Methods of determining, and using, a patterning process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.

In one embodiment, a substrate processing control apparatus includes a position information acquiring module configured to acquire information about a position of a first pattern that is formed in a substrate for a photomask to change an applied stress to the substrate or a transmittance of the substrate. The apparatus further includes a position determining module configured to determine, in accordance with the information about the position of the first pattern, a position of a second pattern to be formed in the substrate to change the applied stress to the substrate or the transmittance of the substrate.

An embodiment of the invention may include a semiconductor structure for ensuring semiconductor design integrity. The semiconductor structure may include an electrical circuit necessary for the operation of the semiconductor circuit and white space having no electrical circuit. The semiconductor structure may include an optical pattern used for validating the semiconductor circuit design formed in the white space of the electrical circuit. In an embodiment of the invention, the optical pattern may include one or more deposition layers. In an embodiment of the invention, the optical pattern may include covershapes. In an embodiment of the invention, the optical pattern may be physically isolated from the electrical circuit. The optical pattern may include a Moir pattern.

A photolithography process includes providing a first test layout including test patterns, and a first light source; forming an initial mask layout according to the first test layout; forming a mask layout including mask layout patterns through an optical proximity correction or a phase-shifting masking; forming exposed patterns by exposing the mask layout using the first light source; and determining a weak region from the first test layout. A first distance between adjacent test patterns in the weak region is unequal to a second distance between corresponding exposed patterns. The photolithography process further includes performing a re-layout on the weak region to increase the first distance, thereby providing an adjusted test layout; performing a light-source optimization to obtain an adjusted light source; and determining the adjusted test layout and the adjusted light source as a second test layout and a second light source, respectively when process window requirements are satisfied.

An exposure device, an exposure method and a photolithography method are provided. The exposure device includes an exposure light source and an optical-path assembly, the optical-path assembly is configured to guide light emitted by the exposure light source to an exposing position, the optical-path assembly includes a light valve array, the light emitted by the exposure light source is able to be guided to the light valve array and then guided to the exposing position after the light is transmitted or reflected by the light valve array, the light valve array includes a plurality of light valve units, and optical transmittance or reflectivity of each of the light valve units is adjustable.

A method and system for calculating probability of success or failure for a lithographic process due to stochastic variations of the lithographic process are disclosed. Lithography is a process that uses light to transfer a geometric pattern from a photomask, based on a layout design, to a resist on a substrate. The lithographic process is subject to random stochastic phenomena, such as photon shot noise and stochastic phenomena in the resist process and resist development, with the resulting stochastic randomness potentially becoming a major challenge. The stochastic phenomena are modeled using a stochastic model, such as a random field model, that models stochastic randomness the exposure and resist process. The stochastic model inputs light exposure and resist parameters and definitions of success of success or failure as to the lithographic process, and outputs a probability distribution function of deprotection concentration indicative of success or failure probability of the lithographic process. In turn, the probability distribution function may be used to modify one or both of the light exposure and resist parameters in order to reduce the effect of stochastic randomness on the lithographic process.

A model-based tuning method for tuning a first lithography system utilizing a reference lithography system, each of which has tunable parameters for controlling imaging performance. The method includes the steps of defining a test pattern and an imaging model; imaging the test pattern utilizing the reference lithography system and measuring the imaging results; imaging the test pattern utilizing the first lithography system and measuring the imaging results; calibrating the imaging model utilizing the imaging results corresponding to the reference lithography system, where the calibrated imaging model has a first set of parameter values; tuning the calibrated imaging model utilizing the imaging results corresponding to the first lithography system, where the tuned calibrated model has a second set of parameter values; and adjusting the parameters of the first lithography system based on a difference between the first set of parameter values and the second set of parameter values.

The present disclosure relates to an extreme ultraviolet lithography, EUVL, device comprising: a reticle comprising a lithographic pattern to be imaged on a target wafer; a light-transmissive pellicle membrane mounted in front of, and parallel to, the reticle, wherein the pellicle membrane scatters transmitted light along a scattering axis; and an extreme ultraviolet, EUV, illumination system configured to illuminate the reticle through the pellicle membrane, wherein an illumination distribution provided by the EUV illumination system is asymmetric as seen in a source-pupil plane of the EUV illumination system; wherein light reflected by the reticle and then transmitted through the pellicle membrane comprises a non-scattered fraction and a scattered fraction formed by light scattered by the pellicle membrane; the EUVL device further comprising: an imaging system having an acceptance cone configured to capture a portion of the light reflected by the reticle and then transmitted through the pellicle membrane.

A simulation apparatus has: a first processing part configured to obtain a value of a parameter in a first set relating to the forming of the pattern; a second processing part configured to obtain a value of a parameter in a second set that is at least partially same as the parameter in the first set and relating to the forming of the pattern; and an integration processing part configured to evaluate, based on the value of the parameter in the first set and the value of the parameter in the second set, a state of the pattern formed on the substrate and a forming condition when the pattern is formed, and to determine based on the result of the evaluation whether or not to make at least one of the first processing part and the second processing part recalculate the value of the parameter in the corresponding set.

A model-based tuning method for tuning a first lithography system utilizing a reference lithography system, each of which has tunable parameters for controlling imaging performance. The method includes the steps of defining a test pattern and an imaging model; imaging the test pattern utilizing the reference lithography system and measuring the imaging results; imaging the test pattern utilizing the first lithography system and measuring the imaging results; calibrating the imaging model utilizing the imaging results corresponding to the reference lithography system, where the calibrated imaging model has a first set of parameter values; tuning the calibrated imaging model utilizing the imaging results corresponding to the first lithography system, where the tuned calibrated model has a second set of parameter values; and adjusting the parameters of the first lithography system based on a difference between the first set of parameter values and the second set of parameter values.