Method and apparatus for controlling a photolithography process

Abstract

A method for determining control parameters for a photolithography process is disclosed, the method comprising: defining a substrate model for representing process parameter feature identifications at various locations on a substrate, the substrate model being defined as a combination of basis functions, the basis functions comprising at least one basis function suitable for representing variations in process parameter feature identifications between multiple substrates and / or between multiple batches of substrates; receiving measurement results of process parameters at various locations on at least one substrate; calculating substrate model parameters using the measurement results and the basis functions; and determining control parameters based on similarities between the substrate model parameters and at least one basis function and variations in process parameter feature identifications between multiple substrates and / or between multiple batches of substrates.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to European applications 19154087.1 filed on January 19, 2019, 19189258.7 filed on July 31, 2019, and 19192740.9 filed on August 21, 2019, the entire contents of which are incorporated herein by reference. Background Art Technical Field

[0003] The present invention relates to a method and a device for controlling a photolithography process, and to a computer program product for implementing the method and the device.

[0004] Related technologies

[0005] The photolithography process is a process in which a lithographic apparatus applies a desired pattern to a substrate (usually to a target portion of the substrate), after which various chemical processing steps and / or physical processing steps act via the pattern to produce the functional features of a complex product. Accurate placement of the pattern on the substrate is a major challenge for reducing the size of circuit components and other products that can be produced by photolithography. Specifically, the challenge of accurately measuring features that have been put down on the substrate is a key step in being able to accurately locate successive layers of features in a superimposed state to produce working devices with high yield. Typically, so-called overlay should be achieved within tens of nanometers in today's submicron semiconductor devices, up to a few nanometers in the most critical layers.

[0006] Therefore, modern lithographic equipment involves extensive measurement or "mapping" operations prior to the step of actually exposing or otherwise patterning the substrate at the target location. In the following discussion, the substrate is referred to as a "wafer" for convenience, without implying any limitation on the type of substrate that can be processed using the present invention. Advanced substrate models (e.g., alignment models) have been and continue to be developed as models and more accurately correct for nonlinear distortions of the wafer grid caused by processing steps and / or the lithographic equipment itself. The term "wafer grid" is used to refer to the coordinate system formed by the (measured) alignment marks at the wafer. For example, the wafer grid is formed by alignment marks in the scribe lines of the wafer, which ideally form an orthogonal grid.

[0007] Alignment, overlay or focus model parameters are calculated so that the substrate model fits the measurement results of the structure on the substrate. Overlay, focus and alignment errors on production wafers that vary depending on the position on the wafer can be described with the help of these substrate models. These substrate models are used in automatic process control (APC) systems to control lithography processes on a batch-to-batch (feed-forward) basis (a batch is a batch of one or more substrates or wafers). However, the measurement data used to establish the substrate model often contain spatial content that exhibits batch-to-batch fluctuations. Therefore, it is often observed that the substrate model parameters based on the measurement data also exhibit batch-to-batch variations. Then, the use of substrate model parameters in controlling the lithography process may lead to instability in the lithography process, because the correction of the lithography process is subsequently determined based on outdated substrate model parameter data (e.g., based on measurement results associated with a previous batch, which is not representative of a subsequent batch, such as a subsequent batch is a batch to be corrected). Therefore, there is a need for a method for determining substrate model parameters that are configured to control the lithography process in a robust and stable manner. Summary of the invention

[0008] The inventors have recognised that control of a lithographic process may be improved without unduly increasing computational or measurement overhead by reducing the impact of batch-to-batch or substrate-to-substrate variations that are included in the measurement data used to determine substrate model parameters.

[0009] The present invention provides a method for determining control parameters for a photolithography process, the method comprising: defining a substrate model for representing process parameter feature identifiers at various locations on a substrate, the substrate model being defined as a combination of basis functions, the basis functions comprising at least one basis function suitable for representing variations in process parameter feature identifiers between multiple substrates and / or between multiple batches of substrates; receiving measurement results of process parameters at various locations on at least one substrate; calculating substrate model parameters using the measurement results and the basis functions; and determining control parameters based on the substrate model parameters and similarity between the at least one basis function and variations in process parameter feature identifiers between multiple substrates and / or between multiple batches of substrates.

[0010] In another aspect of the present invention, a method for modeling measurement data associated with the spatial distribution of values ​​of a process parameter at various locations on a substrate undergoing a process is provided, the method comprising: obtaining the measurement data; transforming the measurement data to the frequency domain using a Fourier transform operation; filtering the transformed measurement data using a spatial filter, the spatial filter being configured to at least partially remove spatial frequency components associated with known noise characteristics and / or limitations of the process's control facilities; obtaining the filtered measurement data using an inverse Fourier transform operation, thereby transforming the filtered measurement data in the frequency domain to the spatial domain; and modeling the filtered measurement data to provide input to the process's control facilities.

[0011] Other features and advantages of the present invention and the structure and operation of various embodiments of the present invention are described in detail below with reference to the accompanying drawings. It should be noted that the present invention is not limited to the specific embodiments described herein. Such embodiments are presented herein only for illustrative purposes. Based on the teachings contained herein, additional embodiments will be apparent to those skilled in the relevant art. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Embodiments of the invention will now be described, by way of example only, with reference to the accompanying schematic drawings, in which:

[0013] Figure 1 depicts a lithographic apparatus according to an embodiment of the invention;

[0014] Figure 2 Schematically shows Figure 1 use of lithographic equipment and other equipment forming a production facility for semiconductor devices;

[0015] Figure 3 Schematically illustrates Figure 1 Measurement and exposure processes in equipment;

[0016] Figure 4 A to Figure 4 C illustrates alignment information used to correct for wafer grid distortion;

[0017] Figure 5 illustrates examples of alignment errors and residuals for two exemplary multi-wafer batches, wherein arrows indicate the direction of the alignment errors;

[0018] Figure 6 is a flow chart illustrating method steps according to an embodiment of the present invention;

[0019] Fig. 7A is a flow chart illustrating the application of an established method for applying feature identification capture to control a photolithographic substrate process, and Figure 7Bis a flow chart illustrating a corresponding method according to an embodiment of the present invention;

[0020] Figure 8 is a flow chart illustrating method steps applied in a photolithography processing method according to the present invention; and

[0021] Fig. 9 Data processing hardware of an apparatus that can be programmed to implement embodiments of the invention is schematically illustrated. DETAILED DESCRIPTION

[0022] Figure 1 A lithographic apparatus LA is schematically depicted. The apparatus comprises:

[0023] - an illumination system (illuminator) IL configured to condition a radiation beam B (e.g. UV radiation or EUV radiation);

[0024] a support structure (e.g. mask table) MT constructed to support a patterning device (e.g. mask) MA and connected to a first positioner PM configured to accurately position the patterning device according to certain parameters;

[0025] a substrate table (e.g. wafer stage) WTa or WTb configured to hold a substrate (e.g. resist-coated wafer) W and connected to a second positioner PW configured to accurately position the substrate according to certain parameters; and

[0026] - a projection system (eg, a refractive projection lens system) PS configured to project the pattern imparted to the radiation beam B by the patterning device MA onto a target portion C of the substrate W (eg, comprising one or more dies).

[0027] The illumination system may include various types of optical components for directing, shaping, or controlling radiation, such as refractive, reflective, magnetic, electromagnetic, electrostatic, or other types of optical components, or any combination thereof.

[0028] The support structure supports (i.e., carries) the weight of the pattern forming device. The support structure holds the pattern forming device in a manner that depends on the orientation of the pattern forming device, the design of the lithographic apparatus, and other conditions (such as, for example, whether the pattern forming device is held in a vacuum environment). The support structure can use mechanical, vacuum, electrostatic or other clamping techniques to hold the pattern forming device. The support structure can be, for example, a frame or table that can be fixed or movable as required. The support structure can ensure that the pattern forming device is in a desired position, for example relative to a projection system. Any use of the term "reticle" or "mask" herein can be considered to be synonymous with the more general term "pattern forming device".

[0029] The term "patterning device" as used herein should be broadly interpreted as referring to any device that can be used to impart a radiation beam with a pattern in its cross-section so as to produce a pattern in a target portion of the substrate. It should be noted that, for example, if the pattern imparted to the radiation beam includes phase-shifting features or so-called assist features, the pattern may not exactly correspond to the desired pattern in the target portion of the substrate. Typically, the pattern imparted to the radiation beam will correspond to a specific functional layer in a device (such as an integrated circuit) produced in the target portion.

[0030] The patterning device may be transmissive or reflective. Examples of patterning devices include masks, programmable mirror arrays, and programmable LCD panels. Masks are well known in lithography and include mask types such as binary, alternating phase-shift, and attenuated phase-shift, as well as various hybrid mask types. An example of a programmable mirror array employs a matrix arrangement of smaller mirrors, each of which can be individually tilted to reflect an incident radiation beam in different directions. The tilted mirrors impart a pattern in the radiation beam reflected by the matrix of mirrors.

[0031] The term "projection system" as used herein should be interpreted broadly to cover any type of projection system suitable for the exposure radiation used or for other factors such as the use of an immersion liquid or the use of a vacuum, including refractive, reflective, catadioptric, magnetic, electromagnetic and electrostatic optical systems, or any combination thereof. Any use of the term "projection lens" herein may be considered synonymous with the more general term "projection system".

[0032] As depicted here, the device is of the transmissive type (eg, employing a transmissive mask). Alternatively, the device may be of the reflective type (eg, employing a programmable mirror array of the type mentioned above, or employing a reflective mask).

[0033] The lithographic apparatus may be of a type having two (dual stage) or more substrate tables (and / or two or more mask tables). In such a "multi-stage" machine, the extra tables may be used in parallel, or preparatory steps may be carried out on one or more tables while one or more other tables are being used for exposure.

[0034] The lithographic apparatus may also be of a type in which at least a portion of the substrate may be covered by a liquid having a relatively high refractive index, such as water, so as to fill the space between the projection system and the substrate. Immersion liquid may also be applied to other spaces in the lithographic apparatus, such as the space between the mask and the projection system. Immersion techniques are well known in the art for increasing the numerical aperture of the projection system. The term "immersion" as used herein does not mean that structures such as the substrate must be immersed in the liquid, but only means that the liquid is located between the projection system and the substrate during exposure.

[0035] The illuminator IL receives a radiation beam from a radiation source SO. For example, when the source is an excimer laser, the source and the lithographic apparatus may be separate entities. In this case, the source is not considered to form part of the lithographic apparatus, and the radiation beam is transferred from the source SO to the illuminator IL by means of a beam delivery system BD including, for example, suitable guide mirrors and / or a beam expander. In other cases, for example, when the source is a mercury lamp, the source may be an integral part of the lithographic apparatus. The source SO and the illuminator IL together with the beam delivery system BD (if necessary) may be referred to as a radiation system.

[0036] The illuminator IL may include an adjuster AD for adjusting the angular intensity distribution of the radiation beam. Typically, at least the outer radial extent and / or the inner radial extent (commonly referred to as σ outer and σ inner, respectively) of the intensity distribution in a pupil plane of the illuminator may be adjusted. Furthermore, the illuminator IL may include various other components, such as an integrator IN and a condenser CO. The illuminator may be used to condition the radiation beam to have a desired uniformity and intensity distribution in its cross-section.

[0037] The radiation beam B is incident on a patterning device (e.g. mask MA) held on a support structure (e.g. mask table MT) and is patterned by the patterning device. Having passed through the mask MA, the radiation beam B passes through a projection system PS which focuses the beam onto a target portion C of the substrate W. With the aid of a second positioner PW and a position sensor IF (e.g. an interferometry device, a linear encoder or a capacitive sensor), the substrate table WTa / WTb can be accurately moved, for example in order to position a different target portion C in the path of the radiation beam B. Similarly, a first positioner PM and a further position sensor (the further position sensor is not in the Figure 1The first positioner PM (as explicitly depicted in the figure) can be used to accurately position the mask MA relative to the path of the radiation beam B, for example after mechanical retrieval from a mask library or during scanning. Typically, movement of the mask table MT can be achieved with the help of a long-stroke module (coarse positioning) and a short-stroke module (fine positioning) forming part of the first positioner PM. Similarly, movement of the substrate table WTa / WTb can be achieved using a long-stroke module and a short-stroke module forming part of the second positioner PW. In the case of a stepper (as opposed to a scanner), the mask table MT may be connected to the short-stroke actuator only, or may be fixed. Mask MA and substrate W can be aligned using mask alignment marks M1, M2 and substrate alignment marks P1, P2. Although the substrate alignment marks illustrated occupy dedicated target portions, the marks may be located in the space between target portions (these marks are referred to as scribe alignment marks). Similarly, in the case where more than one die is arranged on the mask MA, the mask alignment marks may be located between the dies.

[0038] The depicted device can be used in at least one of the following modes:

[0039] 1. In step mode, the mask table MT and the substrate table WTa / WTb are held substantially stationary while an entire pattern imparted to the radiation beam is projected at once onto a target portion C (i.e. a single static exposure). The substrate table WTa / WTb is then shifted in the X-direction and / or Y-direction so that a different target portion C can be exposed. In step mode, the maximum size of the exposure field limits the size of the target portion C imaged in a single static exposure.

[0040] 2. In scan mode, the mask table MT and the substrate table WTa / WTb are scanned synchronously while projecting a pattern imparted to the radiation beam onto a target portion C (i.e. a single dynamic exposure). The speed and direction of the substrate table WTa / WTb relative to the mask table MT may be determined by the (or less) magnification and image reversal characteristics of the projection system PS. In scan mode, the maximum size of the exposure field limits the width of the target portion in a single dynamic exposure (in the non-scanning direction), while the length of the scanning movement determines the height of the target portion (in the scanning direction).

[0041] 3. In another mode, the mask table MT is held substantially stationary, thereby holding the programmable patterning device, and the substrate table WTa / WTb is moved or scanned, while a pattern imparted to the radiation beam is projected onto a target portion C. In this mode, a pulsed radiation source is typically employed, and the programmable patterning device is updated as required after each movement of the substrate table WTa / WTb, or between successive radiation pulses during a scan. This mode of operation can be readily applied to maskless lithography using a programmable patterning device, such as a programmable mirror array of the type mentioned above.

[0042] Combinations and / or variations on the above-described modes of use or entirely different modes of use may also be employed.

[0043] The lithographic apparatus LA in this example is of the so-called dual-platform type, which has two substrate tables WTa and WTb and two stations - an exposure station and a measurement station - between which the substrate table can be exchanged. When a substrate located on one substrate table is exposed at the exposure station EXP, another substrate can be loaded onto the other substrate table at the measurement station MEA, so that various preparatory steps can be performed. The preparatory steps may include using a horizontal sensor LS to map the surface height of the substrate, and using an alignment sensor AS to measure the position of the alignment mark on the substrate. The alignment marks are nominally or nominally arranged in a regular grid pattern. However, due to inaccuracies in the generation of the marks and due to deformations of the substrate during its entire processing, the marks deviate from the ideal grid. Therefore, in the case where the device LA will print the product features in the correct position with extremely high accuracy, in addition to measuring the position and orientation of the substrate, the alignment sensor must actually measure in detail the positions of many marks in various areas on the substrate. Therefore, the measurement of the alignment marks is very time-consuming, and providing two substrate tables enables a significant increase in the throughput of the device. If the position sensor IF is not able to measure the position of the substrate table when it is at the measurement station and at the exposure station, a second position sensor may be provided to enable the position of the substrate table to be tracked at both stations.

[0044] The device also includes a lithographic device control unit LACU, which controls all movements and measurements of the various actuators and sensors described. LACU also includes signal processing and data processing capabilities for implementing the desired calculations related to the operation of the device. In fact, the control unit LACU will be implemented as a system of many subunits, which each handle real-time data acquisition, processing and control of subsystems or components in the device. For example, a processing subsystem can be dedicated to the servo control of a substrate positioner PW. Separated units can even handle coarse actuators and fine actuators or different axes. Another unit can be dedicated to the readout of a position sensor IF. The overall control of the device can be controlled by a central processing unit, which communicates with these subsystem processing units, communicates with an operator, and communicates with other devices involved in the lithographic manufacturing process.

[0045] Figure 2 At 200 is shown a lithographic apparatus LA in the context of an industrial production facility for semiconductor products. Within the lithographic apparatus (or simply "lithography tool" 200), at 202 a measurement station MEA is shown and at 204 an exposure station EXP is shown. At 206 a control unit LACU is shown. Within the production facility, the apparatus 200 forms part of a "lithography cell" or "lithography cluster" which also includes a coating apparatus 208 for applying photosensitive resist and other coatings to a substrate W for patterning by the apparatus 200. At the output side of the apparatus 200, a baking apparatus 210 and a developing apparatus 212 are provided for developing the exposed pattern into a solid resist pattern.

[0046] Once the pattern has been applied and developed, the patterned substrate 220 is transferred to other processing equipment such as those described at 222, 224, and 226. A wide range of processing steps are implemented by various equipment in a typical manufacturing facility. For the purpose of example, the equipment 222 in this embodiment is an etching station, and the equipment 224 performs a post-etching annealing step. Other physical and / or chemical processing steps are applied to other equipment 226, etc. Many types of operations may be required to make actual devices, such as deposition of materials, modification of surface material properties (oxidation, doping, ion implantation, etc.), chemical mechanical polishing (CMP), etc. In fact, the equipment 226 can represent a series of different processing steps performed in one or more equipment.

[0047] As is well known, the manufacture of semiconductor devices involves multiple repetitions of such processing to build device structures layer by layer on a substrate using appropriate materials and patterns. Thus, the substrate 230 arriving at the lithography cluster may be a newly prepared substrate, or the substrate 230 arriving at the lithography cluster may be a substrate that has been completely processed previously in this cluster or in another device. Similarly, depending on the desired processing, the substrate 232 when leaving the device 226 may be returned for subsequent patterning operations in the same lithography cluster, the substrate 232 may be designated for patterning operations in a different cluster, or the substrate 232 may be a finished product to be sent for cutting and packaging.

[0048] Each layer of the product structure requires a different set of process steps, and the equipment 226 used at each layer may be completely different in type. In addition, even if the processing steps to be applied by the equipment 226 are nominally or nominally the same in a large facility, there may be several assumed identical machines working in parallel to perform steps 226 on different substrates. Small differences in settings or failures between these machines may mean that the small differences affect different substrates in different ways. Even steps that are relatively common to each layer, such as etching (equipment 222), can be implemented by several etching equipment that are nominally or nominally the same but work in parallel to maximize throughput. In addition, in practice, different layers require different etching processes, such as chemical etching, plasma etching, depending on the details of the material to be etched, and require special requirements, such as, for example, anisotropic etching.

[0049] Previous and / or subsequent processes may be performed in other lithographic equipment as just mentioned and even in different types of lithographic equipment. For example, some layers in the device manufacturing process that are extremely demanding in parameters such as resolution and overlay may be performed in more advanced lithographic tools than other layers that are less demanding. Thus, some layers may be exposed in immersion lithographic tools, while other layers are exposed in "dry" tools. Some layers may be exposed in tools operating at DUV wavelengths, while other layers are exposed using EUV wavelength radiation.

[0050] Figure 2Also shown is a metrology device 240, which is provided for parameter measurement of products at a desired platform in the manufacturing process. A common example of a measurement station in a modern lithography production facility is a scatterometer (e.g., an angle-resolved scatterometer or a spectral scatterometer), and a common example of a measurement station in a modern lithography production facility can be applied to measure the properties of the developed substrate 220 before etching in the device 222. With the use of the metrology device 240, it can be determined that important performance parameters such as overlay or critical dimension (CD) do not meet specified accuracy requirements in the developed resist. Prior to the etching step, there is an opportunity to strip the developed resist and reprocess the substrate 220 via the lithography cluster. As is known, the measurement results 242 from the device 240 can be used to maintain accurate execution of patterning operations in the lithography cluster by making small adjustments over time by the control unit LACU 206, thereby minimizing the risk of product being out of specification and requiring rework. Of course, the metrology equipment 240 and / or other metrology equipment (not shown) may be applied to measure properties of the processed substrates 232 , 234 as well as the incident substrate 230 .

[0051] Figure 3 A method for exposing a target portion (e.g., a die) to Figure 1 Step on substrate W in a dual-platform device.

[0052] On the left within the dashed box are steps performed at the measurement station MEA, while the right side shows steps performed at the exposure station EXP. Sometimes, one of the substrate tables WTa, WTb will be located at the exposure station, while the other of the substrate tables WTa, WTb is located at the measurement station, as described above. For the purpose of this description, it is assumed that the substrate W has already been loaded into the exposure station. At step 300, a new substrate W' is loaded onto the apparatus by a mechanism not shown. These two substrates are processed in parallel in order to increase the throughput of the lithographic apparatus.

[0053] Reference is first made to a newly loaded substrate W', which may be a previously unprocessed substrate, which is prepared using a new photoresist for the first exposure in the apparatus. However, typically, the lithographic process described will be only one step in a series of exposure and processing steps, so that the substrate W' has passed through this apparatus and / or other lithographic apparatus several times and may also undergo subsequent processes. With particular regard to the problem of improving overlay performance, the task is to ensure that the new pattern is accurately applied to the correct position on the substrate that has been subjected to one or more cycles of patterning and processing. These processing steps gradually introduce distortions in the substrate, which must be measured and corrected to achieve satisfactory overlay performance.

[0054] At 302, alignment measurements using substrate marks P1 etc. and sensors (not shown) are used to measure and record the alignment of the substrate relative to the substrate table WTa / WTb. In addition, an alignment sensor AS will be used to measure a number of alignment marks at various locations W' on the substrate. These measurements are used in one embodiment to establish a so-called wafer grid that very accurately maps the spatial distribution of the alignment marks at various locations on the substrate, including any distortion relative to a nominal rectangular grid. In other words, the measurements record the positional deviation of a point on the substrate relative to its ideal location.

[0055] At step 304, a level sensor LS is also used to measure the wafer height (Z) map relative to the XY position. The height map is used to achieve accurate focus of the pattern being exposed. In addition, the measurement results record the position deviation of a point on the substrate relative to an ideal (flat) substrate in the Z direction.

[0056] When the substrate W' is loaded, the recipe data 306 is received, which defines the exposure to be performed and also defines the properties of the wafer and the previously generated pattern and the pattern to be generated on the substrate W'. The recipe data 306 may also include high-order alignment model parameters obtained from the aforementioned metrology measurements. The measurements of the wafer position, wafer grid and height map obtained at 302 and 304 are added to these recipe data so that the complete set of recipe data and measurement data 308 can be delivered to the exposure station EXP. The measurements of the alignment data include, for example, the X position and Y position of the alignment target formed in a fixed or nominally fixed relationship with the product pattern as the product of the lithography process. These alignment data collected just before the exposure are combined and interpolated to provide the parameters of the alignment model. These parameters and the alignment model will be used during the exposure operation to correct the position of the pattern applied in the current lithography step. Conventional alignment models may include 4, 5 or 6 parameters, which together define the translation, rotation and scaling of the "ideal" grid at different sizes. As further described in US 2013230797A1, advanced models using more parameters are known.

[0057] In this respect, the present description refers primarily to so-called "inter-field" substrate models, which describe positional deviations as characteristics of sites everywhere on the substrate. In actual processes, "intra-field" variations as characteristics of sites within each field (target portion C) are typically modeled. To determine the final position of the applied pattern, the inter-field model as well as the intra-field model can be combined in a known manner.

[0058] At 310, wafer W' is exchanged with wafer W so that the substrate W' to be measured plays the role of the substrate W to be exposed as previously discussed, and wafer W' enters the exposure station EXP. Figure 1 In an exemplary device of , this exchange is performed by exchanging supports WTa and WTb within the device so that the substrates W, W' remain accurately clamped and positioned on those supports to maintain the relative alignment between the substrate stage and the substrate itself. The wafer W that has actually been exposed is removed and the relevant support will receive a new substrate (not shown) for being subjected to measurement. Therefore, once the stage has been exchanged, in order to utilize the measurement information 302, 304 for the substrate W (formerly W') to control the exposure step, it is necessary to determine the relative position between the projection system PS and the substrate stage WTb (formerly WTa). At step 312, mask alignment is performed using mask alignment marks M1, M2. In steps 314, 316, 318, scanning actions and radiation pulses are applied to successive target locations of W at various locations on the substrate in order to complete the exposure of multiple patterns.

[0059] By using the alignment data obtained at the measurement station and the height map in performing the exposure step, these patterns are accurately aligned relative to the desired locations, and in particular, accurately aligned relative to features previously placed on the same substrate. At step 320, the exposed substrate, now labeled W", is removed from the equipment to undergo etching or other processes according to the exposed patterns.

[0060] Figure 4 A to Figure 4 C illustrates a form of alignment information that can be used to correct for wafer grid distortion, as measured by alignment sensor AL, and with respect to alignment marks (targets) 400 in a previous layer on wafer (substrate) W. Figure 4 As shown in A, each target has a nominal position, generally defined by axes X and Y relative to a regular rectangular grid 402. Figure 4 B, the measurement of the actual position 404 of each target shows deviations from the nominal grid.Alignment marks may be provided within device regions of the substrate, and / or alignment marks may be provided in so-called "scribe" regions between device regions.

[0061] Reference Figure 4 C, the measured positions 404 of all targets can be numerically processed to set a model of a distorted wafer grid 406 for this particular wafer. This alignment model is used in patterning operations to control the position of the pattern applied to the substrate. In the illustrated example, the straight lines of the nominal grid have become curved. For this case, a high-order (advanced) alignment model can be used instead of the linear alignment model. It goes without saying that the illustrated distortion is exaggerated compared to the actual situation.

[0062] Even when advanced alignment models are used, errors inevitably remain in the overlay performance of a lithographic apparatus. A single lithographic apparatus may also perform differently than other lithographic apparatuses processing the same substrate. In order to correctly and consistently expose a substrate exposed by a lithographic apparatus, the exposed substrate needs to be inspected to measure performance parameters such as overlay error between subsequent layers, line thickness, critical dimension (CD), focus, etc.

[0063] The detection device is therefore used to determine the properties of the substrate independently of the alignment sensor AS, and in particular to determine how the properties of different substrates or different layers of the same substrate vary from layer to layer. Figure 3 Not shown, but in Figure 2 The scatterometer (shown at 240 in FIG. 1 ) may be integrated into the lithographic apparatus LA or the lithographic cell LC or may be a separate device. It may be a scatterometer, such as the then-current angle-resolved scatterometer described in published US patent application US 2006033921A1.

[0064] The detection equipment can also be used in advanced process control (APC) systems to calibrate individual lithography equipment and allow different tools to be used interchangeably to a greater extent. Recently, improvements in the uniformity of focusing and overlay (interlayer alignment) of the equipment have been achieved by implementing a stability module, resulting in an optimized process window for a given feature size and chip application, thereby enabling the continued production of smaller and more advanced chips. In one embodiment, the stability module automatically resets the system to a predefined baseline at regular time intervals (e.g., daily). More details of lithography and measurement methods including stability modules can be found in US 2012008127A1. Known examples implement three main process control loops. The first loop uses a stability module and a monitoring wafer to provide local control of the lithography equipment. The second (APC) loop is used for local scanner control of the product (determining the focus, dose, and overlay of the product wafer).

[0065] The third control loop allows integration of metrology into the second (APC) loop (e.g., for double patterning). Figure 3 In addition to the measurements taken during the actual patterning operation, all of these loops use Figure 2 Measurements performed by the detection device 240 in.

[0066] As mentioned above, the standard alignment model can have six parameters (actually, three parameters in each X direction and Y direction) and there are more advanced alignment models in addition. On the other hand, for the most demanding processes currently in use and under development, a more detailed correction of the wafer grid is required to achieve the desired overlay performance. Although the standard model may use less than ten parameters, the advanced alignment model usually uses more than fifteen parameters, or more than thirty parameters. Examples of advanced models are high-order wafer alignment (HOWA) models, alignment models based on zone alignment (ZA) and radial basis functions (RBF). HOWA is a disclosed technology based on second-order, third-order and higher-order polynomial functions. For example, regional alignment is described in "Overlay improvement by zone alignment strategy" (Proc. SPIE 6922, 69221G (2008)) by Huang et al. RBF modeling is described in the published U.S. patent application 2012 / 0218533. Advanced models produce complex descriptions of the wafer grid that is corrected during exposure of the target layer. The latest versions of HOWA and RBF provide particularly complex descriptions based on dozens of parameters. This implies that many measurements are required in order to obtain a wafer grid with sufficient detail.

[0067] The alignment and overlay (substrate) models can model positional deviations. The models are also applicable to other lithography patterning characteristics (such as but not limited to critical dimension (CD), focus, and side wall angle (SWA)) because substrate edge-related disturbances of these characteristics have similar root causes. Therefore, the present disclosure refers to substrate models, which include alignment models, CD models, focus models, and SWA models. The substrate model is used to model disturbances introduced in the lithography process and can represent the "signature" or pattern of disturbances for a specific lithography process or process step at various locations on the substrate. These disturbances include but are not limited to alignment (positional deviations in the plane of the substrate), height deviations (perpendicular to the plane), CD, focus, and SWA.

[0068] In the proposed extended model, inter-field overlay can be described in terms of its radial and tangential (R&T) components as seen from the center of the wafer, rather than in terms of the traditional Cartesian (X&Y) orientation in which the measurements are communicated.

[0069] Semiconductor wafers are generally circular, but semiconductor wafers may have smaller flat sections or notches for alignment (e.g., Figure 4). If R is the distance from the center to the edge of the wafer and r is the distance from the point to the center of the wafer, then let t = Rr be the distance from the edge of the wafer for any given exposure field. More generally, the principles of the present invention can be applied to processing rectangular substrates, or other non-circular substrates. Depending on the substrate shape and the processing effects to be modeled, different coordinate systems can be used to express the distance from the edge of the substrate.

[0070] According to the principles of the present invention, one or more specific edge-related basis functions are added to an inter-field substrate model designed to better describe edge effects. Thus, substrate model parameters are calculated using a combination of basis functions including at least one edge basis function associated with a substrate edge.

[0071] Let u(t) be the edge basis function (usually with units of nanometers). Various functions can be considered suitable as edge basis functions associated with the substrate edge. An exponential decay function can be defined as u(t) = C·2 -t / λ , where λ is the half-life decay distance or decay range parameter and C is the amplitude at the edge.

[0072] Another example is the rational function u(t) = C1 / t + C2 / t 2 , where C1 and C2 are shape constants.

[0073] As in the second example above, when rational functions are used as basis functions, caution should be exercised to avoid "division by zero" errors. Using the simple formula above, u(t) will be infinite as "t" approaches zero. Therefore, in a practical implementation, some modifications of the formula are used to avoid excessive values ​​and to avoid situations where calculation errors occur. In one such implementation, we use the form of the term C / (t+δ) instead of C / t, where δ is a smaller deviation that effectively avoids division by zero at the edge of the wafer. (Alternatively and equivalently, the technician can calculate t with reference to a radius R that is slightly larger than the true radius of the substrate.) Alternatively, the technician can apply a rule whereby the rational function is only used for values ​​of "t" that are larger than the minimum value δ that is greater than zero. A variety of measurements can be envisioned.

[0074] In another example, δ can be used as one of the variable parameters of the edge effect model. For example, a function u(t)=C1 / (t+δ) can be envisioned, where C1 and δ play the role of shape constants.

[0075] It should be noted that these exemplary edge basis functions are basis functions with one or more weights expressed in terms of the distance t from the edge of the wafer. In the exponential decay function, one or more terms make the distance from the edge an exponent. In the exemplary rational function, one or more terms make the distance from the edge in the denominator. These forms can be combined and / or other edge basis functions can be used. The feature of these examples is that their effect can be limited to any narrow edge region of the substrate, that is, limited to the surface area of ​​the substrate with a radial width much smaller than the radius of the substrate and bounded by the perimeter of the substrate. Therefore, the edge basis function enables the spatial dependence of a specific interference only related to the substrate region near the edge of the substrate to be considered in multiple interferences. In this way, it does not destroy the definition and fitting of the field model everywhere on the substrate as a whole. It will also be noted that each example only introduces two additional degrees of freedom to the model. Therefore, additional computational complexity is minimized and additional measurement burden is avoided.

[0076] The inventors have observed that in some processes, alignment and / or overlay error signatures exhibit significant variations between multiple substrates and / or between multiple batches of substrates (e.g., batches). The same situation may be applicable for other process parameters such as CD, focus, or other derived process metrics such as edge placement error. Typically, low-order spatial content within the signature shows relatively large batch-to-batch variations, while higher-order terms remain relatively stable. For example, substrate model parameters associated with the behavior of overlay, alignment, or focus at the edge region of the substrate may change significantly for certain processes that introduce significant edge effects of the overlay, alignment, or focus parameters. For example, for a first substrate, the overlay error may increase sharply in magnitude toward the edge of the wafer, while for a second substrate, the magnitude may be less significant, or even of opposite sign. Figure 5 Plots 502 and 504 show uncorrected overlay errors in two different exemplary products. The arrows indicate the direction of the alignment error. The direction of the error can be inward or outward, depending on the root cause and / or measurement rule (e.g., upper layer to lower layer alignment or vice versa). Figure 5 The edge effects are clearly visible.

[0077] The inventors have realized that current automatic process control (APC) systems will not be able to handle such edge feature identification variations, and therefore current automatic process control (APC) systems will not adequately correct these edge disturbances. Therefore, the ability to improve the yield of edge dies using an APC system is adversely affected by this edge effect.

[0078] A simple solution would be to omit basis functions of the substrate model that are sensitive to variations included in the measurement data. However, when basis functions are removed from the substrate model before fitting the measurement data, there is a risk that variations in the measurement data will affect the determination of the substrate model parameters associated with the remaining basis functions due to crosstalk effects. Variations in the measurement data may in this case (where one or more basis functions that capture the variations are intentionally excluded from the fitting algorithm) affect the values ​​of the remaining substrate model parameters in an unpredictable way.

[0079] Therefore, it is proposed to define a substrate model configured to represent measurement data, including at least a portion of one or more process parameter variation signatures. The substrate model is used to determine substrate model parameters describing the measurement data used in fitting the substrate model. Only after the substrate model parameters are determined, selective modification of the substrate model parameters used in controlling the lithography process is applied based on the (determined, assumed or predicted) similarity of the substrate model parameters to substrate-to-substrate or batch-to-batch (a batch is a batch of substrates) variations of the signatures included in the measurement data. In this way, no process parameter signature variation information is transferred to the substrate model parameters used to control the lithography process; the measurement data is fitted to basis functions, the basis functions including at least one basis function sharing similarity to the process parameter signature variation (e.g., an exponential radial basis function suitable for representing edge signature variations of overlay parameters). At least one basis function "absorbs" substrate-to-substrate or batch-to-batch signature variations, reducing the amount of variation observed in other substrate model parameters used to control the lithography process. Therefore, it is now possible to control the lithography process based on more stable substrate model parameters (e.g., substrate model parameters excluding substrate model parameters associated with basis functions similar to process parameter signature variations).

[0080] Modification may involve removing operations such as reducing substrate model parameters including the variation to zero prior to subsequent use in controlling the lithography process. Control parameters for the lithography process may then be determined exclusively based on substrate model parameters associated with basis functions that are dissimilar to the process parameter signature variation.

[0081] Alternatively, the modification may involve weighting the substrate model parameters based on the similarity of their associated basis functions to batch-to-batch or substrate-to-substrate variations of the process parameter signature.The control parameter may then be a weighted function of the substrate model parameters.

[0082] Alternatively, the modification may involve associating different smoothing factors associated with the exponentially weighted moving average (EWMA) filter functionality of the APC control framework with individual substrate model parameters based on the similarity of the substrate model parameters to the content of the measured data variations. For example, substrate model parameters associated with basis functions that capture fluctuating edge variations may be smoothed to a greater extent (e.g., by selecting a larger smoothing factor lambda) by averaging over more batches across the substrate than substrate model parameters that are insensitive to fluctuations in edge feature identification.

[0083] Although only stable substrate model parameters are used for controlling the lithography process, all substrate model parameters may still be used for monitoring the lithography process and thus all substrate model parameters may be used for monitoring purposes.

[0084] Furthermore, substrate model parameters may be determined and subsequently associated with context information associated with the measurement data. For example, substrate model parameters describing edge feature identification variations of process parameters (e.g., overlay) may be associated with certain lithography process properties (e.g., specific apparatus for processing or specific settings of the lithography process). By associating substrate model parameters with context information, a context for a (predicted) model of substrate model parameters may be defined, which allows for determining correction information for correcting process parameters associated with a substrate to be exposed (for which the context information may at least partially be used).

[0085] Modification of substrate model parameters may be automatically accomplished using a learning model that tracks the evolution of measurement data and determines appropriate modifications of substrate model parameters to achieve stable control of the lithography process.

[0086] In an embodiment, a method for determining control parameters for a lithography process is disclosed, the method comprising: defining a substrate model for representing feature identification of process parameters at various locations on a substrate, the substrate model being defined as a combination of basis functions, the basis functions comprising at least one basis function suitable for representing variations in process parameters between multiple substrates and / or between multiple batches of substrates; receiving measurement results of process parameters at various locations on at least one substrate; calculating substrate model parameters using the measurement results and a combination of basis functions; and determining control parameters based on similarity between the substrate model parameters and at least one basis function and variations in feature identification of process parameters between multiple substrates and / or between multiple batches of substrates.

[0087] In another embodiment, at least one basis function is adapted to represent edge feature identity variations between substrates and / or between batches of substrates.

[0088] In another embodiment, the basis functions are polynomials.

[0089] In another embodiment, the polynomials are Zernike polynomials.

[0090] In another embodiment, the control parameter is a weighted function of a substrate model parameter, wherein the weight is based on a similarity of a basis function associated with the substrate model parameter to a process parameter signature variation between multiple substrates and / or between multiple batches of substrates.

[0091] In another embodiment, weight values ​​of substrate model parameters associated with basis functions that have similar variations in process parameter signatures between multiple substrates and / or between multiple batches of substrates are reset to zero.

[0092] In another embodiment, the control parameter is an APC parameter, such as a smoothing parameter associated with an exponentially weighted moving average (EWMA) filter included in a model used in an automatic process control (APC) strategy.

[0093] In another embodiment, the control parameter is a wafer level control (WLC) parameter configured to control the lithographic apparatus on a per substrate basis.

[0094] In another embodiment, individual substrate model parameters are associated with context information associated with the substrate used to provide the measurement data.

[0095] In another embodiment, stable control of the lithography process is achieved by updating weights associated with substrate model parameters using a learning model that tracks the evolution of measurement data and determines appropriate modifications to the substrate model parameter weights.

[0096] Figure 6 6 is a flow chart showing method steps for determining control parameters for a lithography process as performed by some embodiments of the present invention. At step 601, a substrate model for representing process parameter feature identifications at various locations on a substrate is obtained. As explained above, a substrate model is defined as a combination of basis functions, the basis functions including at least one basis function suitable for representing process parameter feature identification variations between multiple substrates and / or multiple batches of substrates (batches). At step 602, measurement results of process parameters at various locations on at least one substrate are received. At step 603, a model is used to determine substrate model parameters using the measurement results and basis functions. At step 604, control parameters are determined based on substrate model parameters. In some embodiments, determination can be based on similarity of process parameter feature identification variations between at least one basis function and multiple substrates and / or multiple batches of substrates. For example, at least one basis function can be a function describing edge effects. In some embodiments, determination can be based on weight values ​​of substrate model parameters. The values ​​of substrate model parameters associated with at least one basis function have reduced weights.

[0097] Fig. 7Ais a flow chart illustrating how parameter signature capture may be applied to a lithographic process of a wafer or a batch (lot) of wafers. At step 701, measurement data for a first wafer / lot (lot 1) is obtained. For example, the measurement data for lot 1 may include overlay data. At step 702, a substrate model representing process parameter signatures at various locations on the wafer / substrate is invoked. At step 703, the model is used to fit the data obtained for lot 1. The model thereby provides an estimate of the parameter signature, which may be used, for example, in exposure correction of one or more substrates of lot 2 and subsequent lots. However, the signature may also be used as a control parameter to help set the exposure correction for lot 1 itself (so-called "rework"). This is shown at step 704, where the measurement data for lot 2 is input to the model, and at step 705, where the parameters obtained from the model estimate for lot 1 are applied to lot 2. Finally, at step 706, residuals for lot 2 are output. The residuals may be used to make rework decisions, for example, for determining exposure corrections for a large number of wafers. The residuals may be used in statistical process control (SPC), for example, for monitoring SPC trends. A residual below a predetermined threshold may be obtained, for example, by controlling the process through exposure correction. The threshold may be application specific and determined based on the requirements of a specific substrate.

[0098] Figure 7B is a flow chart illustrating how parameter feature identification capture may be applied according to an embodiment of the present invention. At step 711, for Fig. 7A Step 701 of the method of obtaining measurement data for the first wafer / batch (Batch 1). Steps 712 and 713 are also substantially the same as Fig. 7A702 and 703 are the same. It should be noted that the modeling at this point preferably includes all basis functions that are found to provide the best estimate of the parameter signature for the substrate at this stage of the lithography process. At step 714, a revised or modified parameter signature is determined by removing or stripping out those parts of the parameter estimate that are known or predetermined to produce "noise", which distorts the parameter signature when the parameter signature is used as a control parameter (i.e., for the processing of the next batch, batch 2, rather than for subsequent processing of batch 1). In the control case implementation, steps 715 to 717 are related to batch 2. In step 715, batch 2 can be exposed using the parameters of the modified parameter signature determined in step 714, in which the noise effects may have been removed. The modified parameter signature may also be referred to as the control parameter signature. In step 716, batch 2 can be measured. In step 717, the measurement results of batch 2 and the control parameter signature can then be used to determine the residual of batch 2. It is possible to perform the method of parameter signature capture without considering and / or removing the noise effect (ie, skipping step 714). However, this method may result in interfering fits for other parameters at various locations on the substrate.

[0099] Table 1 below provides overlay measurement data from processing of wafers, in which three methods of determining control parameter signatures are compared. The data shown include overlay measurement results OvX and OvY in nanometers for orthogonal x and y directions at various locations on the substrate. The data is the result of a statistical analysis from a large number of measurements made on the substrate, and includes for both the x and y directions: 99.7% of the data are small values ​​(99.7); the maximum value (Max); the value corresponding to the mean + 3 standard deviations higher than the mean (m + 3sd) and the magnitude of 3 standard deviations (3sd). In an exemplary implementation, data consisting of the mean plus the value 3 standard deviations higher than the mean (m + 3sd) can be used to estimate control parameters. Measurement data for batch 1 is shown, and this has been used by the substrate model to estimate control parameters, which have then been applied to the processing of batch 2 by each of the three methods. In method 1, the model has been applied without using any exponential edge model (basis function) to consider the effects close to the edge of the substrate. In Method 2, the model has been applied using an exponential marginal model as well as the full parameter signature applied to the processing of Batch 2. In Method 3, the exponential marginal model has been applied in the estimation of the parameter signature, but the effects of the marginal model have been removed for the purpose of applying the control parameter signature in the processing of Batch 2. It should be noted that the control parameter signatures of Method 1 and Method 3 are not the same, this is because the use of exponential marginal modeling in the full parameter signature estimation affects how other parts of the model (i.e., other basis functions) behave in determining the parameter estimates. Subsequently removing the effects of the exponential marginal model in Method 3 does not produce the same parameter signature as if the exponential marginal model was not used in the initial model estimation as in Method 1.

[0100] Table 1

[0101]

[0102]

[0103] Table 1 shows that when there is an exponential edge model used to fit the data for Batch 1 and the resulting unmodified control parameter signature is used for Batch 2, this results in an increase in the mean + 3 standard deviation (m + 3s) overlay values ​​OvX and OvY for Batch 2 (Model 2) compared to when the exponential edge model (Model 1) is not used - 3.54nm and 3.70nm compared to 3.47nm and 3.48nm. However, when Model 3 is used in which the noise effects of the exponential edge model are removed after the initial model estimation, the overlay values ​​are minimal - 3.28 and 2.91.

[0104] Figure 8The invention is a flow chart illustrating the application of a method for parameter signature capture applied to lithographic processing of a wafer or batches of wafers, the method utilizing a model to determine operating parameters including at least one control parameter. The lithographic process involves a plurality of processing steps performed on a series of substrates. At 801, after a first step in the process, measurements of the measured parameters at various locations on the substrate are obtained. At 802, the measurements are provided as input to a model to determine a complete signature estimate of the operating parameters at various locations on the substrate. At this stage, preferably, all basis functions found to provide the best estimate of the parameter signature are used in the model. At 803, the operating parameters are applied using the complete signature estimate in a second processing step of the substrate. In addition, at 804, the complete signature estimate is modified to take into account an effect that is predetermined to have a distorting effect on at least one control parameter, thereby obtaining a control parameter signature estimate of the substrate. At 805, for the first step of the lithographic process, the control parameter signature estimate is applied to the processing of subsequent substrates in the series of substrates.

[0105] Another use of each wafer or each batch of fitting results can be used to obtain the characteristic identification of each background. Background data is data related to the product unit and / or the process that affects the product unit. For example, the background data may include data related to the lithography equipment LA, such as equipment ID, equipment parameters. The background data may also include measurement data related to the process for patterning the product unit performed by the equipment, such as obtaining temperature, pressure, time / date input data. The background data can maintain some information that is not present in the distribution data and can help prediction. For example, each device and chuck can have a unique characteristic identification that affects the multidimensional data. Knowing that the wafer is exposed on a specific device or chuck can help the model to preferably capture the parameters of interest. The background data can be related to the pre-defined structure present on the patterned substrate, for example, due to the use of a specific device or chuck for patterning the substrate. For example, a product unit patterned using a first chuck may have different properties from a product unit patterned using a second chuck. The prediction of the target structure associated with the pre-defined structure on the product unit can be based on the provided background data. For example, background variables can be added as part of the feedforward aspect of the lithography process.

[0106] Feature identification determination of individual backgrounds may be performed on a batch, yielding an optimized feature identification decomposition over the background variables that may be used, for example, for control of individual backgrounds (on a specific lithographic processing tool such as an etcher or on a scanner).

[0107] The measurement data can be further filtered before any subsequent modeling occurs. Typically, noise components are present in the measurement data, which can compromise control actions intended to improve the process based on the modeled measurement data. In order to prevent noise from affecting modeling and / or adversely controlling, it is proposed to remove periodic content in the measurement data with a spatial frequency exceeding a certain threshold. In the case where these process control capabilities do not extend to spatial frequencies above the threshold, the threshold can be based on the process control capabilities, for example. In an embodiment, measurement data associated with the spatial signature of the performance data is obtained, and the spatial signature is transformed into the frequency domain (Fourier transform) to which a frequency filter (e.g., a low-pass filter) is applied. Then, the filtered data is subsequently transformed to the spatial domain by applying an inverse Fourier transform to the filtered data. The filtered measurement data is modeled and serves as an input for process control actions (e.g., APC control).

[0108] The filter parameters (cut-off frequency, functional expression of the frequency filter) can be based on learning methods, for example based on comparing electron beam metrology data (scanning electron beam microscope, SEM) with measurement data of interest (typically based on scatterometry data such as overlay, focus or critical dimension (CD) data).

[0109] In an embodiment, measurement data, such as overprint, focus or CD data, are obtained. The measurement data are then processed using a spatial filtering method, wherein the parameters of the filter used in the spatial filtering are defined based on process control capabilities and / or prior knowledge about the spatial frequency characteristics of noise components included in the measurement data.

[0110] In an embodiment, defining the parameters of the filter is based on a comparison between first measurement data obtained using a first metrology device and second measurement data obtained using a second metrology device.

[0111] In an embodiment, the filtered measurement data is modeled using any of the modeling methods disclosed in this document.

[0112] In an embodiment, the modelled filtered measurement data is used for process control purposes (eg, controlling overlay, CD or focus of a lithographic apparatus).

[0113] Other embodiments of the invention are disclosed in the following list of numbered aspects:

[0114] 1. A method for determining control parameters for a photolithography process, the method comprising:

[0115] Obtaining a substrate model for representing process parameter signatures at locations on the substrate, the substrate model being defined as a combination of basis functions, the basis functions including at least one basis function suitable for representing variations in process parameter signatures between a plurality of substrates and / or between a plurality of batches of substrates;

[0116] receiving measurement results of process parameters at various locations on at least one substrate;

[0117] Determining substrate model parameters using the measurement results and the basis functions; and

[0118] A control parameter is determined based on the substrate model parameter and the similarity of the at least one basis function to a process parameter signature variation between a plurality of substrates and / or between a plurality of batches of substrates.

[0119] 2. The method of clause 1, wherein the at least one basis function is suitable for representing edge feature identity variations between multiple substrates and / or between multiple batches of substrates.

[0120] 3. The method according to aspect 1 or 2, wherein the basis function is a polynomial, such as a Zernike polynomial.

[0121] 4. A method as described in any of the preceding aspects, wherein the control parameter is a weight function of the substrate model parameter, wherein the weight is based on the similarity between the basis function associated with the substrate model parameter and the process parameter feature identification changes between multiple substrates and / or multiple batches of substrates.

[0122] 5. The method of clause 4, wherein weights of substrate model parameters associated with basis functions that exceed a measure of similarity to process parameter signature variations between multiple substrates and / or between multiple batches of substrates are zeroed.

[0123] 6. A method as described in any preceding aspect, wherein the control parameter is an automatic process control (APC) model parameter, such as a smoothing parameter associated with an exponentially weighted moving average (EWMA) filter used in the APC model.

[0124] 7. The method of clause 6, wherein the APC model comprises a plurality of smoothing parameters, and at least two of the plurality of smoothing parameters have different functional dependencies on the substrate model parameters.

[0125] 8. The method of clauses 1 to 5, wherein the control parameters are wafer level control (WLC) parameters configured to control the lithography equipment on an individual substrate basis.

[0126] 9. A method as claimed in any preceding clause, wherein individual substrate model parameters are associated with context information associated with the substrate used to provide measurement data.

[0127] 10. The method of any preceding clause, further comprising: keeping the control parameters up to date by using a learning model that tracks the evolution of the measurement data and determines appropriate modifications of the control parameters to achieve stable control of the lithography process.

[0128] 11. A method for determining control parameters for a photolithography process, the method comprising:

[0129] Obtaining a substrate model for representing process parameter signatures at locations on the substrate, the substrate model being defined as a combination of basis functions, the basis functions including at least one basis function suitable for representing variations in process parameter signatures between a plurality of substrates and / or between a plurality of batches of substrates;

[0130] receiving measurement results of process parameters at various locations on at least one substrate;

[0131] Determining substrate model parameters using the measurement results and the basis functions; and

[0132] The control parameter is determined based on weighted values ​​of the substrate model parameters, wherein values ​​of the substrate model parameters associated with the at least one basis function have decreasing weights.

[0133] 12. A method of determining operating parameters including at least one control parameter for a lithography process using a model, the lithography process involving a plurality of processing steps performed on a series of substrates, the method comprising:

[0134] After a first step in the process, obtaining measurement results for a measurement parameter at locations on at least one substrate;

[0135] applying the measurements to the model to determine complete signature estimates of operating parameters at locations across the substrate;

[0136] applying said operating parameters using said complete feature identification estimate at a second processing step of said substrate;

[0137] modifying the complete signature estimate to account for an effect predetermined to have a distorting effect on the at least one control parameter, thereby obtaining a control parameter signature estimate for the substrate; and

[0138] At the first step, the control parameter signature estimate is applied in processing of at least one subsequent substrate in the series of substrates.

[0139] 13. A method as claimed in clause 12, wherein modifying the complete signature estimate to take into account effects predetermined to have a distorting effect on the at least one control parameter is based on known signature behaviour determined during a control strategy setting phase.

[0140] 14. A method as described in aspect 12 or aspect 13, wherein the operating parameters used in the model are selected by tuning an exponentially weighted moving average (EWMA) filter to provide a half-life decay distance or decay range parameter λ value for each of the parameters.

[0141] 15. A method as described in any preceding aspect, wherein the control parameter signature estimate is used to determine individual background signatures associated with specific equipment used in the lithography process.

[0142] 16. A computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to perform the method of any one of aspects 1 to 15.

[0143] 17. An apparatus for determining control parameters for a lithography process, the apparatus comprising one or more processors configured to execute computer program code to perform the method of any one of clauses 1 to 15.

[0144] 18. A measurement device, comprising the device according to aspect 17.

[0145] 19. A detection device, comprising the device according to aspect 17.

[0146] 20. A lithographic apparatus comprising the apparatus of clause 17.

[0147] 21. A method of modeling measurement data associated with a spatial distribution of values ​​of a process parameter across a substrate undergoing a process, the method comprising:

[0148] Obtaining measurement data;

[0149] transforming the measured data into the frequency domain using a Fourier transform operation;

[0150] filtering the transformed measurement data using a spatial filter configured to at least partially remove spatial frequency components associated with known noise characteristics and / or limitations of controls of the process;

[0151] using an inverse Fourier transform operation to obtain the filtered measurement data, thereby transforming the filtered measurement data in the frequency domain to the spatial domain; and

[0152] The filtered measurement data is modeled to provide input to control facilities for the process.

[0153] The steps of the method described above can be automated in any general purpose data processing hardware (computer) as long as the general purpose data processing hardware can access the measurement data. Figure 2 The hardware can be remote from the processing equipment, even located in a different country. Figure 6 Components of a suitable data processing apparatus (DPA) are shown in FIG. The apparatus may be configured to load a computer program product comprising computer executable code. This may enable the computer components to implement the functionality of a PCA apparatus and / or RCA apparatus as described above when the computer program product is downloaded.

[0154] Reference Fig. 9 , the memory 929 connected to the processor 927 may include multiple memory components, such as a hard disk 961, a read-only memory (ROM) 962, an electrically erasable programmable read-only memory (EEPROM) 963, and / or a random access memory (RAM) 964. It is not necessary for all of the aforementioned memory components to be present. In addition, the aforementioned memory components do not have to be physically very close to the processor 927 or very close to each other. They can be located at a certain distance apart.

[0155] The processor 927 may also be connected to some kind of user interface, such as a keyboard 965 or a mouse 966. A touch screen, trackball, voice converter or other interface known to those skilled in the art may also be used.

[0156] The processor 927 may be connected to a reading unit 967 configured to read data, e.g. in the form of computer executable code, from a data carrier such as a floppy disk 968 or a CDROM 969 and in some cases store the data on a data carrier such as a floppy disk 968 or a CDROM 969. A DVD or other data carrier known to those skilled in the art may also be used.

[0157] The processor 927 may also be connected to a printer 970 for printing output data on paper, and to any other type of display 971 known to those skilled in the art, such as a monitor or LCD (Liquid Crystal Display).

[0158] The processor 927 may be connected to a communication network 972, such as a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), etc., by means of a transmitter / receiver 973 responsible for input / output (I / O). The processor 927 may be configured to communicate with other communication systems via the communication network 972. In an embodiment of the present invention, an external computer (not shown) (e.g., an operator's personal computer) may log into the processor 927 via the communication network 972.

[0159] Processor 927 can be implemented as a stand-alone system or as multiple processing units operating in parallel, wherein each processing unit is configured to perform subtasks of a larger program. The processing unit can also be divided into one or more main processing units and several sub-processing units. Some processing units of processor 927 can even be located at a distance from other processing units and communicate via communication network 972. The connection between modules can be wired or wireless.

[0160] The computer system may be any signal processing system having analog and / or digital and / or software technology configured to perform the functions discussed in the present invention.

[0161] Although specific reference may be made in the present invention to the use of lithographic apparatus in IC manufacturing, it should be understood that the lithographic apparatus described in the present invention may have other applications, such as manufacturing integrated optical systems, guidance and detection patterns for magnetic domain memories, flat panel displays, liquid crystal displays (LCDs), thin film magnetic heads, etc. As already mentioned, the present invention may be applied to industrial processing applications that are completely separate from lithography. Examples may be in the production of optical components, automobile manufacturing, construction-any number of applications where the target data exists in the form of measurements made at a certain spatial distribution throughout the product. As in the example of lithography. Although specific reference may be made above to the use of embodiments of the present invention in the context of optical lithography, it should be understood that the present invention may be used in other types of lithography (e.g., imprint lithography) and is not limited to optical lithography where the context permits. In imprint lithography, the topography in the patterning device defines the pattern produced on the substrate. The topography of the patterning device may be pressed into a resist layer supplied to a substrate; on the substrate, the resist is cured by applying electromagnetic radiation, heat, pressure, or a combination thereof. The patterning device is moved out of the resist after the resist is cured, leaving a pattern in the resist.

[0162] As used herein, the terms "radiation" and "beam" encompass all types of electromagnetic radiation, including ultraviolet (UV) radiation (e.g., having a wavelength equal to or approximately equal to 365 nm, 248 nm, 193 nm, 157 nm, or 126 nm) and extreme ultraviolet (EUV) radiation (e.g., having a wavelength in the range of 5 nm to 20 nm), as well as particle beams, such as ion beams or electron beams.

[0163] The above description is intended to be illustrative, not restrictive. Therefore, it will be apparent to those skilled in the art that the invention described may be modified without departing from the spirit and scope of the claims appended hereto. In addition, it should be understood that the structural features or method steps shown or described in any one embodiment of the present invention may also be used in other embodiments.

Claims

1. A method for determining control parameters for a photolithography process, the method comprising: Obtaining a substrate model for representing process parameter signatures at locations on the substrate, the substrate model being defined as a combination of basis functions, the basis functions including at least one basis function suitable for representing variations in process parameter signatures between a plurality of substrates and / or between a plurality of batches of substrates; receiving measurement results of process parameters at various locations on at least one substrate; Determine substrate model parameters using the measurement results and the basis functions; as well as The control parameter is determined based on the substrate model parameter and the at least one basis function and similarity of process parameter signature variations between a plurality of substrates and / or between a plurality of batches of substrates.

2. The method of claim 1, wherein: The at least one basis function is adapted to represent edge feature identity variations between substrates and / or between batches of substrates.

3. The method of claim 1, wherein: The basis function is a polynomial.

4. The method of claim 1, wherein: The control parameter is a weighted function of the substrate model parameter, wherein the weight is based on a similarity of the basis function associated with the substrate model parameter to a process parameter signature variation between multiple substrates and / or between multiple batches of substrates.

5. The method of claim 4, wherein: Weight values ​​of substrate model parameters associated with basis functions that exceed a measure of similarity to process parameter signature variations between substrates and / or between batches of substrates are reduced or zeroed.

6. The method of claim 1, wherein: The control parameters are automatic process control (APC) model parameters.

7. The method of claim 6, wherein: The automatic process control model includes a plurality of smoothing parameters, and at least two smoothing parameters of the plurality of smoothing parameters have different functional dependencies on the substrate model parameters.

8. The method of claim 1, wherein: The control parameters are configured to control the lithographic apparatus on a per substrate basis.

9. The method of claim 1, wherein: Individual substrate model parameters are associated with context information associated with the substrate used to provide the measurement data.

10. The method of claim 1, further comprising: The control parameters are kept up to date by using a learning model that tracks the evolution of the measurement data and determines appropriate modifications of the control parameters to achieve stable control of the lithography process.

11. A computer program product comprising instructions which, when executed on at least one processor, cause the at least one processor to perform the method of claim 1.

12. An apparatus for determining control parameters for a lithography process, the apparatus comprising one or more processors configured to execute computer program code to perform the method of any one of claims 1 to 10.

13. A measurement device comprising the device as claimed in claim 12.

14. A lithographic apparatus comprising the apparatus as claimed in claim 12.

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