Subfield control of lithographic processes and associated apparatuses

Abstract

The invention relates to subfield control of lithographic processes and associated apparatuses. There is disclosed a method for determining an in-field correction to control a lithographic apparatus configured to expose a pattern on an exposure field of a substrate, the method comprising: obtaining metrology data for determining the in-field correction; determining an accuracy metric indicative of a lower accuracy in the event that the metrology data is unreliable and / or in the event that the lithographic apparatus is limited in initiating a potential initiation input based on the metrology data; and determining the intra-field correction based at least in part on the accuracy metric.

Description

[0001] Division Explanation

[0002] This application is a divisional application of a Chinese patent application with application number 202080048266.7 and title "Sub - field control of lithography processes and associated equipment", filed on June 10, 2020.

[0003] Cross - Reference to Related Applications

[0004] This application claims the priority of EP application 19184412.5 filed on July 4, 2019 and EP application 19186820.7 filed on July 17, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0005] The present invention relates to methods and apparatus for applying a pattern to a substrate and / or measuring the pattern in a lithography process. Background Art

[0006] A lithographic apparatus is a machine that applies a desired pattern onto a substrate, typically onto a target portion of the substrate. For example, a lithographic apparatus can be used in the manufacture of integrated circuits (ICs). In such a case, a patterning device, alternatively referred to as a mask or reticle, can be used to generate a circuit pattern to be formed on the various layers of the IC. The pattern can be transferred onto a target portion (e.g., including a part of a die, a single die, or several dice) of a substrate (e.g., a silicon wafer). The transfer of the pattern is typically via imaging onto a layer of radiation - sensitive material (resist) provided on the substrate. Usually, a single substrate will contain a network of adjacent target portions that are successively patterned. Known lithographic apparatuses include so - called steppers, in which each target portion is irradiated by exposing the entire pattern onto the target portion at once, and so - called scanners, in which each target portion is irradiated by scanning the pattern with a radiation beam in a given direction ("scan" direction), while synchronously scanning the substrate parallel or anti - parallel to this direction. The pattern can also be transferred from the patterning device to the substrate by imprinting the pattern onto the substrate.

[0007] To monitor a lithography process, parameters of a patterned substrate are measured. For example, the parameters can include overlay error between successive layers formed in or on the patterned substrate and critical dimension (CD) of a developed photosensitive resist. The measurement can be performed on a product substrate and / or on a dedicated metrology target. There are various techniques for measuring microstructures formed in a lithography process, including using a scanning electron microscope and various dedicated tools. A fast and non-invasive dedicated inspection tool is a scatterometer, in which a radiation beam is directed onto a target on a substrate surface, and properties of the scattered or reflected light beam are measured. Two main types of scatterometers are known. A spectroscopic scatterometer directs a broadband radiation beam onto a substrate and measures the spectrum (intensity as a function of wavelength) of the radiation scattered into a specific narrow angular range. An angle-resolved scatterometer uses a monochromatic radiation beam and measures the intensity of the scattered radiation as a function of angle.

[0008] Examples of known scatterometers include angle-resolved scatterometers of the type described in US2006033921A1 and US2010201963A1. The targets used by such scatterometers are relatively large (e.g., 40 μm × 40 μm) gratings, and the measurement beam produces a spot smaller than the grating (i.e., underfilled grating). In addition to measuring overlay based on reconstruction of the measured feature shape, devices such as those described in published patent application US2006066855A1 can be used to measure diffraction-based overlay. Diffraction-based overlay metrology using diffraction order dark field imaging enables overlay measurements on smaller targets. Examples of dark field imaging metrology can be found in international patent applications WO 2009 / 078708 and WO 2009 / 106279, which are incorporated herein by reference in their entirety. Further developments of this technique are described in published patent publications US20110027704A, US20110043791A, US2011102753A1, US20120044470A, US20123581A, US20130258310A, US20130271740A, and WO2013178422A1. These targets can be smaller than the illumination spot and can be surrounded by product structures on a wafer. Multiple gratings can be measured in one image using a composite grating target. The content of all of these applications is also incorporated herein by reference.

[0009] Currently, overlay errors are controlled and corrected by, for example, a correction model described in US2013230797A1. In recent years, advanced process control techniques have been introduced and measurements of metrology targets applied to a substrate and the applied device patterns are used. These targets allow the measurement of overlay using high throughput inspection equipment such as a scatterometer, and this measurement can be used to generate a correction that is fed back into the lithography apparatus when subsequent substrates are continued to be patterned. An example of advanced process control (APC) is described in, for example, US2012008127A1. The inspection equipment can be separate from the lithography apparatus. Within the lithography apparatus, a wafer correction model is conventionally applied based on measurements of overlay targets set on the substrate as a preparatory step for each patterning operation. Current correction models include higher order models to correct for non-linear distortion of the wafer. The correction model can also be extended to account for other measurements and / or computational effects such as thermal distortion during the patterning operation.

[0010] However, while the use of higher order models may be able to account for more effects, the use of such models may be limited if the patterning device itself does not provide control of the corresponding parameters during the patterning operation. In addition, even advanced correction models may be insufficient or less than ideal for correcting certain overlay errors.

[0011] It is desirable to improve such process control methods by, for example, addressing at least one of the highlighted problems above. Summary of the Invention

[0012] In a first aspect of the present invention, there is provided a method for determining in-field correction for sub-field control of a lithography process for exposing a pattern on an exposure field of a substrate, the exposure field including a plurality of sub-fields, the method comprising: obtaining a database including in-field fingerprint data linked to historical lithography apparatus metrology data; determining an estimate of the in-field fingerprint from the lithography apparatus metrology data and the database; and determining the in-field correction of the lithography process based on the estimated in-field fingerprint.

[0013] In a second aspect of the present invention, there is provided a method for determining in-field correction for sub-field control of a lithography process for exposing a pattern on an exposure field of a substrate, the exposure field including a plurality of sub-fields, the method comprising: performing an optimization to determine the in-field correction, the optimization maximizing the number of the sub-fields within specifications.

[0014] In a third aspect of the present invention, there is provided a method for determining in-field correction for sub-field control of a manufacturing process, the manufacturing process including a lithography process for exposing a pattern on an exposure field of a substrate, the exposure field including a plurality of sub-fields, the manufacturing process including at least one additional processing step, the method including performing an optimization to determine the in-field correction, the optimization including a co-optimization based on at least one lithography parameter related to the lithography process and at least one processing parameter related to the at least one additional processing step.

[0015] In a fourth aspect of the present invention, there is provided a method for determining in-field correction for sub-field control of a lithography process, the lithography process being for exposing a pattern on an exposure field of a substrate in forming a plurality of stacked layers, the exposure field including a plurality of sub-fields, the method including constructing a physical and / or empirical through-stack model that describes how an interested parameter propagates layer-by-layer through the stack.

[0016] In a fifth aspect of the present invention, there is provided a method for determining in-field correction for sub-field control of a lithography process, the lithography process being for exposing a pattern on an exposure field of a substrate, the exposure field including a plurality of sub-fields, the method including: determining a sensitivity metric that describes the sensitivity of the correction to input data for determining the correction and / or layout of the pattern; and determining the in-field correction for sub-field control based on the sensitivity metric.

[0017] In a sixth aspect of the present invention, there is provided a method for determining in-field correction for controlling a lithography apparatus, the lithography apparatus being configured to expose a pattern on an exposure field of a substrate, the method including: acquiring measurement data for determining the in-field correction; determining a precision metric indicating a lower precision in cases where the measurement data is unreliable and / or in cases where the lithography apparatus is limited in initiating a potential start input based on the measurement data; and determining the in-field correction at least in part based on the precision metric.

[0018] There is also disclosed a computer program including program instructions that, when run on a suitable device, are operable to perform the method of any of the above aspects.

[0019] Other aspects, features, and advantages of the present invention, as well as the structure and operation of various embodiments of the present invention, are described in detail below with reference to the accompanying drawings. Note that the present invention is not limited to the specific embodiments described herein. Such embodiments are presented herein for illustrative purposes only. Based on the teachings contained herein, additional embodiments will be apparent to those skilled in the relevant art(s). BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Embodiments of the present invention will now be described by way of example with reference to the accompanying drawings, in which:

[0021] Figure 1 depicts a lithography apparatus together with other equipment of a production facility for forming semiconductor devices;

[0022] Figure 2 depicts a schematic diagram of overall lithography, where overall lithography represents the cooperation between three key technologies for optimizing semiconductor manufacturing;

[0023] Figure 3 shows an exemplary source of process parameters;

[0024] Figure 4 is an overlay map with respect to field positions, showing the influence of die stress for a specific manufacturing process; and

[0025] Figure 5 is a flowchart of a method according to an embodiment of the present invention. Detailed Description

[0026] Before describing embodiments of the present invention in detail, it is instructive to present an example environment in which embodiments of the present invention may be implemented.

[0027] Figure 1 At 200, a lithography apparatus LA is shown as part of an industrial production facility for implementing a high-volume lithography manufacturing process. In this example, the manufacturing process is applicable to manufacturing semiconductor products (integrated circuits) on a substrate such as a semiconductor wafer. Those skilled in the art will recognize that a wide variety of products can be manufactured by processing different types of substrates with different variations of this process. Using the production of semiconductor products purely as an example, it still has great commercial significance today.

[0028] Within the lithography apparatus (or simply "lithography tool" 200), a measurement station MEA is shown at 202 and an exposure station EXP is shown at 204. A control unit LACU is shown at 206. In this example, each substrate accesses the measurement station and the exposure station to have a pattern applied. In an optical lithography apparatus, for example, a projection system is used to transfer a product pattern from a patterning device MA to a substrate using conditioned radiation and the projection system. This is achieved by forming a patterned image in a layer of radiation-sensitive resist material.

[0029] The term "projection system" as used herein should be interpreted broadly to include any type of projection system, including refractive, reflective, catadioptric, magnetic, electromagnetic, and electrostatic optical systems, or any combination thereof, adapted to the exposure radiation being used, or for other factors such as the use of an immersion liquid or the use of a vacuum. The patterning device MA may be a mask or a reticle that imparts a pattern to a radiation beam that is transmitted or reflected by the patterning device. Well-known operating modes include step mode and scan mode. It is well known that the projection system can cooperate with the support and positioning systems for the substrate and the patterning device in various ways to apply a desired pattern to a number of target portions on the substrate. A programmable patterning device may be used instead of a reticle having a fixed pattern. For example, the radiation may include electromagnetic radiation in the deep ultraviolet (DUV) or extreme ultraviolet (EUV) bands. The present invention is also applicable to other types of lithography processes, such as imprint lithography and direct write lithography, such as electron beam lithography.

[0030] A lithography equipment control unit LACU that controls all movements and measurements of various actuators and sensors to receive a substrate W and a reticle MA and to perform a patterning operation. The LACU also includes signal processing and data processing capabilities to perform desired calculations related to the operation of the equipment. In practice, the control unit LACU will be implemented as a system consisting of many subunits, each subunit handling real-time data acquisition, processing, and control of subsystems or components within the equipment.

[0031] Before applying a pattern to a substrate at exposure station EXP, the substrate is processed at measurement station MEA so that various preparation steps can be performed. The preparation steps can include mapping the surface height of the substrate using a leveling sensor and measuring the position of alignment marks on the substrate using an alignment sensor. The alignment marks are nominally arranged in a regular grid pattern. However, due to inaccuracies in creating the marks, and also due to deformations that occur in the substrate throughout its processing, the marks deviate from the ideal grid. Therefore, if the apparatus is to print product features at the correct locations with very high precision, the alignment sensor must actually measure the positions of many marks on the substrate area in addition to measuring the position and orientation of the substrate. The apparatus can be of the so-called dual-platform type, having two substrate tables, each substrate table having a positioning system controlled by a control unit LACU. While one substrate on one substrate table is being exposed at exposure station EXP, another substrate can be loaded onto the other substrate table at measurement station MEA so that various preparation steps can be performed. Thus, the measurement of alignment marks is very time-consuming, and providing two substrate tables can significantly increase the throughput of the apparatus. If a position sensor IF cannot measure the position of the substrate when it is located at the measurement station and the exposure station, a second position sensor can be provided to enable tracking of the position of the substrate table at both stations. For example, a lithographic apparatus LA can be of the so-called dual-platform type, having two substrate tables and two stations - an exposure station and a measurement station - between which the substrate tables can be exchanged.

[0032] Within a production facility, the apparatus 200 forms part of a "lithography cell" or "lithography cluster", which also contains a coating apparatus 208 for applying a photosensitive resist and other coatings to the substrate W to be patterned 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 physical resist pattern. Between all these apparatuses, a substrate handling system is responsible for supporting the substrates and transferring them from one apparatus to another. These apparatuses, collectively referred to as the track, are controlled by a track control unit, which itself is controlled by a supervisory control system SCS that also controls the lithographic apparatus via a lithographic apparatus control unit LACU. Thus, the different apparatuses can be operated to maximize throughput and processing efficiency. The supervisory control system SCS receives recipe information R, which provides a more detailed definition of the steps to be performed to create each patterned substrate.

[0033] Once the pattern has been applied and developed in the lithography cell, the patterned substrate 220 is transferred to other processing equipment, as shown at 222, 224, 226. A wide range of processing steps are implemented by various equipment in a typical manufacturing facility. As an example, the equipment 222 in this embodiment is an etch station, and the equipment 224 performs an etch-back annealing step. Additional physical and / or chemical processing steps are applied in additional equipment 226 and so on. Fabricating a real device may require many types of operations, such as material deposition, 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 pieces of equipment. As another example, equipment and processing steps for implementing self-aligned multiple patterning can be provided to generate multiple smaller features based on a precursor pattern laid down by a lithography apparatus.

[0034] As is well known, the fabrication of semiconductor devices involves many repetitions of such processing to build device structures layer by layer on a substrate using appropriate materials and patterns. Thus, the substrates 230 arriving at the lithography cluster can be newly prepared substrates, or they can be substrates that have been previously processed in this cluster or in an entirely different piece of equipment. Similarly, depending on the processing required, the substrates 232 leaving the equipment 226 can be returned for subsequent patterning operations in the same lithography cluster, they can be designated for patterning operations in a different cluster, or they can be finished products that are to be sent for dicing and packaging.

[0035] Each layer of the product structure requires a different set of processing steps, and the equipment 226 used in each layer can be completely different in type. Moreover, even when the processing steps to be applied by the equipment 226 are nominally the same, in a large facility, there can be several supposedly identical machines working in parallel to perform step 226 on different substrates. Minor differences in setup or malfunction between these machines can mean that they affect different substrates in different ways. Even for relatively common steps for each layer, such as etching (equipment 222), it can be implemented by several nominally identical but parallel working etch devices to maximize production volume. Additionally, 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 special requirements such as anisotropic etching.

[0036] As described above, the pre- and / or post-processing may be carried out in other lithography apparatuses, even in different types of lithography apparatuses. For example, some layers in a device manufacturing process that have very high requirements for parameters such as resolution and overlay may be carried out using more advanced lithography tools than other layers with lower requirements. Thus, some layers may be exposed in an immersion lithography tool, while other layers may be exposed in a "dry" tool. Some layers may be exposed in a tool operating at a DUV wavelength, while other layers are exposed using EUV wavelength radiation.

[0037] In order to expose a substrate correctly and consistently by a lithography apparatus, it is desirable to inspect the exposed substrate to measure properties such as overlay error between subsequent layers, line thickness, critical dimension (CD), etc. Thus, the manufacturing facility in which the lithography cell LC is located also includes a metrology system that receives some or all of the substrates W that have been processed in the lithography cell. The metrology results are provided directly or indirectly to the supervisory control system SCS. If an error is detected, the exposure of subsequent substrates can be adjusted, especially if the metrology can be completed quickly enough and fast enough such that other substrates in the same batch still need to be exposed. In addition, the exposed substrates can be stripped and reprocessed to increase yield, or discarded, thus avoiding performing further processing on substrates known to be defective. In the case where only some target portions of the substrate are faulty, further exposure can only be performed on those good target portions.

[0038] Figure 1 Also shown is metrology equipment 240, which is provided for measuring parameters of a product at a desired stage in the manufacturing process. A common example of a metrology station in a modern lithography production facility is a scatterometer, such as a dark field scatterometer, an angle-resolved scatterometer, or a spectroscopic scatterometer, and it can be applied to measure properties of a developed substrate at 220 before etching in apparatus 222. Using the metrology equipment 240, important performance parameters such as overlay or critical dimension (CD) can be determined, for example, not to meet the specified accuracy requirements in the developed resist. Before the etching step, there is an opportunity to strip the developed resist and reprocess the substrate 220 through the lithography cluster. Through small adjustments over time by the supervisory control system SCS and / or the control unit LACU 206, the metrology results 242 from the equipment 240 can be used to maintain the precise performance of the patterning operations in the lithography cluster, thus minimizing the risk that the product does not meet the specifications and requires reprocessing.

[0039] In addition, the metrology equipment 240 and / or other metrology equipment (not shown) can be applied to measure properties of the processed substrates 232, 234 and the incoming substrate 230. This metrology equipment can be used on the processed substrates to determine important parameters such as overlay or CD.

[0040] Typically, the patterning process in a lithographic apparatus LA is one of the most critical steps in the process, which requires high precision in the dimensions and placement of structures on a substrate W. To ensure this high precision, three systems can be combined in a so-called "integrated" control environment as schematically depicted in Figure 2 . One of these systems is the lithographic apparatus LA, which is (virtually) connected to a metrology tool MET (the second system) and a computer system CL (the third system). The key to such an "integrated" environment is to optimize the collaboration between these three systems to enhance the overall process window and provide a tight control loop to ensure that the patterning performed by the lithographic apparatus LA remains within the process window. The process window defines a range of process parameters (e.g., dose, focus, overlay), within which a particular manufacturing process produces a defined result (e.g., a functional semiconductor device) - typically allowing the process parameters in a lithography process or patterning process to vary within this range.

[0041] The computer system CL can use (parts of) the design layout to be patterned to predict which resolution enhancement techniques are to be used and perform computational lithography simulations and calculations to determine which mask layouts and lithographic apparatus settings achieve the maximum overall process window of the patterning process ( Figure 2 depicted by the double arrows in the first scale SC1 in ). Typically, the resolution enhancement techniques are set to match the patterning capabilities of the lithographic apparatus LA. The computer system CL can also be used to detect where the lithographic apparatus LA is currently operating within the process window (e.g., using input from the metrology tool MET) to predict whether there might be defects due to, for example, sub-optimal processing ( Figure 2 depicted by the arrow pointing to "0" in the second scale SC2 in ).

[0042] The metrology tool MET can provide input to the computer system CL for accurate simulations and predictions and can provide feedback to the lithographic apparatus LA to identify possible drifts, e.g., in the calibration state of the lithographic apparatus LA ( Figure 2 depicted by the multiple arrows in the third scale SC3 in ).

[0043] A variety of techniques can be used to improve the accuracy of replicating a pattern on a substrate. In IC production, accurately replicating a pattern onto a substrate is not the only consideration. Another concern is yield, which is typically measured by how many functional devices can be produced per substrate in a device manufacturer or a device manufacturing process. A variety of methods can be employed to improve yield. One such method attempts to make the production of a device (e.g., imaging a portion of a design layout onto a substrate using a lithography apparatus such as a scanner) more tolerant to perturbations of at least one processing parameter during the processing of the substrate (e.g., during imaging a portion of a design layout onto a substrate using a lithography apparatus). The concept of the overlay process window (OPW) is a useful tool for this method. The production of a device (e.g., an IC) can include other steps such as substrate measurement, loading or unloading of the substrate, loading or unloading of a patterned load, positioning a die under a projection optics before exposure, stepping from one die to another, etc., before, after, or during imaging. Additionally, various patterns on a patterning device can have different process windows (i.e., the space of processing parameters under which a pattern will be produced according to specifications). Examples of pattern specifications related to potential system defects include inspection necking, line pullback, line thinning, CD, edge placement, overlay, anti-top loss, anti-punchthrough, and / or bridging. The process window for all or some of the patterns (usually patterns within a particular region) on a patterning device can be obtained by combining (e.g., overlaying) the process windows of each individual pattern. Thus, the process window for these patterns is called the overlay process window. The boundaries of the OPW can include the boundaries of the process windows of some individual patterns. In other words, these individual patterns limit the OPW. These individual patterns can be referred to interchangeably herein as "hotspots", "critical features", or "process window limiting patterns (PWLP)". When controlling the lithography process, it is possible and often economical to focus on hotspots. When there are no defects in the hotspots, it is very likely that all patterns are defect-free. If the processing parameter value is outside the OPW, imaging becomes more tolerant to perturbations when the processing parameter value is closer to the OPW, or if the processing parameter value is inside the OPW, imaging becomes more tolerant to perturbations when the processing parameter value is away from the boundaries of the OPW.

[0044] Figure 3An exemplary source of the process parameter 350 is shown. One source can be data 310 of a processing apparatus, such as parameters of a source of a lithographic apparatus, a track, etc., a projection optical device, a substrate table, etc. Another source can be data 320 from various substrate metrology tools, such as a substrate height map, a focus map, a critical dimension uniformity (CDU) map, etc. The data 320 can be obtained before the applicable substrate undergoes a step (e.g., development), preventing rework of the substrate. Another source can be data 330 from one or more patterning device metrology tools, a patterning device CDU map, a change in a patterning device (e.g., mask) film stack parameter, etc. Yet another source can be data 340 from an operator of the processing apparatus.

[0045] Certain overlay components (or other parameters of interest) on each substrate will be truly random in nature. However, whether their cause is known or not, other components are systematic in nature. In the case where similar substrates experience similar overlay error patterns, the error pattern can be referred to as the “fingerprint” of the lithography process. Overlay errors can generally be divided into two different groups: 1) Contributions that vary across the entire substrate are referred to in the art as inter-field fingerprints.

[0046] 2) Contributions that vary similarly across each target portion (field) of the substrate are referred to in the art as intra-field fingerprints.

[0047] The control of the lithography process is typically based on feedback or feedforward measurements, and then models such as inter-field (across-substrate fingerprint) or intra-field (across-field fingerprint) models are used for modeling. U.S. Patent Application 20180292761 describes a control method for controlling performance parameters such as overlay at the sub-field level using an advanced correction model, which is incorporated herein by reference. Another control method using sub-field control is described in European Patent Application EP3343294A1, which is also incorporated herein by reference.

[0048] However, although advanced correction models can include, for example, 20 - 30 parameters, the currently used lithography apparatuses (for the sake of brevity, the term “scanner” will be used throughout the specification) may not have actuators corresponding to one or more parameters. Therefore, only a subset of the entire parameter set of the model can be used at any given time. In addition, since advanced models require many measurements, it is not desirable to use these models in all cases because the time required to perform the necessary measurements reduces the production throughput.

[0049] Some of the main contributions to overlay errors include, but are not limited to, the following:

[0050] Scanner-specific errors: These errors can come from various subsystems of the scanner used during the exposure of the substrate, actually generating a scanner-specific fingerprint;

[0051] Wafer deformation caused by processing: Various processes performed on a substrate may cause the substrate or wafer to deform;

[0052] Illumination setting differences: This is caused by the settings of the illumination system, such as the shape of the aperture, lens launcher positioning, etc.;

[0053] Heating effects - The heating-induced effects are different between different sub-fields of the substrate, especially for substrates where different sub-fields include different types of components or structures;

[0054] Mask writing errors: Due to manufacturing limitations, errors may already exist in the patterning device; and

[0055] Topography variations: The substrate may have topography (height) variations, especially around the wafer edge.

[0056] It is possible to perform modeling of the registration errors of the individual sub-fields of a field (e.g., at the die level or other functional area levels), rather than modeling the registration error of the entire field, or in addition to modeling the entire field. Although the latter requires more processing time, since both the field and the sub-fields within it are modeled, it allows for the correction of error sources related to only specific sub-fields as well as error sources related to the entire field. Of course, other combinations are also possible, such as modeling the entire field and only certain sub-fields.

[0057] Even when the errors are fully modeled, there are difficulties in the resulting correction excitation. Some corrections cannot be effectively performed using the available control parameters (control knobs). In addition, although other corrections may be initiated, doing so may actually lead to adverse side effects. Essentially, due to dynamic and control limitations as well as sensitivities, the actual operation of the scanner to implement corrections is limited.

[0058] Figure 4Shows a specific example of in-field overlay fingerprint that is difficult to initiate correction. It shows the pattern of overlay OV (y-axis) opposite to the X (or Y) direction. Each intersection point represents the measured overlay value, and each point is the necessary corresponding compensation correction. The fitted line is the (near-ideal) correction distribution, which is fitted to the correction (points). The sawtooth pattern shown in the overlay fingerprint is obvious; each section through which the overlay passes changes substantially linearly, where X is a single die (this figure represents the overlay measurement on 4 dies). The correction distribution follows (and thus compensates for) the overlay fingerprint. Such a fingerprint is considered to be the result of large stress caused by, for example, large stacks used in 3D-NAND or DRAM processes. This stress manifests itself both at the wafer level (resulting in severe wafer warping) and at the die level. At the die level, the overlay fingerprint includes magnification within each die. Since there are multiple dies in the exposure field, the resulting in-field overlay fingerprint shows the sawtooth pattern shown (usually on the scale of dozens of nanometers). Depending on the orientation of the device, the pattern can be slit-based or scan-based. Regardless of the orientation, the available models and initiators cannot be used to correct the overlay. Specifically, it is impossible to initiate the correction of such an extreme pattern only within the scanner.

[0059] Although the embodiments herein will be specifically described in terms of overlay or edge placement error (EPE) that manifests as a sawtooth pattern or fingerprint (e.g., caused by in-die stress in 3D-NAND or DRAM processes, as Figure 4 shown), it should be understood that it can be used to correct any other higher-order overlay, EPE, or focus fingerprint.

[0060] To optimally correct the overlay fingerprint as Figure 4 shown, it is important to be able to adjust the scanner at a spatial scale smaller than the pitch of the periodic distribution, e.g., smaller than Figure 4 one "tooth" of the repeating sawtooth distribution. Such a single-tooth region is typically associated with the cell structure within a single die. Therefore, the interface with the scanner should allow defining separately controllable regions within the exposure field. This concept is called the sub-field control interface; an example of it is disclosed in the aforementioned European patent application EP3343294A1. For example, the control distribution of the wafer stage of the scanner configured for the first cell die / cell structure can be defined largely independently of the control distribution of the second cell / die structure positioned further along the scan direction. The sub-field control infrastructure allows for more optimized correction of repeating overlay (or focus) variations at the sub-field resolution. In addition, the ability to independently control different sub-field regions allows reducing the die-to-die or cell-to-cell variations of overlay / focus fingerprints within the chip and / or within the cell.

[0061] Typically, scanner overlay control uses dynamic stage position control to adjust the placement of structures (features) such that the overlay error is minimized. In principle, this can be achieved by pre-correcting for the expected overlay error fingerprint (e.g., due to stress accumulation from applying subsequent layers) and / or by adjusting the placement of features within subsequent layers to be sufficiently aligned with features in the previous layer(s).

[0062] Such scanner control can be used in combination with other techniques such as reticle feature correction offsets. Ideally, the shift would be exactly opposite to the error shift being corrected, e.g., the feature shift due to deformation caused by stress after applying a subsequent layer. The effect is that using such a reticle will leave much less amount to be corrected by the scanner overlay correction infrastructure. However, the correction via the reticle must be static and cannot address any variations in the overlay fingerprint (e.g., field-to-field, wafer-to-wafer, and / or lot-to-lot variations). Such variations can be of the same order of magnitude as the fingerprint itself. In addition, there are startup and sensitivity limitations in the reticle write correction inherent in the write tool (e.g., electron beam tool or similar tool) used for the control.

[0063] Scanner overlay correction is typically applied by the stage controller of the projection lens and / or the lens manipulator (odd aberration control can be used to control the placement of features). However, as previously mentioned, the scanner cannot fully follow any desired overlay correction distribution. One reason is due to the limitations on the achievable speed and acceleration of the wafer (and reticle) stage. Another reason is that the scanner exposes the substrate with a relatively large illumination spot (the so-called slit length represents the size of the spot in the scan direction, reference: EP application EP19150960.3, the entire content of which is incorporated herein by reference). The extension of the spot means that in cases where the desired overlay correction is not just a simple shift across the entire die / unit, during the scan exposure, some portion of the features within the die / unit will always be sub-optimally positioned. This variation in the effective position (overlay) correction during the scan operation effectively results in blurring of the aerial image of the features, which in turn leads to a loss of contrast. This dynamic effect is commonly referred to as the moving standard deviation (MSD). The limitations on stage positioning are typically associated with the average position (overlay) error and are commonly referred to as the moving average (MA) error.

[0064] More specifically, the moving average (MA) error and moving standard deviation (MSD) of errors of a lithography platform relate to a critical time window that includes the time interval during which each point on the die is exposed (in other words: receives photons). If the average position error of a point on the die is high during this time interval (in other words: high MA error), the effect is a shift in the exposed image, resulting in a registration error. If the standard deviation of the position error is high during this time interval (in other words: high MSD error), the image may be smeared, resulting in a fading error.

[0065] Both the average registration error (MA) and the contrast loss caused by MSD are contributors to the overall edge placement error (EPE) budget and thus need to be carefully balanced when determining the specific control distribution for the wafer and / or mask platform; typically, a more targeted control method for MA will result in a higher MSD impact, while an MSD-targeted control method may lead to unacceptably large MA errors. EPE is a combined error resulting from global critical dimension uniformity (CDU), local CDU (e.g., line edge roughness LER / line width roughness LWR), and registration error. It is these parameters that have the greatest impact on yield, as errors in these parameters affect the relative positions of features, and whether any two features inadvertently touch or fail to touch inadvertently.

[0066] A variety of methods for improving sub-field control to correct in-field fingerprints will now be described. First, an optimized method for improving in-field correction of an edge field (or other layout) will be described, the edge field including a partial die or having a pattern with non-uniform in-die stress within a slit. Tooling (slit / start range) limits the correction ability, meaning that the correction of certain dies will not start correctly.

[0067] For example, the optimization may include in-field "in-spec sub-field" optimization, such as in-field "in-spec die" or "in-spec sub-die" optimization, the latter describing where the die can be further divided into sub-die regions, each sub-die region defined by a different functional region. The functional regions can be defined and differentiated according to their intended functions (e.g., memory, logic, scribe channels, etc.), as these functional regions can have different process control requirements (e.g., process window and optimal parameter values). Another example of "in-spec sub-die" optimization is when the die is exposed in multiple exposures (e.g., stitched die).

[0068] Such in-field “in-spec sub-field” optimization aims to maximize the number of die or sub-die within the field that are within spec and can thus potentially result in functional devices, rather than applying an average optimization (e.g., least squares minimization) across the entire field. Examples and methods for individual sub-field (e.g., die or sub-die) optimization and control are disclosed in the aforementioned European Patent Application EP3343294A1 and US20180292761. EP3343294A1 discloses various methods for initiating corrections based on parameters of interest. These include tilting the mask platform and / or the wafer platform relative to each other. Focus variation curvature can be introduced via the projection lens optics (e.g., lens manipulator) and (in the scan direction) by varying the relative tilt of the mask platform relative to the wafer platform during exposure (in either direction, i.e., including across the exposure slit). These and other methods will be obvious to the skilled person and will not be discussed further.

[0069] Specifically, US20180292761 discloses separately modeling sub-fields to determine individual sub-field corrections. In an embodiment, the in-field in-spec sub-field optimization described herein can include co-optimization of in-field die within the in-field and (multiple) sub-field models.

[0070] When optimizing a parameter of interest, in-field, in-spec sub-field (e.g., in-spec die) optimization can use prior knowledge of the product (die layout) and / or measurements of in-field stress or stress within the die. Least squares optimization typically treats each location within the sub-field equally, regardless of the field / die layout. Thus, least squares optimization may prefer a correction for “only” two out-of-spec locations, but each correction is in a different sub-field / die, rather than a correction for four out-of-spec locations, but only affecting one sub-field / die. However, since a single defect often results in a defective chip, maximizing the number of defect-free die (i.e., in-spec die) is ultimately more important than simply minimizing the number of defects per field. It should be understood that in-spec die optimization can include maximum absolute value (max abs) optimization for each chip. Such max abs optimization can minimize the maximum deviation of the performance parameter from the control target.

[0071] In-field specification subfield optimization can determine the best subfield control trajectory that maximizes the number of dies within the specification based on the in-die stress and / or startup capabilities of the scanner. Due to the correction capabilities within the scanner, edge dies and / or dies with non-uniform (or asymmetric) stress are often difficult to correct. Because of this, the optimization can allow sacrificing such dies (e.g., allowing them to have a large number of defects), or otherwise weighting them, or giving them less consideration / importance. This can be achieved in a variety of ways, e.g., by giving such dies a large process window (e.g., close to or even larger than the viable process window), or otherwise weighting the parameters related to these dies in the optimization. The decision to sacrifice a die or give it a lower weight can be made based on the die and / or field position on the substrate (e.g., the position of the fingerprint within the die expected to be particularly difficult, such as at the substrate edge), the expected, estimated, or measured in-die stress fingerprint (e.g., estimated from scanner metrology such as leveling data and the corresponding in-die topography - such as by using the methods described later). Of course, even without such a weighting strategy, the maximum abs optimization tends to correct dies with uniform in-die stress and that are easier to correct.

[0072] The correction capabilities across the width slit are particularly limited. Thus, currently a single value can be selected for one or more parameters (e.g., overlay, MA, or MSD) that minimizes the error across the slit (e.g., least squares minimization), and thus this single value is applied to all subfields / dies across the slit. This is not a problem for some fields, but for other fields, such as those close to the substrate edge (including edge dies) and / or those fields that include dies showing significant non-uniform in-die stress, there may not be a correction available that will produce all dies across the slit / in the field. More specifically, the present optimization scheme can set a single threshold for the parameter of interest (e.g., MSD) and constrain any subfield or die not to exceed that threshold. However, in some cases, it may be better to allow exceeding the threshold for one subfield if the in-specification die metric is improved. This may be the case if the startup potential is not sufficient to perform the correction determined to keep all subfields below the threshold, and / or if the subfield is relatively unimportant (e.g., an edge die or a die with non-uniform stress and thus unlikely to be produced anyway).

[0073] In another embodiment, in-field or in-die co-optimized corrections for at least two control plans are proposed. The control plans can relate to, for example, different tools used when forming structures or integrated circuits on a substrate. In an embodiment, one of the tools can be a scanner (corrections in the scanner control plan). For example, other tools can include one or more of an etcher (etch control plan), a bake tool (bake control plan, e.g., where the parameter can be the bake time), a develop tool (develop control plan), and a coating or deposition tool (deposition control plan, e.g., where the parameter can be the resist thickness or even the material used).

[0074] In-field in-die stress and / or sub-field patterns are largely due to process behavior. For example, controlling process tools will affect how in-die stress builds up on the substrate. By combining scanner corrections with process tool parameter adjustments, better control of the fingerprints generated by such in-die stress can be achieved. In particular, it has been observed that the sub-field correction potential of the current sub-field model tends to be non-linear. Combining this with the non-linear correction potential of one or more process tools can provide a greater correction space and more optimized corrections.

[0075] Sub-field control co-optimization can be, for example, one or more of overlay, MA, and MSD. It can be in-die or sub-field optimization within the specifications as described above (i.e., these embodiments can be combined and complementary). The optimization can take into account production volume and the time to perform a specific correction. Specifically, some etch corrections, while beneficial in terms of overlay or other parameters, may take a long time to initiate. Thus, joint optimization can balance production volume with the parameter of interest, or the decision can be to apply only such longer-duration corrections to critical areas or "hot spots". Different regions (sub-fields or sub-dies) can be assigned different weightings between quality (e.g., overlay, MSD, EPE, or other quality parameters of interest) and production volume / time to perform correction actions. Such weighting or balancing can depend on, for example, the criticality or the "in-specification sub-field" of the corresponding process window.

[0076] In addition, in-field and / or in-die fingerprints can be decomposed into group fingerprints, which can then be linked to context (context data), for example. Context data can describe the processing history of a particular substrate; for example, which processing steps have been applied, which one or more individual devices have been used in the execution of these steps (e.g., which etch chamber and / or deposition tool has been used; and / or which scanner and / or chuck has been used to expose the previous layer), and / or which parameter settings have been applied by those one or more devices during the processing steps (e.g., the setting of temperature or pressure within an etch recipe, or parameters such as illumination mode, alignment recipe, etc. in a scanner). In-die and in-field stress, as well as associated sub-field and in-field fingerprints (e.g., overlay fingerprints), highly depend on such context. Therefore, it is possible to predict such stress (and thus make appropriate corrections) based on the context. This can be achieved, for example, by establishing a database or a machine learning network that links such in-field or in-die fingerprints (e.g., overlay fingerprints) to context data. For example, such a library can be built based on a large amount of metrology data with known context.

[0077] Specifically, such techniques can include monitoring the run-to-run residuals of in-field or in-die fingerprints, for example, using special reticles that are very densely populated with targets and / or via metrology techniques within the die (measurements on in-die targets), and / or leveling / wafershape data. Then, these shapes / fingerprints can be separated by any suitable means (e.g., according to suitable KPIs and / or by component analysis techniques).

[0078] In run-to-run (commonly abbreviated as run2run) control, fingerprints (e.g., overlay fingerprints) are estimated based on a set of substrates (e.g., wafers) measured per batch. One or more measurement fields from these substrates are adapted to the fingerprint, which is then typically mixed with an earlier fingerprint to create a new fingerprint estimate using an exponentially weighted moving average (EWMA) filter. Alternatively, the fingerprint can simply be updated periodically, or even measured once and kept unchanged. Combinations of some or all of these methods are also possible. Then, the results of this calculation are used to optimize the job run in order to set one or more scanner starters and / or other tool starters / settings for the next batch to reduce or minimize overlay.

[0079] The co - optimization of scanner parameters and one or more processing tool parameters can include the optimization of MA or MSD or a combination of MA / MSD associated with the scanner correction distribution relative to a suitable performance parameter (e.g., overlay or expected EPE error of one or more critical features within a sub - field / die). In such an embodiment, the method can include identifying one or more critical features within a sub - field and performing co - optimization to find co - optimization settings for at least two different tools that minimize the expected overlay, MSD, and / or EPE of the (multiple) critical features, and / or using the expected overlay, MSD, and / or EPE of the (multiple) critical features as terms in a merit function.

[0080] In another embodiment, a physical and / or empirical through - stack model is presented that describes how a parameter of interest (e.g., overlay or EPE) propagates through the stack (e.g., from layer to layer). This can include predicting / estimating the overlay through the stack at the sub - field level, considering that the stress fingerprint within the die will be affected by multiple different process fingerprints (e.g., those involved in deposition and / or etching processes).

[0081] Such a through - stack model has many advantages. The physical / empirical model will provide insights into the overlay. For example, a sub - field correction model can calculate the residuals after using sub - field correction. Further knowledge of sub - field correction can be incorporated back into the through - stack model to better optimize the stack design.

[0082] Modifying the product and / or changing the process will have an impact on the in - field and in - die (sub - field) fingerprints. Current methods include optimizing the process or product and then correcting via appropriate sub - field correction, which is a short - term and expensive solution. Experimental iterations are costly and time - consuming, while maximizing processing time / effort is operationally expensive. Balancing lithography and process effects via such a through - stack model can accelerate R & D.

[0083] Such a through - stack model can be used to help achieve the two optimization embodiments described herein (in - spec die optimization and / or multi - tool co - optimization). The ability to predict the overlay through the stack (especially that caused by stress within the die) provides potentially better in - spec die or yield loss prediction. Additionally, this model - based estimation of the overlay through the stack better enables the construction of a fingerprint database to provide appropriate correction.

[0084] Also proposed is to optimize the control strategy based on a sensitivity metric that describes the sensitivity of a particular correction to the input / measurement data used to determine the correction and / or layout of the device being exposed; for example, the sensitivity of a control distribution to the quality of the measurement data (e.g., overlay data) used to determine that control distribution. Sub-field correction can be based on parameter and / or decay optimization, where key parameters such as MSD, correction distribution, and wafer stage / reticle stage jitter affect the overall performance of sub-field optimization.

[0085] For example, such a sensitivity metric can be used to determine and / or quantify precision; for example, the sensitivity metric can include a precision metric for potential start inputs (e.g., quantifying the possible precision of a potential start). For example, in cases where the input data / measurement data used to determine a potential start input is unreliable (e.g., due to noise) and / or the start potential is limited and cannot correctly start the potential start input, the precision metric can indicate a lower precision. Understanding the sensitivity and variations in one or more scanner parameters (e.g., KPIs) can improve process monitoring / control and more accurate fingerprint determination, resulting in better scanner start-up and improved overlay, thus increasing yield. For example, different control strategies can be selected based on the sensitivity or precision metric.

[0086] More specifically, control strategy optimization can optimize, for example, scanner-reticle co-optimization control distribution, control loop time filtering, and / or control loop weighting. As an example, if the measurement data is known to be noisy, different scanner-reticle co-optimization can be used compared to when the measurement data noise is small. Scanner-reticle co-optimization is described in European Patent Application No. EP 19177106.2, which is incorporated herein by reference and describes the co-optimization of correction strategies for both the reticle formation process and the scanner exposure process to determine an optimized reticle correction such that the co-optimized scanner correction is corrected to be simpler in the scan direction to initiate the overlay error distribution. Co-optimization can also consider the capabilities and / or sensitivities of the reticle writing tool to better optimize the reticle correction. Such co-optimization can include, for example, solving an iterative algorithm that optimizes (e.g., minimizes) the value of a performance parameter (e.g., overlay or EPE) based on the sub-distributions of the scanner and the reticle writing tool.

[0087] In addition, when a relatively "noise-tolerant" control strategy is selected, a sparser and / or simpler measurement strategy can be used. This enables controlling the sensitivity by controlling the measurement (e.g., by measuring more or fewer points). The sparser measurement data can also include scanner measurement data (combined to supplement or replace other measurement data), such as leveling measurement data.

[0088] In another embodiment, a control strategy or control recipe may be derived and / or selected based on a library of sparse (more specifically scanner) metrology data and intra-field or intra-sub-field (intra-die) fingerprints (or associated control recipes). This may significantly reduce the high computational effort involved in determining a control recipe for each process (e.g., each wafer). A database of intra-field (and / or intra-sub-field) fingerprints and / or associated corrections may be created for a particular field geometry based on, for example, training data associated with the associated MSD and sub-field correction parameters. Such a database may be used, for example, to determine a fast and relatively accurate correction profile for scanner activation based on (e.g., inline) scanner measurements. In contrast, currently activation profiles for intra-die stress-inducing fingerprints need to be generated by external tooling before the corrections are sent to the scanner.

[0089] For example, while all wafers have intra-die stress, it is difficult to understand how the stress fingerprint evolves from wafer to wafer because it is not possible to perform external measurements on all wafers. Currently, extensive measurements are performed to measure the intra-field, intra-sub-field or intra-die fingerprint resulting from such intra-die stress on a subset of wafers and to determine a correction that is merged with the leveling measurements for a specific wafer and used to determine the correction. Here, it is proposed to use the leveling data to estimate the fingerprint due to intra-die stress and / or the corresponding correction.

[0090] In this way, the training data may include non-scanner or external metrology data (e.g., fingerprint data including intra-field and / or intra-sub-field fingerprints, such as overlay fingerprint data measured using a dedicated metrology tool, etc.) and corresponding scanner metrology data (e.g., leveling data), and a suitable solver (e.g., higher order, such as third order, equation or even machine learning algorithm or network (e.g., neural network)) is trained to learn the correlation between the non-scanner / external metrology data and the scanner metrology data. Using such a database, the intra-field or intra-sub-field fingerprint and / or its appropriate correction can be determined based on the scanner metrology data, thereby achieving inline correction for the fingerprint (e.g., caused at least in part by intra-die stress). However, it should also be understood that such a database or trained solver can be used in a feedback control loop or monitoring tool (e.g., to flag particularly high stress distributions and therefore flag possible out-of-specification tooling).

[0091] Such a database that links scanner measurements to in-field fingerprints (such as those resulting from in-die stresses) can be used (or combined and trained) in conjunction with the aforementioned database that links context to in-field fingerprints. Thus, in-field fingerprints (e.g., resulting from in-die stresses) can be determined (e.g., inline) based on both context and scanner measurements.

[0092] In addition, the sensitivity metric can be used in relation to current product performance (e.g., CD ratio / lithography margin) to identify variations and offsets (e.g., connecting input data to the product via the sensitivity metric).

[0093] The sensitivity metric can also be used as an input to time filtering methods and APC control; for example, the weighting can be adjusted based on the sensitivity of the start distribution to user preferences and input data or based on the noise level of the data.

[0094] Figure 5 is a flowchart showing an exemplary arrangement combining many of the above concepts. The training phase TP uses external metrology data DAT MET and corresponding scanner metrology data DAT SCAN . The external metrology data DAT MET can include, for example, fingerprint data such as in-field fingerprints and / or optionally sub-field or die-level fingerprints (all mentions of in-field fingerprints should be understood to include the possibility of smaller-scale sub-field fingerprints). For example, such in-field fingerprints can be in the form of one or more of overlay data, in-die metrology data, scanning electron microscope data. For example, the scanner metrology data DAT SCAN can include one or more leveling data, such as leveling MA error, height map data, continuous wafer map.

[0095] In the training phase TP, the external metrology data DAT MET and corresponding scanner metrology data DAT SCAN can be used to build a fingerprint database FPDB, which includes, for example, the fingerprint data linked to the corresponding scanner measurement data DAT SCAN (e.g., derived from the metrology data DAT MET and can include in-field fingerprints generated by die-level stress). This can be done by training a suitable solver as described above. The fingerprint database FPDB can also include appropriate corrections and / or correction recipes for each in-field fingerprint.

[0096] In the production phase PP, the scanner metrology data DAT from the scanner SCAN SCAN is combined with the fingerprint database FPDB built in the training phase to infer in-field fingerprints as part of the optimization step OPT. External metrology data DAT from the metrology tool DAT MET can be used to support and / or validate the inference. Since this metrology data DAT MET is only used or mainly used to validate via scanner metrology DAT SCANInferred in-field (e.g., stress) fingerprints rather than actual in-field fingerprint determination, so it can be significantly sparser than many existing metrology strategies (fewer measurements, e.g., at fewer locations and / or using fewer wafers). Alternatively or additionally, metrology data can be directed, for example, based on the determined in-field / within-die fingerprints. For example, measurements can be targeted at regions or locations where the fingerprints show particularly large errors or residuals, which indicate particularly high stress within the die (e.g., compared to the rest of the die).

[0097] The optimization step OPT can also include determining a sensitivity metric, e.g., determining the sensitivity of a parameter of interest (e.g., a KPI), and using this sensitivity metric to optimize the correction. Determining the sensitivity metric can use any method described herein.

[0098] As described above, the optimization step OPT can be a co-optimization for controlling the scanner SCAN and another tool (e.g., the etcher ETCH).

[0099] As described above, the optimization step OPT can be within-specification die or within-specification subfield optimization.

[0100] As described above, the optimization step OPT can use a through-stack model to consider the influence of previous layers during optimization.

[0101] Thus, the output OUT can include one or more of the following:

[0102] · Estimates of in-field and / or within-subfield / within-die fingerprints, such as fingerprints at least partially generated by stress within the die, without direct measurement (e.g., per wafer) - this can be verified by (e.g., limited or sparse) metrology;

[0103] · Optimized metrology schemes (e.g., sampling schemes) using sparse and / or directed measurements;

[0104] · Optimized correction, e.g., using in-field and / or within-die stress fingerprints to reduce lead times and metrology costs;

[0105] · Evolution data tracking the evolution of within-die fingerprints over time / field / wafer / lot.

[0106] Thus, such an arrangement implements a within-die fingerprint (e.g., due to stress) monitoring feature per wafer, the results of which (and the evolution of the fingerprints over time / field / wafer / lot) can be used to further fine-tune process control. The arrangement also provides more efficient metrology, reduces the performance of unnecessary metrology, and also provides guidance for metrology to more severely stressed areas of interest within the die. In addition, the arrangement facilitates monitoring of the applied scanner correction to obtain in-field stress fingerprints; e.g., to monitor how well the applied drive is performing in terms of product performance.

[0107] Using such a database, in-field fingerprints can be determined based on scanner measurement data and / or appropriately corrected, thereby enabling in-line correction of die stress.

[0108] The following numbered clauses include concepts disclosed herein, each of which can be implemented as a computer program and / or implemented within a suitably configured lithographic apparatus:

[0109] 1. A method for determining in-field correction for sub-field control of a lithographic process for exposing a pattern on an exposure field of a substrate, the exposure field including a plurality of sub-fields, the method including performing an optimization to determine the in-field correction, the optimization being such that it maximizes the number of sub-fields within specifications.

[0110] 2. The method according to clause 1, wherein performing the optimization includes weighting and / or sacrificing one or more sub-fields that are considered to have a higher likelihood of being non-functional.

[0111] 3. The method according to clause 2, wherein the decision to weight and / or sacrifice one or more sub-fields is based on prior knowledge of the product being exposed.

[0112] 4. The method according to clause 2 or 3, wherein the decision to weight and / or sacrifice one or more sub-fields is based on a measurement of in-field stress.

[0113] 5. The method according to clause 4, wherein sub-fields showing a higher level of non-uniformity in the stress are more likely to be weighted and / or sacrificed.

[0114] 6. The method according to clause 5, wherein a higher level of non-uniformity is determined based on whether the stress uniformity of the die is higher than a stress uniformity threshold.

[0115] 7. The method according to any one of clauses 2 to 6, wherein the decision to weight and / or sacrifice one or more sub-fields is based on the position of the field and / or sub-field on the substrate.

[0116] 8. The method according to clause 7, wherein sub-fields at or near the edge of the substrate are more likely to be weighted and / or sacrificed.

[0117] 9. The method according to any of the foregoing clauses, wherein the optimization includes maximum absolute value optimization per sub-field.

[0118] 10. The method according to any of the foregoing clauses, wherein the optimization determines an optimal sub-field control trajectory that maximizes the number of sub-fields within specifications.

[0119] 11. A method as described in any of the preceding clauses, wherein the optimization takes into account the start-up capabilities of a lithographic apparatus used to perform a lithographic process.

[0120] 12. A method as described in any of the preceding clauses, wherein each sub-field comprises a single die or a part of a single die.

[0121] 13. A method as described in any of the preceding clauses, wherein said determining in-field correction comprises at least partially correcting in-sub-field and / or in-field fingerprints associated with stress patterns in the sub-field or in the field.

[0122] 14. A method for determining in-field correction for sub-field control of a manufacturing process, the manufacturing process comprising a lithographic process for exposing a pattern on an exposure field of a substrate, the exposure field comprising a plurality of sub-fields, the manufacturing process comprising at least one additional processing step, the method comprising:

[0123] - Performing an optimization to determine the in-field correction, the optimization comprising a co-optimization based on at least one lithographic parameter related to the lithographic process and at least one processing parameter related to at least one additional processing step.

[0124] 15. The method according to clause 14, wherein the at least one lithographic parameter relates to the control of a lithographic apparatus used to perform the lithographic process, and the at least one processing parameter relates to the control of at least one processing apparatus used to perform the at least one additional processing step.

[0125] 16. The method according to clause 15, wherein the at least one processing apparatus comprises one or more of an etching apparatus or etching apparatus chamber, a deposition apparatus, a baking apparatus, a developing apparatus, and a coating apparatus.

[0126] 17. The method according to any one of clauses 14 to 16, wherein the optimization is with respect to one or more of edge placement error, overlay, moving average error, and moving standard deviation error.

[0127] 18. The method according to any one of clauses 14 to 16, wherein the optimization is with respect to maximizing the number of sub-fields within specifications.

[0128] 19. The method according to clause 18, wherein the optimization comprises performing the method of any one of clauses 1 to 13.

[0129] 20. The method according to any one of clauses 14 to 19, wherein the optimization comprises a balance between throughput and quality.

[0130] 21. The method according to clause 20, wherein the balance between throughput and quality is weighted differently for different sub-fields.

[0131] 22. The method according to any one of clauses 14 to 21, wherein said determining in-field correction includes at least partially correcting in-subfield and / or in-field fingerprints associated with stress patterns within a subfield or in the field; and the method includes:

[0132] - predicting in-subfield and / or in-field fingerprints based on context data describing the processing context of the substrate; and

[0133] - wherein said determining in-field correction includes determining the correction based on the predicted in-subfield and / or in-field fingerprints.

[0134] 23. The method according to clause 22, wherein said step of determining the correction based on the predicted in-subfield and / or in-field fingerprints includes: referring to a library of said context data that links sets of fingerprints to a plurality of substrates.

[0135] 24. The method according to clause 23, wherein the method further includes the following initial steps:

[0136] - obtaining fingerprint data describing the in-subfield and / or in-field fingerprints of a plurality of substrates and corresponding context data describing the processing history of each substrate;

[0137] - decomposing the in-field and / or in-subfield fingerprints into sets of fingerprints; and

[0138] - compiling the library that links the sets of fingerprints to the context data.

[0139] 25. A method for determining in-field correction for subfield control of a lithography process for exposing a pattern on an exposure field of a substrate in forming a plurality of stacked layers, the exposure field including a plurality of subfields, the method including:

[0140] - constructing a physical and / or empirical through-stack model that describes how an interested parameter propagates through the stack layer by layer.

[0141] 26. The method according to clause 25, including using the model to estimate the evolution of the interested parameter through the stack at the subfield level.

[0142] 27. The method according to clause 25 or 26, including using the model to calculate the residual error after initiating in-field correction.

[0143] 28. The method according to any one of clauses 25 to 27, including using the through-stack model in the method of clause 24 when compiling the library.

[0144] 29. The method according to any one of clauses 25 to 27, comprising using the direct stack model to predict the value of a parameter of interest; and using the predicted value in the optimization step in the method according to any one of clauses 1 to 13.

[0145] 30. A method for determining in-field correction for sub-field control of a lithography process for exposing a pattern on an exposure field of a substrate, the exposure field comprising a plurality of sub-fields, the method comprising: determining a sensitivity metric that describes the sensitivity of the correction to input data for determining the correction and / or layout of the pattern; and determining the in-field correction for sub-field control based on the sensitivity metric.

[0146] 31. The method according to clause 30, wherein the sensitivity metric describes the precision of the potential activation input.

[0147] 32. The method according to clause 31, wherein the sensitivity metric indicates a lower precision in cases where the input data is unreliable and / or where the activation potential is limited and cannot correctly activate the potential activation.

[0148] 33. The method according to any one of clauses 30 to 32, wherein the step of determining the in-field correction comprises optimizing one or more of the following: scanner-mask co-optimization control distribution, control loop time filtering, and / or control loop weighting.

[0149] 34. The method according to any one of clauses 30 to 33, further comprising selecting a control strategy from a control strategy library based on lithography tool measurement data using the sensitivity metric.

[0150] 35. The method according to any one of clauses 30 to 33, further comprising selecting a control strategy using the sensitivity metric based on lithography tool measurement data using a trained solver.

[0151] 36. The method according to clause 35, comprising: obtaining training data from a plurality of substrates, including non-lithography tool measurement data and corresponding lithography tool measurement data; and training the solver to link the non-lithography tool measurement data and the lithography tool measurement data.

[0152] 37. The method according to any one of clauses 34 to 36, wherein the lithography tool measurement data includes leveling data.

[0153] 38. The method according to any one of clauses 30 to 37, comprising determining an estimate of the die stress based on the leveling data; and determining a correction based on the estimated die stress.

[0154] 39. The method according to clause 38, wherein the steps of determining the estimation and determining the correction are performed for each die based on the leveling data from each substrate.

[0155] 40. A method for determining in-field correction for sub-field control of a lithography process for exposing a pattern on an exposure field of a substrate, the exposure field including a plurality of sub-fields, the method comprising:

[0156] Obtaining a database including in-field fingerprint data linked to historical lithography tool metrology data; determining an estimate of the in-field fingerprint based on the lithography tool metrology data and the database; and determining an in-field correction of the lithography process based on the estimated in-field fingerprint.

[0157] 41. The method according to clause 40, wherein the in-field fingerprint data includes in-field fingerprints related to stress patterns within each field.

[0158] 42. The method according to clause 40 or 41, wherein the in-field fingerprint data includes sub-field fingerprints related to stress patterns within each sub-field.

[0159] 43. The method according to any one of clauses 39 to 42, including obtaining external metrology data from an earlier substrate; and

[0160] Verifying the in-field correction based on the external metrology data.

[0161] 44. The method according to clause 43, wherein the external metrology data is sparser than the data directly required to determine the in-field correction.

[0162] 45. The method according to clause 43 or 44, including using the estimate of the in-field fingerprint to determine a metrology strategy for the external metrology.

[0163] 46. The method according to clause 45, wherein the determining the metrology strategy includes determining a sampling scheme for the external metrology.

[0164] 47. The method according to any one of clauses 39 to 46, including monitoring the relationship between the estimate of the in-field fingerprint and the in-field correction.

[0165] 48. The method according to any one of clauses 40 to 47, wherein the determining the in-field correction includes performing an optimization on at least one parameter of interest.

[0166] 49. The method according to clause 48, wherein the optimization maximizes the number of sub-fields within the specifications.

[0167] 50. The method according to clause 49, wherein the optimization includes maximum absolute value optimization per sub-field.

[0168] 51. The method according to clause 49 or 50, wherein the performing of the optimization includes weighting and / or sacrificing one or more sub-fields that are considered to have a relatively high likelihood of being non-functional.

[0169] 52. The method according to clause 51, wherein the decision to weight and / or sacrifice one or more sub-fields is based on prior knowledge of the product being exposed.

[0170] 53. The method according to clause 51 or 52, wherein the decision to weight and / or sacrifice one or more sub-fields is based on the said estimation of the in-field fingerprint.

[0171] 54. The method according to clause 53, wherein, in the case where the said estimation of the in-field fingerprint indicates that one or more non-uniform sub-fields exhibit a relatively high level of non-uniformity with respect to the in-sub-field stress, these non-uniform sub-fields are weighted and / or sacrificed.

[0172] 55. The method according to clause 54, wherein the determination of the relatively high level of non-uniformity is based on the determination of whether the in-sub-field stress uniformity of the sub-field is higher than a stress uniformity threshold.

[0173] 56. The method according to any one of clauses 51 to 55, wherein the decision to weight and / or sacrifice one or more sub-fields is based on the position of the field and / or sub-field on the substrate.

[0174] 57. The method according to clause 56, wherein sub-fields at or near the edge of the substrate are more likely to be weighted and / or sacrificed.

[0175] 58. The method according to any one of clauses 49 to 57, wherein the optimization determines an optimal sub-field control trajectory that maximizes the number of sub-fields within the specifications.

[0176] 59. The method according to any one of clauses 48 to 58, wherein the optimization takes into account the start-up capabilities of the lithography apparatus used to perform the lithography process.

[0177] 60. The method according to any one of clauses 48 to 59, wherein the parameter of interest includes one or more of edge placement error, overlay, moving average error, and moving standard deviation error.

[0178] 61. The method according to any one of clauses 48 to 60, wherein the optimization includes co-optimization in terms of at least two of the said parameters of interest, the parameters of interest including at least one lithography parameter related to the lithography process and at least one process parameter related to at least one additional processing step.

[0179] 62. The method according to clause 61, wherein the at least one lithography parameter relates to the control of a lithography apparatus for performing a lithography process, and the at least one processing parameter relates to the control of at least one processing apparatus for performing the at least one additional processing step.

[0180] 63. The method according to clause 62, wherein the at least one processing apparatus comprises one or more of the following: an etching apparatus or etching apparatus chamber, a deposition apparatus, a baking apparatus, a developing apparatus, and a coating apparatus.

[0181] 64. The method according to any one of clauses 48 to 63, comprising the steps of constructing a physical and / or empirical through-stack model that describes how an interested parameter propagates through a stack formed on a substrate in multiple layers;

[0182] using the through-stack model to estimate the evolution of the interested parameter through the stack at the sub-field level; and

[0183] using the estimate of the evolution of the interested parameter through the stack in the optimization.

[0184] 65. The method according to clause 64, comprising, after initiating in-field correction, using the through-stack model to calculate a residual error;

[0185] and using the residual error for in-field correction in subsequent optimization.

[0186] 66. The method according to clause 64 or 65, comprising using the through-stack model to predict the value of an interested parameter; and

[0187] using the predicted value in the step of determining in-field correction.

[0188] 67. The method according to any one of clauses 48 to 66, comprising determining a sensitivity metric that describes the sensitivity of corrections to the input data for determining in-field correction and / or layout of the pattern; and

[0189] using the sensitivity metric in the optimization step.

[0190] 68. The method according to clause 67, wherein the sensitivity metric describes the precision of a potential start-up input.

[0191] 69. The method according to clause 68, wherein the sensitivity metric indicates a lower precision in cases where the input data is unreliable and / or where the start-up potential is limited and cannot correctly initiate the potential start-up.

[0192] 70. The method according to any one of clauses 67 to 69, wherein the step of determining the in-field correction comprises optimizing one or more of the following: scanner-mask co-optimization control distribution, control loop time filtering, and / or control loop weighting.

[0193] 71. The method according to any one of clauses 67 to 70, further comprising selecting a control strategy from a control strategy library based on lithography apparatus metrology data using a sensitivity metric.

[0194] 72. The method according to clause 40, wherein the step of determining the in-field correction is further based on a database that links group fingerprints to context data.

[0195] 73. The method according to any one of clauses 40 to 72, wherein each sub-field comprises a single die or a portion of a single die.

[0196] 74. The method according to any one of clauses 40 to 73, further comprising selecting a control strategy from a control strategy library based on an estimate of the in-field fingerprint using lithography apparatus metrology data.

[0197] 75. The method according to any one of clauses 40 to 74, further comprising:

[0198] obtaining training data including external metrology data and / or in-field fingerprints derived therefrom and corresponding lithography apparatus metrology data from a plurality of substrates; and

[0199] training the solver to link the external metrology data and / or in-field fingerprints to the lithography apparatus metrology data.

[0200] 76. The method according to any one of clauses 40 to 75, wherein the lithography apparatus metrology data comprises leveling data.

[0201] 77. The method according to any one of clauses 40 to 76, wherein the steps of determining an estimate of the in-field fingerprint and determining the in-field correction are performed for each substrate.

[0202] 78. The method according to any one of clauses 40 to 77, wherein the steps of determining an estimate of the in-field fingerprint and determining the in-field correction are performed for each field and / or each sub-field.

[0203] 79. The method according to any one of clauses 40 to 78, comprising monitoring the evolution of in-field fingerprint data over time, wafers, and / or batches.

[0204] 80. A method for determining an in-field correction for sub-field control of a lithography process for exposing a pattern in an exposure field of a substrate, the exposure field comprising a plurality of sub-fields, the method comprising:

[0205] Perform optimization to determine in-field correction, the optimization maximizing the number of sub-fields within specifications.

[0206] 81. A method for determining in-field correction for sub-field control of a manufacturing process, the manufacturing process including a lithography process for exposing a pattern on an exposure field of a substrate, the exposure field including a plurality of sub-fields, the manufacturing process including at least one additional processing step, the method including:

[0207] - Perform optimization to determine in-field correction, the optimization including co-optimization in at least one lithography parameter related to the lithography process and at least one processing parameter related to at least one additional processing step.

[0208] 82. A method for determining in-field correction for sub-field control of a lithography process, the lithography process for exposing a pattern on an exposure field of a substrate in a plurality of layers forming a stack, the exposure field including a plurality of sub-fields, the method including:

[0209] Construct a physical and / or empirical through-stack model that describes how the parameter of interest propagates layer-by-layer through the stack.

[0210] 83. A method for determining in-field correction for sub-field control of a lithography process, the lithography process for exposing a pattern on an exposure field of a substrate, the exposure field including a plurality of sub-fields, the method including:

[0211] Determine a sensitivity metric that describes the sensitivity to correcting the input data for determining the correction and / or layout of the pattern; and

[0212] Determine the in-field correction for sub-field control based on the sensitivity metric.

[0213] 84. A computer program including program instructions that, when run on a suitable device, are operable to perform the method of any one of clauses 40 to 83.

[0214] 85. A non-transitory computer program carrier including the computer program of clause 84.

[0215] 86. A lithography apparatus operable to perform the method of any one of clauses 40 to 83; and using the correction in a subsequent exposure.

[0216] 87. A method for determining in-field correction for controlling a lithography apparatus, the lithography apparatus configured to expose a pattern on an exposure field of a substrate, the method including:

[0217] Obtain measurement data for determining in-field correction;

[0218] Determine a precision metric indicating lower precision in cases where measurement data is unreliable and / or in cases where the lithographic apparatus is limited in initiating a potential start input based on the measurement data; and

[0219] Determine the in-field correction at least in part based on the precision metric.

[0220] 88. The method according to clause 87, wherein the potential start input is configured to control a platform of the lithographic apparatus and / or a projection lens manipulator.

[0221] 89. The method according to clause 87, wherein the target of the in-field correction is to control sub-fields of an exposure field.

[0222] 90. The method according to any one of clauses 87 to 89, wherein the step of determining the in-field correction comprises:

[0223] Co-optimizing a first control distribution for the lithographic apparatus and a second control distribution for a mask writing process; and / or

[0224] Optimizing a time filtering constant and / or a weighting constant used in a control loop for controlling the lithographic apparatus, wherein the control loop uses measurement data.

[0225] 91. The method according to clause 87, further comprising selecting a control strategy from a control strategy library using the precision metric, and wherein the in-field correction is at least in part based on the selected control strategy.

[0226] 92. The method according to clause 91, wherein the control strategy comprises a measurement strategy of a metrology device and / or the lithographic apparatus.

[0227] 93. The method according to clause 92, wherein a measurement density associated with the measurement strategy corresponding to the selected control strategy depends on the precision metric.

[0228] 94. The method according to clause 87, further comprising using a trained solver to select a control strategy using the precision metric based on lithographic apparatus measurement data.

[0229] 95. The method according to clause 94, comprising: obtaining training data from a plurality of substrates, the training data including non-lithographic apparatus measurement data and corresponding lithographic apparatus measurement data; and training the solver to link the non-lithographic apparatus measurement data and the lithographic apparatus measurement data.

[0230] 96. The method according to clause 94 or 95, wherein the lithographic apparatus measurement data comprises leveling data.

[0231] 97. The method according to clause 96 further comprises determining an estimate of the die internal stress based on the leveling data; and determining an in-field correction based on the estimated die internal stress.

[0232] 98. The method according to clause 97, wherein said steps of determining the estimate and determining the in-field correction are performed for each die.

[0233] 99. A computer program comprising program instructions which, when run on a suitable device, are operable to perform the method of clause 87.

[0234] 100. A non-transitory computer program carrier comprising the computer program of clause 99.

[0235] 101. A lithographic apparatus operable to perform the method of clause 87 and to use said in-field correction in a subsequent exposure.

[0236] Although a patterning device in the form of a physical mask has been described, the term "patterning device" in this application also includes a data product that conveys a pattern in digital form, for example, used in combination with a programmable patterning device.

[0237] Although the above may have specifically referred to the use of embodiments of the present invention in the context of optical lithography, it will be appreciated that the present invention can be used in other applications, such as 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 created on the substrate. The topography of the patterning device can be pressed into a resist layer supplied to the substrate, and the resist is cured on this layer by applying electromagnetic radiation, heat, pressure, or a combination thereof. The patterning device is removed from the resist, leaving a pattern therein after the resist is cured.

[0238] The terms "radiation" and "beam" in relation to a lithographic apparatus include all types of electromagnetic radiation, including ultraviolet (UV) radiation (e.g., having a wavelength of about 365, 355, 248, 193, 157, or 126 nm) and extreme ultraviolet (EUV) radiation (e.g., having a wavelength in the range of 5 - 20 nm), as well as particle beams, such as ion beams or electron beams.

[0239] Where the context permits, the term "lens" can refer to any one or combination of various types of optical components, including refractive, reflective, magnetic, electromagnetic, and electrostatic optical components.

[0240] The foregoing description of the specific embodiments will so fully reveal the general nature of the invention that others can, by applying the knowledge of those skilled in the art, readily modify and / or adapt these specific embodiments for various applications without undue experimentation and without departing from the general concept of the invention. Therefore, such adaptations and modifications are intended to be within the meaning and range of equivalents of the disclosed embodiments based on the teachings and guidance presented herein. It should be understood that the phrases or terms herein are for description by way of example and not limitation, so that the terminology or phrases of this specification will be interpreted by those skilled in the art in light of the teachings and guidance.

[0241] The breadth and scope of the invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the appended claims and their equivalents.

Claims

1. A method for determining in - field correction for sub - field control of a lithography process, the lithography process being used to expose a pattern on an exposure field of a substrate, the exposure field including a plurality of sub - fields, the method comprises: Constructing a physical and / or empirical through - stack model that describes how an interested parameter propagates through the stack layer by layer.

2. The method according to claim 1, comprising using the model to estimate the evolution of the interested parameter through the stack at the sub - field level.

3. The method according to claim 1 or 2, comprising using the model to calculate the residual error after initiating in - field correction.

4. A method for determining in - field correction for sub - field control of a lithography process, the lithography process being used to expose a pattern on an exposure field of a substrate, the exposure field including a plurality of sub - fields, the method comprises: Determining a sensitivity metric that describes the sensitivity of the correction to the input data used to determine the correction and / or layout of the pattern; and determining the in - field correction for sub - field control based on the sensitivity metric.

5. The method according to claim 4, wherein the sensitivity metric describes the precision of a potential initiation input.

6. The method according to claim 5, wherein, in the case where the input data is unreliable and / or the initiation potential is limited and cannot correctly initiate the potential initiation, the sensitivity metric indicates a lower precision.

7. The method according to any one of claims 4 to 6, wherein the step of determining the in - field correction comprises optimizing one or more of the following: scanner - mask co - optimization control distribution, control loop time filtering, and / or control loop weighting.

8. The method according to any one of claims 4 to 7, further comprising selecting a control strategy from a control strategy library using the sensitivity metric based on lithography tool measurement data.

9. The method according to any one of claims 4 to 7, further comprising using a trained solver to select a control strategy using the sensitivity metric based on lithography tool measurement data.

10. The method according to claim 9, comprises: Obtaining training data from a plurality of substrates, including non - lithography tool measurement data and corresponding lithography tool measurement data; and training the solver to link the non - lithography tool measurement data and the lithography tool measurement data.

11. The method according to any one of claims 8 to 10, wherein the lithography tool measurement data includes leveling data.

12. The method according to any one of claims 4 to 11, comprising determining an estimate of the die stress based on the leveling data; and determining a correction based on the estimated die stress.

13. The method according to claim 12, wherein the steps of determining the estimate and determining the correction are performed for each die based on the leveling data from each substrate.

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