sensitive element by the heater on the basis of the actuation signal.

A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Computer-implemented methods of optimizing a process simulation model that predicts a result of a semiconductor device fabrication operation to process parameter values characterizing the semiconductor device fabrication operation are disclosed. The methods involve generating cost values using a computationally predicted result of the semiconductor device fabrication operation and a metrology result produced, at least in part, by performing the semiconductor device fabrication operation in a reaction chamber operating under a set of fixed process parameter values. The determination of the parameters of the process simulation model may employ pre-process profiles, via optimization of the resultant post-process profiles of the parameters against profile metrology results. Cost values for, e.g., optical scatterometry, scanning electron microscopy and transmission electron microscopy may be used to guide optimization.

An apparatus includes a decision module that is configured to: receive a performance metric relating to performance conditions of an optical system emitting a light beam; estimate, based on the performance metric and a predetermined learning model, an effectiveness of a proposed change to the optical system; and output a change command to the optical system if it is estimated that the proposed change to the optical system would be effective.

A layout geometry of a lithographic mask is received. The layout geometry is partitioned into feature images, for example as selected from a library. The library contains predefined feature images and their corresponding precalculated mask 3D (M3D) filters. The M3D filter for a feature image represents the electromagnetic scattering effect of that feature image for a given source illumination. The mask function contribution from each of the feature images is calculated by convolving the feature image with its corresponding M3D filter. The mask function contributions are combined to determine a mask function for the lithographic mask illuminated by the source illumination.