Abstract:
A method is proposed involving obtaining data regarding an expected focus offset during a patterning process due to topography of a region of a substrate surface. A modification of a transmission or reflection of a region of a patterning device associated with the region of the substrate surface is determined based on the data. Using the patterning device modified according the determined modification during the patterning process mitigates an impact of the substrate topography on a parameter of the patterning process.
Abstract:
A computer-implemented method of defect validation for a device manufacturing process, the method comprising: obtaining a first image of a pattern processed into an area on a substrate using the device manufacturing process under a first condition; obtaining a metrology image from the area; aligning the metrology image and the first image; and determining from the first image and the metrology image whether the area contains a defect, based on one or more classification criteria.
Abstract:
Disclosed herein is a computer-implemented method to improve a lithographic process of processing a portion of a design layout onto a substrate using a lithographic apparatus, the method including: adjusting a first processing parameter among processing parameters of the lithographic process to cause the processing to be more tolerant to perturbations of at least one of the processing parameters during processing; and adjusting a second processing parameter among processing parameters of the lithographic process to cause the processing to be more tolerant to perturbations of at least one of the processing parameters during processing.
Abstract:
Disclosed herein is a computer-implemented defect prediction method for a device manufacturing process involving processing one or more patterns onto a substrate, the method including: determining values of one or more processing parameters under which the one or more patterns are processed; and determining or predicting, using the values of the one or more processing parameters, existence, probability of existence, a characteristic, and/or a combination selected from the foregoing, of a defect resulting from production of the one or more patterns with the device manufacturing process.
Abstract:
Described herein are methods related to improving a simulation processes and solutions (e.g., retargeted patterns) associated with manufacturing of a chip. A method includes obtaining a plurality of dose-focus settings, and a reference distribution based on measured values of the characteristic of a printed pattern associated with each setting of the plurality of dose-focus settings. The method further includes, based on an adjustment model and the plurality of dose-focus settings, determining the probability density function (PDF) of the characteristic such that an error between the PDF and the reference distribution is reduced. The PDF can be a function of the adjustment model and variance associated with dose, the adjustment model being configured to change a proportion of non-linear dose sensitivity contribution to the PDF. A process window can be adjusted based on the determined PDF of the characteristic.
Abstract:
Systems and methods for predicting substrate geometry associated with a patterning process are described. Input information including geometry information and/or process information for a pattern are received; and, using a machine learning prediction model, multi-dimensional output substrate geometry is predicted. The multi-dimensional output information comprises pattern probability images. A stochastic edge placement error band and/or a stochastic failure rate may be predicted based on the pattern probability images. The input information comprises simulated aerial images, simulated resist images, target substrate dimensions, and/or data from a scanner associated with semiconductor device manufacturing. Different aerial images may correspond to different heights in resist layers associated with the patterning process, for example.
Abstract:
Described herein is a method for training a machine learning model configured to predict a substrate image corresponding to a printed pattern of a substrate as measured via a metrology tool. The method involves obtaining a training data set comprising (i) metrology data of the metrology tool used to measure the printed pattern of the substrate, and (ii) a mask pattern employed for imaging the printed pattern on the substrate; and training, based on the training data set, a machine learning model to predict the substrate image of the substrate as measured by the metrology tool such that a cost function is improved, wherein the cost function comprises a relationship between the predicted substrate image and the metrology data.
Abstract:
A method of topography determination, the method including: obtaining a first focus value derived from a computational lithography model modeling patterning of an unpatterned substrate or derived from measurements of a patterned layer on an unpatterned substrate; obtaining a second focus value derived from measurement of a substrate having a topography; and determining a value of the topography from the first and second focus values.
Abstract:
Disclosed herein are various methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the characteristics under that process condition, whether that hot spot is defective; obtaining characteristics of each of the process conditions; obtaining characteristics of each of the hot spots; and training a machine learning model using a training set comprising the characteristics of one of the process conditions, the characteristics of one of the hot spots, and whether that hot spot is defective under that process condition.
Abstract:
Disclosed herein is a computer-implemented defect prediction method for a device manufacturing process involving processing a pattern onto a substrate, the method comprising: identifying a processing window limiting pattern (PWLP) from the pattern; determining a processing parameter under which the PWLP is processed; and determining or predicting, using the processing parameter, existence, probability of existence, a characteristic, or a combination thereof, of a defect produced from the PWLP with the device manufacturing process.