The inventive concept provides a mask treating apparatus. The mask treating apparatus includes a support unit configured to support and rotate a mask, the mask having a first pattern within a plurality of cells thereof and a second pattern outside regions of the plurality of cells; a heating unit including a laser irradiation module and a moving module, the laser irradiation module having a laser irradiator for irradiating a laser light to the second pattern, the moving module configured to change a position of the laser irradiation module; and a controller configured to control the support unit and the heating unit, and wherein when a treating position is divided into four equal parts from a first quadrant to a fourth quadrant based on a center of the mask, the laser irradiator is positioned at the fourth quadrant and the first quadrant in a direction linearly moving from a standby position to the treating position, positioned at the third quadrant in a direction which is perpendicular to the fourth quadrant, and positioned at the second quadrant in a direction which is perpendicular to the first quadrant, and wherein the controller controls a rotation of the support unit so the second pattern is positioned at the fourth quadrant.

The inventive concept provides a mask treating apparatus. The mask treating apparatus includes a support unit configured to support and rotate a mask, the mask having a first pattern within a plurality of cells thereof and a second pattern outside regions of the plurality of cells; a heating unit including a laser irradiation module and a moving module, the laser irradiation module having a laser irradiator for irradiating a laser light to the second pattern, the moving module configured to change a position of the laser irradiation module; and a controller configured to control the support unit and the heating unit, and wherein when a treating position is divided into four equal parts from a first quadrant to a fourth quadrant based on a center of the mask, the laser irradiator is positioned at the fourth quadrant and the first quadrant in a direction linearly moving from a standby position to the treating position, positioned at the third quadrant in a direction which is perpendicular to the fourth quadrant, and positioned at the second quadrant in a direction which is perpendicular to the first quadrant, and wherein the controller controls a rotation of the support unit so the second pattern is positioned at the fourth quadrant.

Methods of generating a characteristic pattern for a patterning process and training a machine learning model. A method of training a machine learning model configured to generate a characteristic pattern for a mask pattern includes obtaining (i) a reference characteristic pattern that meets a satisfactory threshold related to manufacturing of the mask pattern, and (ii) a continuous transmission mask (CTM) for use in generating the mask pattern; and training, based on the reference characteristic pattern and the CTM, the machine learning model such that a first metric between the characteristic pattern and the CTM, and a second metric between the characteristic pattern and the reference characteristic pattern is reduced.

Methods, a non-transitory computer-readable storage medium, devices, and a system in relation to training a convolutional neural network for deriving corrected digital pattern descriptions from digital pattern descriptions for use in a process for producing photomasks are disclosed. A reinforcement learning agent is trained to derive corrected digital pattern descriptions from respective digital pattern descriptions. The training is based on a first plurality of generated digital pattern descriptions and an obtained physical model using which predicted binary patterns of photomasks can be derived that would result from inputting digital pattern descriptions to the process for producing photomasks. A second plurality of digital pattern descriptions is then generated, and corresponding corrected digital pattern descriptions are generated using the trained reinforcement learning agent, thereby generating training data. The training data can be used to train a convolutional neural network to derive corrected digital pattern descriptions from digital pattern descriptions, the trained neural network can be used to derive a corrected digital pattern description, and the corrected digital pattern description can be used to produce a photomask according to the corrected digital pattern description.

Methods of fabricating lithographic masks include performing mask process correction (MPC) on a mask tape out (MTO) design layout. Performing MPC may include identifying a plurality of unit cells (each being iterated in the MTO design layout and including a plurality of curve patterns), and performing model-based MPC on at least one of the plurality of unit cells. These methods may further include performing electron beam exposure based on the MTO design layout on which the MPC is performed. The performing model-based MPC on at least one of the plurality of unit cells may be based on at least one of an aspect ratio, sizes, curvatures of curved edges, density, and a duty of the plurality of curve patterns.

Methods of fabricating lithographic masks include performing mask process correction (MPC) on a mask tape out (MTO) design layout. Performing MPC may include identifying a plurality of unit cells (each being iterated in the MTO design layout and including a plurality of curve patterns), and performing model-based MPC on at least one of the plurality of unit cells. These methods may further include performing electron beam exposure based on the MTO design layout on which the MPC is performed. The performing model-based MPC on at least one of the plurality of unit cells may be based on at least one of an aspect ratio, sizes, curvatures of curved edges, density, and a duty of the plurality of curve patterns.

A method for process control in the manufacture of semiconductor devices including performing metrology on at least one Design of Experiment (DOE) semiconductor wafer included in a lot of semiconductor wafers, the lot forming part of a batch of semiconductor wafer lots, generating, based on the metrology, one or more correctables to a process used to manufacture the lot of semiconductor wafers and adjusting, based on the correctables, the process performed on at least one of; other semiconductor wafers included in the lot of semi-conductor wafers, and other lots of semiconductor wafers included in the batch.

A method for process control in the manufacture of semiconductor devices including performing metrology on at least one Design of Experiment (DOE) semiconductor wafer included in a lot of semiconductor wafers, the lot forming part of a batch of semiconductor wafer lots, generating, based on the metrology, one or more correctables to a process used to manufacture the lot of semiconductor wafers and adjusting, based on the correctables, the process performed on at least one of; other semiconductor wafers included in the lot of semi-conductor wafers, and other lots of semiconductor wafers included in the batch.

A method of generating a mask used in fabrication of a semiconductor device includes, in part, selecting a source candidate, generating a process simulation model that includes a stochastic variance band model in response to the selected source candidate, performing a first optical proximity correction (OPC) on the data associated with the mask in response to the process simulation model, assessing one or more lithographic evaluation metrics in response to the OPC mask data, computing a cost in response to the assessed one or more lithographic evaluation metrics, and determining whether the computed cost satisfies a threshold condition. In response to the determination that the computed cost does not satisfy the threshold condition, a different source candidate may be selected.

A method of generating a mask used in fabrication of a semiconductor device includes, in part, selecting a source candidate, generating a process simulation model that includes a stochastic variance band model in response to the selected source candidate, performing a first optical proximity correction (OPC) on the data associated with the mask in response to the process simulation model, assessing one or more lithographic evaluation metrics in response to the OPC mask data, computing a cost in response to the assessed one or more lithographic evaluation metrics, and determining whether the computed cost satisfies a threshold condition. In response to the determination that the computed cost does not satisfy the threshold condition, a different source candidate may be selected.