Abstract: Techniques herein include a bladder-based dispense system using an elongate bladder configured to selectively expand and contract to assist with dispense actions. This dispense system compensates for filter-lag, which often accompanies fluid filtering for microfabrication. This dispense system also provides a high-purity and high precision dispense unit. A meniscus sensor monitors a position of a meniscus of process fluid at a nozzle. The elongate bladder unit is used to maintain a position of the meniscus at a particular location by selectively expanding or contracting the bladder, thereby moving or holding a meniscus position. Expansion of the elongate bladder is also used for a suck-back action after completing a dispense action.

Abstract: Techniques herein include a bladder-based dispense system using an elongate bladder configured to selectively expand and contract to assist with dispense actions. This dispense system compensates for filter-lag, which often accompanies fluid filtering for microfabrication. This dispense system also provides a high-purity and high precision dispense unit. A meniscus sensor monitors a position of a meniscus of process fluid at a nozzle. The elongate bladder unit is used to maintain a position of the meniscus at a particular location by selectively expanding or contracting the bladder, thereby moving or holding a meniscus position. Expansion of the elongate bladder is also used for a suck-back action after completing a dispense action.

Abstract: A method for patterning a substrate is described. The method includes forming a layer of radiation-sensitive material on a substrate, and preparing a pattern in the layer of radiation-sensitive material using a lithographic process, wherein the pattern is characterized by a critical dimension (CD) and a roughness. Following the preparation of the pattern in the layer of radiation-sensitive material, the method further includes performing a CD slimming process to reduce the CD to a reduced CD, and performing a vapor smoothing process to reduce the roughness to a reduced roughness.

Abstract: A method is provided for preparing a prepatterned substrate for use in DSA integration. In one example, the method includes removing a radiation-sensitive material pattern overlying a patterned cross-linked polystyrene copolymer layer by a) exposure to a solvent vapor, b) exposure to a liquid solvent, and c) repeating steps a)-b) until the radiation-sensitive material pattern is completely removed. In another example, the method includes removing a neutral layer by affecting removal of an underlying patterned radiation-sensitive material layer, which includes swelling the neutral layer; and removing the radiation-sensitive material pattern and the swollen neutral layer in portions by exposing the swollen layer and pattern to a developer solution. Swelling the neutral layer includes a) exposure to a solvent vapor; b) exposure to a liquid solvent; and c) repeating steps a)-b) until the neutral layer is sufficiently swollen to allow penetration of the developing solution through the swollen neutral layer.

Abstract: A method for patterning a substrate is described. The method includes forming a layer of radiation-sensitive material on a substrate, and preparing a pattern in the layer of radiation-sensitive material using a lithographic process, wherein the pattern is characterized by a critical dimension (CD) and a roughness. Following the preparation of the pattern in the layer of radiation-sensitive material, the method further includes performing a CD slimming process to reduce the CD to a reduced CD, and performing a vapor smoothing process to reduce the roughness to a reduced roughness.

Abstract: A method is provided for preparing a prepatterned substrate for use in DSA integration. In one example, the method includes removing a radiation-sensitive material pattern overlying a patterned cross-linked polystyrene copolymer layer by a) exposure to a solvent vapor, b) exposure to a liquid solvent, and c) repeating steps a)-b) until the radiation-sensitive material pattern is completely removed. In another example, the method includes removing a neutral layer by affecting removal of an underlying patterned radiation-sensitive material layer, which includes swelling the neutral layer; and removing the radiation-sensitive material pattern and the swollen neutral layer in portions by exposing the swollen layer and pattern to a developer solution. Swelling the neutral layer includes a) exposure to a solvent vapor; b) exposure to a liquid solvent; and c) repeating steps a)-b) until the neutral layer is sufficiently swollen to allow penetration of the developing solution through the swollen neutral layer.

Abstract: To train a machine learning system, a set of different values of one or more photoresist parameters, which characterize behavior of photoresist when the photoresist undergoes processing steps in a wafer application, is obtained. A set of diffraction signals is obtained using the set of different values of the one or more photoresist parameters. The machine learning system is trained using the set of measured diffraction signals as inputs to the machine learning system and the set of different values of the one or more photoresist parameters as expected outputs of the machine learning system.

Abstract: To generate a simulated diffraction signal, one or more values of one or more photoresist parameters, which characterize behavior of photoresist when the photoresist undergoes processing steps in a wafer application, are obtained. One or more values of one or more profile parameters are derived using the one or more values of the one or more photoresist parameters. The one or more profile parameters characterize one or more geometric features of the structure. A simulated diffraction signal is generated using the one or more values of the one or more profile parameters. The simulated diffraction signal characterizes behavior of light diffracted from the structure. The generated simulated diffraction signal is associated with the one or more values of the one or more photoresist parameters.

Abstract: To control a photolithography cluster using optical metrology, a structure is fabricated on a wafer using the photolithography cluster. A measured diffraction signal off the structure is obtained. The measured diffraction signal is compared to a simulated diffraction signal. The simulated diffraction signal is associated with one or more values of one or more photoresist parameters. The one or more photoresist parameters characterize behavior of photoresist when the photoresist undergoes processing steps in the photolithography cluster. The simulated diffraction signal was generated using one or more values of one or more profile parameters. The one or more values of the one or more profile parameters used to generate the simulated diffraction signal were derived from the one or more values of the one or more photoresist parameters associated with the simulated diffraction signal.

Abstract: To control a photolithography cluster using optical metrology, a structure is fabricated on a wafer using the photolithography cluster. A measured diffraction signal off the structure is obtained. The measured diffraction signal is compared to a simulated diffraction signal. The simulated diffraction signal is associated with one or more values of one or more photoresist parameters. The one or more photoresist parameters characterize behavior of photoresist when the photoresist undergoes processing steps in the photolithography cluster. The simulated diffraction signal was generated using one or more values of one or more profile parameters. The one or more values of the one or more profile parameters used to generate the simulated diffraction signal were derived from the one or more values of the one or more photoresist parameters associated with the simulated diffraction signal.

Abstract: To train a machine learning system, a set of different values of one or more photoresist parameters, which characterize behavior of photoresist when the photoresist undergoes processing steps in a wafer application, is obtained. A set of diffraction signals is obtained using the set of different values of the one or more photoresist parameters. The machine learning system is trained using the set of measured diffraction signals as inputs to the machine learning system and the set of different values of the one or more photoresist parameters as expected outputs of the machine learning system.

Abstract: To generate a simulated diffraction signal, one or more values of one or more photoresist parameters, which characterize behavior of photoresist when the photoresist undergoes processing steps in a wafer application, are obtained. One or more values of one or more profile parameters are derived using the one or more values, of the one or more photoresist parameters. The one or more profile parameters characterize one or more geometric features of the structure. A simulated diffraction signal is generated using the one or more values of the one or more profile parameters. The simulated diffraction signal characterizes behavior of light diffracted from the structure. The generated simulated diffraction signal is associated with the one or more values of the one or more photoresist parameters.

Abstract: A method, system, and computer program product for offline data collection using data entry forms that are generated and emailed to data entry systems, on which data may then be entered into the data entry forms while the data entry system is not communicatively connected. A data collection process, comprises the steps of building a data entry form for entering requested data, generating an email message including the data entry form, transmitting the email message, receiving an email message including the requested data, and posting the received requested data to a database.