Uniformity control for plasma processing using wall recombination.

Abstract

A system, method, and apparatus for processing a substrate. The plasma processing system includes a process chamber having a chamber body with walls including a first material that encloses an interior space. The plasma processing system further includes a plasma source designed to expose a substrate disposed in the process chamber to a plasma-related flux. The first material has a first set of recombination coefficients associated with the plasma-related flux. The plasma processing system further includes a second material disposed along a first region of the chamber body, the first material having a second set of plasma recombination coefficients associated with the plasma-related flux. The second set of plasma recombination coefficients is different from the first set of plasma recombination coefficients.

Description

[Technical field]

[0001] The present disclosure relates to methods and systems for controlling plasma processing. In particular, the present disclosure relates to uniformity control for plasma processing using plasma species recombination on walls (e.g., plasma radical wall recombination). [Background technology]

[0002] Plasma processing is widely used in the semiconductor industry. Plasma can modify the chemistry of the process gases (e.g., generate ions, radicals, etc.), create new species, and generate a flux of ions to the wafer with energies ranging from a few electron volts (eV) to several thousand eV, without limitations related to the process temperature. There are many types of plasma sources (e.g., capacitively coupled plasma (CCP), inductively coupled plasma (ICP), microwave-generated plasma, electron cyclotron resonance (ECR), etc.), covering a wide operating process range from a few mTorr to several Torr.

[0003] Today, a common plasma process specification is high uniformity of process results (e.g., uniformity across the wafer, right up to the edge of the wafer). For example, process uniformity requirements in semiconductor manufacturing today may include requirements of approximately 1%-2% across the entire wafer, excluding 1-3 mm from the edge. These stringent constraints are constantly becoming ever more rigid as researchers discover new methods for controlling process uniformity and / or improvements to existing methods for controlling process uniformity. Different uniformity control methods may be effective for some processes and completely useless for other processes. Summary of the Invention

[0004] The following is a simplified summary of the disclosure to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure, nor to delineate the scope or claims of particular embodiments of the disclosure. Its sole purpose is to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0005] In an exemplary embodiment, a plasma processing system includes a process chamber including a chamber body having a first wall material including a first set of recombination coefficients for a set of plasma species surrounding an interior volume. The plasma processing system further includes a plasma source designed to expose a substrate disposed in the process chamber to a plasma related flux. The plasma processing system may further include a second material disposed along a first region of the chamber body, the second material having a second set of recombination coefficients associated with the plasma related flux. The second set of plasma recombination coefficients is different from the first set of plasma recombination coefficients.

[0006] In an exemplary embodiment, a method includes obtaining a process result profile for a first substrate. The process result profile may include a plurality of thickness values ​​of the first substrate measured after processing the first substrate in a processing chamber having a chamber body including walls having a first set of recombination coefficients. The method further includes determining that the process result profile includes a first thickness value for a first location on the first substrate that deviates from a first reference thickness value. The method further includes determining a second material that includes a second set of plasma recombination coefficients different from the first set of plasma recombination coefficients and a second location along the chamber body proximate to the first location on the first substrate. The method further includes determining the second material and the second location in response to determining that the first process result profile includes a first thickness value that deviates from the reference thickness value. The method further includes processing the second substrate in the processing chamber with the second material disposed along the chamber body at the second location.

[0007] In an exemplary embodiment, a processing chamber apparatus includes a chamber body having a wall including a first material that encloses an interior space. The first material has a first set of plasma recombination coefficients. The processing chamber apparatus further includes a second material disposed along a first region of the chamber body. The second material has a second set of plasma recombination coefficients that are different from the first set of plasma recombination coefficients. The processing chamber apparatus further includes a third material disposed along a second region of the chamber body. The third material has a third set of plasma recombination coefficients that are different from the first set of plasma recombination coefficients and the second set of plasma recombination coefficients.

[0008] The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 illustrates a processing system according to an aspect of the present disclosure. [Diagram 2] FIG. 1 illustrates a processing system including a chamber body having a surface material configuration according to some embodiments. [Figure 3A] FIG. 2 illustrates a process result profile, according to some embodiments. [Figure 3B] FIG. 2 illustrates a process result profile, according to some embodiments. [Figure 4] 1 is a plan view of a substrate support structure according to some embodiments. [Diagram 5] FIG. 2 illustrates a bottom view of a chamber body in a processing chamber including a plasma injection site according to some embodiments. [Figure 6] FIG. 1 is a block diagram illustrating an example system architecture in which embodiments of the present disclosure may operate. [Figure 7] FIG. 1 illustrates a model training workflow and a model application workflow for surface material configuration, according to one embodiment of the present disclosure. [Figure 8] 1 is a flow diagram of one exemplary method for predicting a recombination configuration for a processing chamber, according to some embodiments of the present disclosure. [Figure 9] FIG. 1 is a block diagram of an example computing device capable of plasma delivery and / or processing, operating in accordance with some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Today, a common plasma process parameter is high uniformity of process results (e.g., uniformity across the wafer, right to the edge of the wafer). This parameter is often extremely difficult to achieve because it involves many factors, many of which interfere with other factors. Plasma uniformity, chamber design, wafer temperature distribution, bias electrode design, etc. are only some of those factors. Radio frequency (RF) antennas and processing chambers are manufactured and assembled to achieve the highest level of process uniformity.

[0011] One factor that affects process uniformity in a process chamber is wall recombination of radicals in the chamber body. Conventional systems generally ignore the effect of wall recombination on plasma processing unless the recombination coefficient for the walls is a few percent or more. In some processes, especially slow processes, wall recombination of reactive species plays a larger role than is generally considered, even when the recombination coefficient is much smaller than one percent. Manipulating the surface material in the chamber body of the processing chamber can change the process profile by modifying the plasma recombination rate along the surface of the chamber body. For example, choosing a material for the surface, placing a liner or other element made from a material with selected properties, and / or using a film can have a particular effect on portions of the process profile. Furthermore, the location where each of the materials is disposed in the chamber can affect different process results across the surface of the processed substrate. Since the recombination coefficient of ions is a constant (e.g., about 1), wall recombination can be effective for processes that have a larger or dominant contribution from radicals reacting with the substrate.

[0012] Aspects of the present disclosure provide methods, systems, and apparatus that enable improved control of plasma processing in a processing chamber. For example, as described herein, the methods, systems, and apparatus utilize different materials with diverse sets of plasma recombination coefficients of plasma radicals in the chamber to modify (e.g., correct) defects in the plasma process. For example, a first material may be placed proximate to the edge of the substrate to improve process uniformity defects that occur toward the edge region of the substrate. The present disclosure, in some embodiments, introduces new processing chamber surface material configurations that include actuators, couplers, or other devices to arrange materials with different plasma recombination rates to selectively modify plasma process results (e.g., to improve uniformity across the wafer). In some embodiments, the present disclosure identifies materials and locations in the processing chamber (e.g., using modeling techniques) to arrange materials to improve process results (e.g., to more accurately meet process result requirements or reference a process result profile).

[0013] In an exemplary embodiment, a plasma processing system includes a process chamber including a chamber body having a wall including a first material having a first set of plasma recombination coefficients surrounding an interior space. The plasma processing system further includes a plasma source designed to expose a substrate disposed in the process chamber to a plasma-related flux. The first set of plasma recombination coefficients is associated with the plasma-related flux. The plasma processing system may further include a second material disposed along a first region of the chamber body, the second material having a second set of plasma recombination coefficients associated with the plasma-related flux. The second set of plasma recombination coefficients is different from the first set of plasma recombination coefficients.

[0014] In an exemplary embodiment, a method includes obtaining a process result profile of a first substrate. The process result profile may include a plurality of thickness values ​​of the first substrate measured after processing the first substrate in a processing chamber having a chamber body including walls having a first material with a first set of plasma recombination coefficients. The method further includes determining that the process result profile includes a first thickness value for a first location on the first substrate that deviates from a first reference thickness value. The method further includes determining a second material that includes a second set of plasma recombination coefficients different from the first set of plasma recombination coefficients and a second location along the chamber body proximate to the first location on the first substrate. Determining the second material and the second location is in response to determining that the first process result profile includes a first thickness value that deviates from the first reference thickness value. The method further includes processing the second substrate in the processing chamber with the second material disposed along the chamber body at the second location.

[0015] In an exemplary embodiment, a processing chamber apparatus includes a chamber body having a wall including a first material that encloses an interior space. The processing chamber apparatus further includes a second material disposed along a first region of the chamber body. The first material has a second set of plasma recombination coefficients that are different from the first set of plasma recombination coefficients. The processing chamber apparatus further includes a third material disposed along the second region of the chamber body. The third material has a third set of plasma recombination coefficients that are different from the first set of plasma recombination coefficients and the second set of plasma recombination coefficients.

[0016] FIG. 1 illustrates a processing system 100 according to an embodiment of the present disclosure. The processing system 100 may include a processing chamber 120 and a plasma source 110. The plasma source includes a wall 102 (e.g., for holding atmospheric pressure), a gas inlet 112, and a gas distribution volume bounded by the wall. The processing chamber 120 includes a wall 111 that holds an internal vacuum and provides support for the plasma source 110, a substrate support 116, and a gas outlet 114. The gas inlet 112 and the gas outlet 114 may provide a flow of a feed gas under a processing gas pressure through the processing system. The feed gas may include any of air, O2, N2, Ar, NH3, He, and / or other suitable processing gases. The plasma source 110 may include a gas expansion volume of a gas injector (e.g., without plasma). The plasma source 110 may be designed to supply plasma to (e.g., by generating or facilitating a flow into) the processing chamber 120. The plasma source delivers plasma through plasma injection sites 118A-B. Processing chamber 120 houses a substrate 130 to be processed by processing system 100. Processing system 200 can be a plasma chamber, including an etch chamber, a deposition chamber (including atomic layer deposition, chemical vapor deposition, physical vapor deposition), etc. For example, the plasma chamber can be a chamber for a plasma etcher or a plasma cleaning device, etc.

[0017] 1, plasma may be injected into the processing chamber 120 through an annular opening (e.g., an annular plasma injection site). In some embodiments, the processing system 100 may include other plasma injection configurations, such as using circular, linear, and / or other geometric openings. In another embodiment, plasma may be injected into the processing chamber 120 using multiple plasma injection sites, each of which includes one or more of the previously described geometric configurations or other configurations not described herein.

[0018] The processing system 100 includes a first surface material configuration. In some embodiments, the walls 111 of the processing system 100 have a material that includes a set of recombination coefficients that relate to the rate of reaction and combination of radicals (e.g., from nitrogen atoms N to nitrogen molecules N2). In some embodiments, the processing device may include one or more surface materials (e.g., liners, films, plates, etc.) that have a different set of recombination coefficients than the walls 111. In some embodiments, the processing system 100 includes an initial or uncorrected surface material configuration that may be used to process a substrate and obtain (e.g., using substrate metrology) an initial process result profile (e.g., process result profile 300 of FIG. 3A). The initial process results may be further used to refine the process result profile (e.g., to improve process uniformity) by updating (e.g., adding surface material, positioning or repositioning surface material, etc.) the initial surface material configuration to a corrected or updated surface material configuration. The processing system 100 may further be used to process a new substrate and obtain (e.g., using substrate metrology) an updated process result profile.

[0019] FIG. 2 illustrates a processing system 200 including a chamber body having a surface material configuration according to some embodiments. The processing system 200 may include a processing chamber 220 and a plasma source 210. The plasma source includes a wall 202 (e.g., for holding atmospheric pressure), a gas inlet 212, and a gas distribution space bounded by the wall. The processing chamber 220 includes a wall 211 that holds an internal vacuum and provides support for the plasma source 210, a substrate support 216, and a gas outlet 214, and may include features described in connection with the processing chamber in other embodiments. The gas inlet 212 and the gas outlet 214 may provide a flow of a feed gas under a processing gas pressure through the processing system. The feed gas may include any of air, O2, N2, Ar, NH3, He, and / or other suitable processing gases. The plasma source 210 may include a gas expansion space of a gas injector (e.g., without plasma). The plasma source 210 is designed to supply (eg, generate or facilitate flow into) a plasma to the processing chamber 220 to process a substrate 230 disposed within the processing chamber 220 .

[0020] In some embodiments, the plasma may be injected into the processing chamber 220 through an annular opening, as shown in Figure 2. In some embodiments, the processing system 200 may include other plasma injection configurations, such as using circular, linear, and / or other geometric openings. In another embodiment, the plasma may be injected into the processing chamber 220 using multiple plasma injection sites, each of which includes one or more of the previously described geometric configurations or other configurations not described herein.

[0021] 2, the processing system 200 includes a surface material configuration. In some embodiments, the walls 211 of the processing chamber 220 may include a material having a recombination coefficient related to the rate of reaction and combination of radicals (e.g., nitrogen atoms N to nitrogen molecules N2). In some embodiments, the processing device may include one or more surface materials 232A-C (e.g., liners, films, plates, etc.) of a material having a different recombination coefficient than the walls 211.

[0022] The surface material may include a liner or other element made from a material that includes selected properties (e.g., associated with a set of plasma species recombination rates or coefficients). In some embodiments, the material may be disposed on the chamber body (e.g., as a film). The surface material may have a first set of plasma recombination coefficients (e.g., each associated with a different plasma species). The plasma recombination coefficient may include the rate at which reactive species of the plasma bind or interact at or near the surface of the chamber body 220. The plasma recombination coefficient may correspond to a plasma or plasma-related flux generated by the plasma source 210.

[0023] In many processes, especially slow processes (e.g., processes that have a process result rate below a certain threshold amount of time or where the entire process or individual process procedures (e.g., process steps) include processing durations that exceed a threshold duration), wall recombination of reactive species plays a larger role, even when the recombination coefficient is much smaller than one percent. Thus, manipulating the material of the surfaces of the chamber body of the processing chamber can change the process profile. For example, choosing a material for the surface, placing a liner or other element made from a material with selected properties, or using a film can have a particular effect on portions of the process profile. Additionally, the location where each of the materials is disposed within the chamber can affect different process results across the surface of the processed substrate.

[0024] As shown in FIG. 2, the processing chamber 220 may include a first surface material 232A disposed along the wall 211 of the body of the processing chamber 220. The first surface material 232A may include a material including a first set of recombination rates (e.g., recombination coefficients) lower than those of the wall 211. The processing chamber 220 may include a second surface material 232B including a set of recombination rates (e.g., recombination coefficients) lower than those of the wall 211. The processing chamber may include a third surface material that may include a material including a recombination rate (e.g., set of recombination coefficients) higher than those of the wall. The surface material configurations shown in FIG. 2 are merely examples and illustrate exemplary locations where the surface materials 232A-C may be distributed within the processing chamber.

[0025] In some embodiments, surface materials 232A-C can be disposed proximate regions of substrate 230 to affect local process results of the substrate proximate the surface materials. For example, as shown in FIG. 2, surface materials 232A and 232B are disposed in processing chamber 220 at locations proximate the edge of substrate 230. Surface materials 232A-B affect process results of edge regions of the substrate based on relative recombination of materials. For example, in embodiments where surface materials 232A-B have one or more recombination coefficients higher than the corresponding recombination of the material of wall 211, the addition of surface materials 232A-B enhances process results near the edge of substrate 230 when processed in processing chamber 220. In another example, in embodiments where surface materials 232A-B have one or more recombination coefficients lower than that of the material of wall 211, the addition of surface materials 232A-B degrades process results near the edge of substrate 230 when processed in processing chamber 220.

[0026] In another example, the surface material 232C can be disposed along the walls 211 at a location proximate to a central region of the substrate 330 to affect process results of the substrate 230 at and / or near the center of the substrate 230. For example, in an embodiment where the surface material 232C has a higher recombination coefficient than the material of the walls 211 (and / or the support structures 216, etc.), the addition of the surface material 232C enhances process results near the center of the substrate 230 processed in the processing chamber 220. In another example, in an embodiment where the surface material 232C has a lower recombination coefficient than the material of the walls 211 (and / or the support structures 216, etc.), the addition of the surface material 232C reduces process results near the center of the substrate 230 when processed in the processing chamber 220.

[0027] In some embodiments, the surface material may be movable within the process chamber. For example, the processing chamber may include an actuator coupled to the surface material(s) 232A-C. The surface material(s) 232A-C may be coupled to and disposed at a distance from the wall 211. The actuator may be utilized to change the distance between the surface material(s) 232A-C. For example, the surface material may be coupled to a moveable platform that is fixed (e.g., clamped and / or affixed) to the wall 211. The actuator may move the surface material closer to or farther away from the substrate. For example, the surface materials 232A and 232C may be moved lowered or raised closer to or farther away from the substrate 230. In some embodiments, the surface material(s) 232A-C are substantially parallel to one or more of the walls 211.

[0028] In some embodiments, the substrate support structure 216 may be capable of moving (e.g., moving) to move the substrate closer to one or more of the surface materials 232A-C. For example, the substrate support structure 216 may move up or down to raise or lower the substrate, increasing or decreasing the distance between the substrate and the surface material.

[0029] In some embodiments, in addition to or as an alternative to surface materials deployed using liners and / or films, the surface material may be deployed within the processing chamber as a foldable diaphragm structure. The foldable diaphragm structure may include multiple movable blocking panels or leaves that can change its shape (e.g., increase / decrease exposed surface area), resulting in a greater or lesser effect on the process results in areas proximate to the foldable diaphragm. In some embodiments, the processing chamber includes a mechanism (e.g., a moving plate, a rotating plate, etc.) for changing the position of the foldable diaphragm structure within the processing chamber. In some embodiments, the surface material may be disposed on a movable plate and / or structure capable of repositioning the surface material within the chamber, for example, the surface material may be disposed on a rotatable wall or moving plate.

[0030] In some embodiments, the processing chamber includes mechanisms for heating and / or cooling the surface materials 232A-C within the processing chamber. The heating and / or cooling of the surface materials 232A-C may modify one or more associated recombination coefficients of the surface materials. The heating and / or cooling may extend the usable range of the recombination coefficients (e.g., without changing the manufacturing equipment).

[0031] In some embodiments, the chamber body includes a wall 211 having a first material including a first set of plasma recombination coefficients (e.g., for various plasma species) and one or more of surface materials 232A-C having a second set of plasma recombination coefficients. The surface materials 232A-C may include a second set of plasma recombination coefficients in a first region within the chamber body and a third set of plasma recombination coefficients in a second region within the chamber body. In some embodiments, a combination of three or more materials having various recombination coefficients may be utilized throughout the interior space along the chamber body. In some embodiments, the surface materials 232A-C may be disposed along or adjacent to the substrate support assembly 216.

[0032] In some embodiments, the recombination coefficient of the surface material 232A-C and / or the chamber body wall 211 material may be related to silicon nitridation occurring in the process chamber 220. Silicon nitridation uses nitrogen atoms N (radicals), which are usually obtained in a plasma discharge by dissociation of nitrogen molecules N2. Nitridation may be a slow process, and thus the radical flux and its profile on the wafer may be entirely determined by faster processes, such as radical generation, radical flow from the generation region (e.g., plasma source 210) to the exhaust (e.g., exhaust port 214), and radical diffusion to the walls (including the substrate) and recombination on the walls. Placing the material (e.g., surface material 232A-C) in a region (e.g., along the surface of the chamber body) with high or low recombination rates of some plasma species can modify the local recombination rate of radicals (e.g., of various plasma species) and result in a processed substrate with improved uniformity (e.g., a substrate with smaller deviations of process results, such as film thickness across the surface of the substrate).

[0033] In some embodiments, portions of the chamber body wall 211 may include surface regions or portions having materials with high and / or low recombination rates for various plasma species. A first surface material 232A including a material with a low recombination coefficient (e.g., good reflector of radicals, smaller value than the chamber body wall 211) may be disposed near one or more regions on a substrate having a process value (e.g., film thickness, critical dimension, etc.) below a reference process result (e.g., desired thickness or process result uniformity). For example, if a process profile (e.g., thickness profile) of a substrate processed in a fabrication chamber including quartz walls had a process result with a relatively higher (e.g., thicker) center region and a lower (e.g., thinner) edge region, a surface material with a low recombination rate may be disposed to cover the wall (e.g., liner, film, etc.) near the wafer edge. Examples of such materials with low recombination rates include Pyrex or other borosilicate glasses, boron nitride films, etc. For example, Pyrex, boron silicate carbide, and boron nitride films may have recombination coefficients for nitrogen that are 3-10 times lower than the recombination coefficient of quartz, which is often used for chamber walls. Different materials, such as titanium or stainless steel, which have recombination coefficients for nitrogen that are 3-10 times higher than quartz, may be disposed near the center of the wafer to reduce process results (e.g., thickness) near the center.

[0034] In some embodiments, plasma and flow simulations may be used to determine dimensions and locations of surface materials 232A-C to set up the manufacturing chamber 100 for processing substrates that result in a process profile having a higher process uniformity rating than alternative surface material configurations and / or than without the surface material 232. The surface material 232, in embodiments, may include disks, rings, coatings, films, and / or other components.

[0035] FIG. 3A illustrates a process result profile 300 according to some embodiments. The process result profile may include an initial process profile or an uncorrected process result profile (e.g., using the surface material configuration shown in FIG. 1 and described in the corresponding description) of a substrate processed in a processing system (e.g., plasma source, processing chamber, etc.). The process result profile may indicate a process result parameter (e.g., thickness, critical dimension, etc.). A first axis 304 is associated with a location across the surface of the wafer. For example, the process profile may be measured radially from a first edge to a second edge, and the process profile runs closely through the center of the substrate. A second axis 302 indicates a process result value (e.g., thickness value). In some embodiments, the values ​​may be normalized to another value or a reference value, or may indicate a relative value of the process result (e.g., percentage of maximum value) in some cases.

[0036] The process result profile 300 may show portions of process results that are greater (e.g., thicker) and / or smaller (e.g., thinner) than a threshold process result value (e.g., an average process result value, a process control limit, a statistical value such as deviation or variance, etc.). For example, a first region 308 of the process result profile 300 represents a region on a surface of a substrate (e.g., a center of a substrate). As shown in FIG. 3A, the process result profile 300 shows that the first region 308 includes process result values ​​that are greater than an average process result. Values ​​within the first region 308 may be reduced to reduce variance among other process result values ​​in other regions of the substrate (e.g., to improve process uniformity). A second region 306 of the process result profile 300 represents a region on a surface of a substrate (e.g., an edge of a substrate). As shown in FIG. 3A, the process result profile 300 shows that the second region 306 includes process result values ​​that are smaller than an average process result. The value in the second region 306 may be increased to reduce the variance between other process result values ​​in other regions of the substrate (eg, to improve process uniformity).

[0037] As further described in the embodiments, different surface material configurations (e.g., different sets of materials with recombination coefficients disposed at various locations in the processing chamber) can be deployed during substrate processing to affect the process result (e.g., thickness) across the surface of the substrate. For example, the different surface material configurations can increase the process result in the second region 306 and reduce the process result value in the first region 308, as shown in FIG. 3A (e.g., to improve process uniformity).

[0038] FIG. 3B illustrates a process result profile 350 according to some embodiments. The process result profile may include a modified or corrected process result profile (e.g., using the surface material configuration shown in FIG. 2 and described in the corresponding description) of a substrate processed in a processing system (e.g., plasma source, processing chamber, etc.). The process result profile may indicate a process result parameter (e.g., thickness, critical dimension, etc.). A first axis 354 is associated with a location across the surface of the wafer. For example, the process profile may be taken radially from a first edge to a second edge and proceed proximate to the center of the substrate. A second axis 352 indicates a process result value (e.g., thickness value). In some embodiments, the values ​​may be normalized to another value or a reference value, or may possibly indicate a relative value of the process result (e.g., a percentage of a maximum value). The process result profile 350 may include one or more features and / or aspects of the process result profile 350.

[0039] As further described in other embodiments, different surface material configurations (e.g., different sets of materials with recombination coefficients disposed at various locations in a processing chamber) can be employed during substrate processing to affect the process result values ​​of the substrate. For example, comparing process result profile 350 to process result profile 300 of FIG. 3A, the different surface material configuration can increase the process result value of second region 256 to updated second region 356 and reduce the process result value of first region 308 to updated first region 358. As described in connection with FIG. 2, the following modifications can be made in response to using the surface material configuration: Process result profiles 300 and 350 are simply used to illustrate how recombination configurations (e.g., arrangements of materials with different recombination rates) in a chamber can be updated to process a substrate and generally produce more uniform process results.

[0040] FIG. 4 is a plan view of a substrate support structure 400, according to some embodiments. As shown in FIG. 4, the substrate support structure 400 may include a support surface 402. The support surface 402 may include a first material having a first set of recombination coefficients (e.g., the same as the walls 111 and 211 of FIGS. 1 and 2, respectively). The substrate support structure 400 supports a substrate 406. The substrate support structure further includes a surface material 404. The surface material 404 may include one or more features and / or aspects of the surface materials 232A-C. The surface material 404 may have a second set of recombination rates that is different from the first set of recombination rates of the support surface 402. The substrate 406 and the surface material 404 are disposed on the support surface 402.

[0041] In some embodiments, the surface material 404 is disposed as a single disk including a hollow center designed to fit the substrate 406. In some embodiments, the surface material 404 is disposed as multiple disks or rings. For example, the surface material 404 may be disposed as concentric rings centered about the center of the substrate 406. Generally, the surface material 404 is disposed in the region between the edge of the support surface 402 and the edge of the substrate 406. In some embodiments, the surface material 404 is disposed up to the edge of the support surface 402 and / or up to the edge of the substrate 406, while in other embodiments, the surface material 404 is disposed such that there is a gap between the outer edge of the surface material 404 and the edge of the support surface 402 and / or a gap between the edge of the substrate 406 and the inner edge of the surface material 404.

[0042] FIG. 5 is a bottom view of a chamber body 500 in a processing chamber including a plasma injection site 504, according to some embodiments. As shown in FIG. 5, the chamber body 500 includes wall structures 504A-C, surface materials 502A-B, and a plasma injection site 506. The wall structures 504A-C can include a first material having a first set of recombination coefficients. The wall structures 504A-C form an opening to create the plasma injection site 506. The plasma injection site 506 is designed to deliver plasma to (e.g., facilitate flow into) the processing chamber. In some embodiments, the plasma injection site 506 can include an annular opening. In some embodiments, the chamber body 500 can include other plasma injection configurations, such as using circular, linear, and / or other geometric openings. In another embodiment, plasma can be injected into the processing chamber using multiple plasma injection sites, each of which includes one or more of the previously described geometric configurations or other configurations not described herein.

[0043] In some embodiments, the chamber body 500 includes a first surface material 502A disposed along a first region of the chamber body 500 along a wall structure 504A disposed within the plasma between openings of the plasma injection site 506. In some embodiments, the first surface material 502A is arranged in a circular configuration, while in other embodiments, the first surface material 502A is arranged in a set of concentric rings or disks. In some embodiments, the surface material 502A covers the entirety of the first region of the chamber body 500 (e.g., abutting an edge of the plasma injection site).

[0044] In some embodiments, the chamber includes a second region disposed between the wall structures 504B-C. The second region may be disposed outside of a radius or outer perimeter of the plasma injection site 506 to an edge of the chamber body 500. The chamber body may include a second surface material 502B disposed in the second region along the wall structures 504B-C. The second surface material 502B may have a first set of recombination coefficients that are different from the wall structures 504A-C. In some embodiments, the first surface material 502A has the same recombination coefficient as the second surface material 502B. In some embodiments, one or both of the set of recombination coefficients of the first surface material 502A or the set of recombination coefficients of the second surface material 502B may be greater or less than the corresponding recombination coefficients of the materials of the wall structures 504A-C.

[0045] 6 is a block diagram illustrating an example system architecture 600 in which an embodiment of the present disclosure may operate. The fabrication chamber 100 includes a client device 620, a fabrication device 624, a metrology device 628, a server 612, and a data store 640. The server 612 may be part of a modeling system 610. The modeling system 610 may further include server machines 670 and 680.

[0046] The manufacturing equipment 624 (eg, associated with producing a corresponding product, such as a wafer, by the manufacturing equipment 624 ) may include one or more processing chambers 626 .

[0047] The client device 620, the manufacturing tool 624, the metrology tool 628, the server 612, the data store 640, the server machine 670, and the server machine 680 may be coupled to each other via a network 630 to model plasma recombination and determine a recombination configuration (e.g., to improve process uniformity of substrate processing in the processing chamber 626).

[0048] In some embodiments, network 630 is a public network that provides client devices 620 with access to server 612, data store 640, and / or other publicly available computing devices. In some embodiments, network 630 is a private network that provides client devices 620 with access to manufacturing equipment 624, metrology equipment 628, data store 640, and / or other privately available computing devices. Network 630 may include one or more wide area networks (WANs), local area networks (LANs), wired networks (e.g., Ethernet networks), wireless networks (e.g., 802.11 networks or Wi-Fi networks), cellular networks (e.g., Long Term Evolution (LTE) networks), routers, hubs, switches, server computers, cloud computing networks, and / or combinations thereof.

[0049] The client device 620 may include computing devices such as personal computers (PCs), laptops, mobile phones, smartphones, tablet computers, netbook computers, network-connected televisions ("smart TVs"), network-connected media players (e.g., Blu-ray players), set-top boxes, over-the-top (OTT) streaming devices, operator boxes, etc. The client device 620 may include a recombination component 622. The recombination component 622 may receive data such as process result data from a metrology tool 628 and display the process result data (e.g., in the form of a process result profile (e.g., process result profiles 300, 350 of FIGS. 3A and 3B, respectively)) on the client device. The recombination component 622 may interact with one or more elements of the modeling system 610 to determine one or more configurations of surface materials (e.g., materials including varying sets of recombination coefficients and locations where those materials should be disposed) to be disposed within the processing chamber 626 to process substrates that meet threshold criteria (e.g., process uniformity requirements).

[0050] Data store 640 may be a memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or another type of component or device capable of storing data. Data store 640 may store one or more pieces of historical data 642, including process result data 644 and / or surface material configuration data 646. In some embodiments, historical data 642 may be used to train, validate, and / or test machine learning models 690 of modeling system 610.

[0051] The modeling system 610 may include one or more computing devices, such as a rack mount server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, etc. In some embodiments, the modeling system 610 may include a prediction component 616. The prediction component 616 may take data retrieved from a metrology tool 628 and generate recombination configuration data. The prediction component receives metrology data from the metrology tool 628. The metrology data may include a process result profile associated with a substrate processed in the processing chamber 626. The prediction component determines a recombination configuration (e.g., using the model 690). The recombination configuration may include one or more materials having a set of recombination coefficients disposed at a determined location within the processing chamber 626. For example, a substrate processed in a processing chamber including surface materials disposed according to the recombination configuration produces a processed substrate having a processed result that meets a threshold criterion (e.g., a process uniformity requirement).

[0052] In some embodiments, the prediction component 616 may use the historical data 642 to determine a recombination configuration that, when applied to a processing chamber, results in a substrate processed in the chamber that meets a threshold criterion (e.g., a process uniformity requirement). In some embodiments, the prediction component 616 may use a model 690 (e.g., a trained machine learning model) to identify a recombination configuration that, when utilized by a processing chamber, results in a substrate having a process result that meets a threshold condition (e.g., a process uniformity requirement). The model 690 may use the historical data to determine the recombination configuration.

[0053] In some embodiments, modeling system 610 further includes server machine 670 and server machine 680. Server machines 670 and 680 can be one or more computing devices (such as a rack mount server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, etc.), data stores (e.g., hard disks, memory, databases), networks, software components, or hardware components.

[0054] The server machine 670 may include a dataset generator 672 that is capable of generating a dataset (e.g., a set of data inputs and a set of target outputs) for training, validating, or testing a machine learning model.

[0055] The server machine 680 includes a training engine 682, a validation engine 684, and a testing engine 686. The training engine 682 may be capable of training models 690 (e.g., machine learning models) using one or more of the process result data 644 and the surface material configuration data 646. The validation engine 684 may determine the accuracy of each of the models 690 based on a corresponding set of features of each training set. The validation engine 684 may discard models 690 that have an accuracy that does not meet a threshold accuracy. The testing engine 686 may determine the model 690 that has the highest accuracy of all of the trained machine learning models based on the test (and optionally, validation) set.

[0056] In some embodiments, training data is provided to train model 690 such that the trained machine learning model is to receive new inputs having new metrology data including a process result profile, and to produce new outputs based on the new inputs, the new recombination configuration indicating at least a new surface material (e.g., having a recombination coefficient) and a location within the process chamber where the new material should be disposed such that a processing chamber processing a substrate with the new recombination configuration produces a substrate in a state where the substrate meets a threshold criterion (e.g., a process uniformity requirement).

[0057] Model 690 may refer to a model created by training engine 182 using a training set that includes data inputs and corresponding target outputs (historical outcomes of cell culture under parameters associated with the target inputs). Patterns in the data set may be discovered that map the data inputs to the target outputs (e.g., identifying connections between portions of cell growth data and resulting yields of target product formation), and machine learning model 690 is provided with mappings that capture these patterns. Machine learning model 690 may use one or more of logistic regression, syntactic analysis, decision trees, or support vector machines (SVMs). Machine learning may consist of linear, single level of nonlinear operation (e.g., SVMs) and / or may be neural networks.

[0058] The confidence data may include or indicate a level of confidence that one or more recombination configurations will result in a substrate having process results that meet a threshold criterion (e.g., process uniformity requirements) when processing the substrate according to the recombination configuration. In one non-limiting example, the confidence level is a real number between 0 and 1, inclusive, where 0 indicates no confidence in one or more prescriptive actions and 1 represents absolute confidence in the prescriptive actions.

[0059] For purposes of explanation and not limitation, aspects of the disclosure describe training of machine learning models and use of trained learning models using information related to historical data 642. In other embodiments, heuristic or rule-based models are used to determine normative actions. In some embodiments, model 690 includes physics-based elements or derives predictions through physics-based principles. For example, model 690 may include physics-based models based on plasma and flow equations, principles, and / or simulations.

[0060] In some embodiments, the functionality of client device 620, server 612, data store 640, and modeling system 610 may be provided by fewer machines than those shown in Figure 6. For example, in some embodiments, server machines 670 and 680 may be combined into a single machine, and in some other embodiments, server machines 670 and 680 and server 612 may be combined into a single machine.

[0061] In general, functionality described in one embodiment as being performed by client device 620, data store 640, metrology equipment 628, manufacturing equipment 624, and modeling system 610 may, in other embodiments, also be performed on server 612, where appropriate. Additionally, functionality assigned to a particular component may be performed by different components or multiple components operating in conjunction with one another.

[0062] In embodiments, a "user" may be represented as a single individual. However, other embodiments of the present disclosure encompass a "user" being an entity controlled by multiple users and / or automated sources. For example, a set of individual users federated as a group of administrators may be considered a "user."

[0063] 7 illustrates a model training workflow 705 and a model application workflow 717 for a surface material configuration (e.g., a plasma recombination configuration) according to one embodiment of the present disclosure. In an embodiment, the model training workflow 705 may be implemented in a server, which may or may not include a recombination configuration application, and the trained model is provided to a recombination configuration application (e.g., on a client device 620 of FIG. 6), which may implement the model application workflow 717. The model training workflow 705 and the model application workflow 717 may be implemented by processing logic executed by a processor of a computing device. One or more of these workflows 705, 717 may be implemented by one or more machine learning modules implemented, for example, by the server 612 of FIG. 6.

[0064] A model training workflow 705 is for training one or more machine learning models (e.g., deep learning models) to perform one or more classification, segmentation, detection, recognition, determination, etc. tasks associated with the recombination configuration predictor. A model application workflow 717 is for applying the one or more trained machine learning models to perform classification, segmentation, detection, recognition, determination, etc. tasks to identify the surface material configuration (e.g., plasma recombination configuration). One or more of the machine learning models may receive and process the result data (e.g., metrology data of processed wafers) and the recombination configuration data.

[0065] Various machine learning outputs are described herein. A particular number and arrangement of machine learning models are described and shown. However, it should be understood that the number and type of machine learning models used and the arrangement of such machine learning models can be modified to achieve the same or similar end result. Thus, the arrangement of machine learning models described and shown is merely an example and should not be construed as limiting.

[0066] In an embodiment, one or more machine learning models are trained to perform one or more of the following tasks: Each task may be performed by a separate machine learning model. Alternatively, a single machine learning model may perform each of the tasks or a subset of the tasks. Additionally or alternatively, different machine learning models may be trained to perform different combinations of the tasks. In one example, one or several machine learning models may be trained, where the trained ML model is a single shared neural network with multiple shared layers and multiple higher-level separate output layers, each of which outputs a different prediction, classification, discrimination, etc. Tasks that one or more trained machine learning models may be trained to perform are as follows: 1. Recombination Configuration Predictor - As previously described, relationships between plasma recombination configurations (e.g., the arrangement of surface materials disposed along a surface of a chamber body at determined locations according to the determined plasma recombination configuration) may be employed to predict a recombination configuration that, when utilized in a processing chamber, results in a substrate processed in the processing chamber having process results that meet a threshold criterion (e.g., process uniformity requirements). The recombination configuration predictor receives data indicative of a process result profile and outputs a first material comprising a first set of plasma recombination coefficients and a first location along the chamber body proximate to a corresponding location on a first substrate.

[0067] One type of machine learning model that can be used to perform some or all of the above tasks is an artificial neural network, such as a deep neural network. An artificial neural network generally includes a feature representation component that includes a classifier or regression layer that maps features to a desired output space. A convolutional neural network (CNN), for example, hosts multiple layers of convolutional filters. In the lower layers, pooling may be performed and nonlinearities may be addressed, and a multi-layer perceptron is usually added on top of the lower layers to map the top layer features extracted by the convolutional layers to a decision (e.g., classification output). Deep learning is a class of machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Deep neural networks can learn in a supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) manner. Deep neural networks include a hierarchy of layers, with different layers learning different representation levels corresponding to different levels of abstraction. In deep learning, each level learns to transform its input data into a slightly more abstract and complex representation. In particular, the deep learning process can learn on its own which features should optimally go into which levels. The "deep" in "deep learning" refers to the number of layers through which data is transformed. More precisely, deep learning systems have substantial credit assignment path (CAP) depth. A CAP is a chain of transformations from input to output. A CAP represents the potential causal relationship between input and output. For feedforward neural networks, the CAP depth may be the depth of the network, which may be the number of hidden layers + 1. For recurrent neural networks, where a signal may propagate through a layer more than once, the CAP depth is potentially unlimited.

[0068] Training of a neural network can be accomplished in a supervised learning fashion, which involves feeding a training dataset of labeled inputs through the network, observing its outputs, defining an error (by measuring the difference between the output and the label values), and using techniques such as deep gradient descent and backpropagation to adjust the network's weights across all its layers and nodes such that the error is minimized. In many applications, repeating this process across many labeled inputs in the training dataset results in a network that can produce correct outputs when presented with inputs that differ from those present in the training dataset.

[0069] For the model training workflow 705, a training data set containing hundreds, thousands, tens of thousands, hundreds of thousands, or more process result data 710 (e.g., process result profiles, thickness profiles) should be used to form the training data set. In an embodiment, the training data set may also include associated recombination configuration data 712 to form the training data set, where each data point and / or associated recombination configuration may include various labels or classifications of one or more types of useful information. This data may be processed to generate one or more training data sets 736 for training one or more machine learning models.

[0070] In one embodiment, generating the one or more training data sets 736 includes collecting one or more process result measurements (e.g., metrology data) of processed substrates processed in a chamber including a varying recombination configuration disposed on a chamber wall of the associated chamber.

[0071] To accomplish the training, processing logic inputs the training dataset(s) 736 to one or more untrained machine learning models. Prior to inputting the first input to the machine learning models, the machine learning models may be initialized. Processing logic trains the untrained machine learning model(s) based on the training dataset(s) to generate one or more trained machine learning models that perform various operations as described above.

[0072] Training may be performed by inputting one or more of the process result data 710 and the recombination configuration data 712 into the machine learning model one by one. In some embodiments, training the machine learning model includes tuning the model to receive the process result data 710 (e.g., process result profile, thickness profile of the processed substrate) and output a recombination configuration prediction (e.g., one or more materials having a set of recombination coefficients and corresponding locations where the corresponding one or more materials should be disposed in the processing chamber). The machine learning model processes the input to generate an output. The artificial neural network includes an input layer consisting of values ​​at the data points. The next layer is called a hidden layer, and the nodes in the hidden layer each receive one or more of the input values. Each node includes parameters (e.g., weights) to apply to the input values. Thus, each node essentially inputs the input values ​​into a multivariate function (e.g., a nonlinear mathematical transformation) to produce an output value. The next layer may be another hidden layer or an output layer. In either case, the nodes in the next layer receive output values ​​from the nodes in the previous layer, and each node applies weights to those values ​​and then generates its own output value. This can be done at each layer. The final layer is the output layer, where there is one node for each class, prediction and / or output that the machine learning model can produce.

[0073] Thus, the output may include one or more predictions or inferences. For example, the output predictions or inferences may include the determined recombination configuration. The processing logic may cause the substrate to be processed using the recombination configuration and receive an updated thickness profile. The processing logic may compare the updated thickness profile to a target thickness profile and determine whether a threshold criterion is met (e.g., a thickness value measured across the surface of the wafer falls within a target threshold window). The processing logic may determine an error (i.e., a classification error) based on a difference between the updated thickness profile and the target thickness profile. The processing logic adjusts weights of one or more nodes in the machine learning model based on the error. An error term or delta may be determined for each node in the artificial neural network. Based on this error, the artificial neural network adjusts one or more of its parameters (weights for one or more inputs of the node) for one or more of its nodes. The parameters may be updated in a backpropagation fashion, such that nodes in the top layer are updated first, followed by nodes in the next layer, and so on. An artificial neural network includes multiple layers of "neurons," with each layer receiving values ​​as inputs from neurons in the previous layer. The parameters for each neuron include weights associated with the values ​​received from each of the neurons in the previous layer. Adjusting the parameters may thus include adjusting weights assigned to each of the inputs for one or more neurons in one or more layers in the artificial neural network.

[0074] Once the model parameters are optimized, model validation may be performed to determine whether the model has improved and to determine the current accuracy of the deep learning model. After one or more rounds of training, the processing logic may determine whether a stopping criterion has been met. The stopping criterion may be a target level of accuracy, a target number of images processed from the training dataset, a target amount of change to the parameters over one or more previous data points, a combination thereof, and / or other criteria. In one embodiment, the stopping criterion is met when at least a minimum number of data points have been processed and at least a threshold accuracy has been achieved. The threshold accuracy may be, for example, 70%, 80% or 90% accuracy. In one embodiment, the stopping criterion is met when the accuracy of the machine learning model stops improving. If the stopping criterion is not met, further training is performed. If the stopping criterion is met, the training may be completed. Once the machine learning model is trained, a reserved portion of the training dataset may be used to test the model.

[0075] As an example, in one embodiment, a machine learning model (e.g., recombination configuration predictor 767) is trained to determine a recombination configuration (e.g., materials that include a set of recombination coefficients and the locations where those materials should be disposed in a chamber to process a substrate to meet a threshold criterion (e.g., process uniformity requirement)). Similar processes can be performed to train machine learning models to perform other tasks, such as those described above. A large set of (e.g., thousands to millions) process result profiles (e.g., thickness profiles) can be collected and a recombination configuration (e.g., surface material configuration in a process chamber) can be determined.

[0076] Once the one or more trained machine learning models 738 are generated, they may be stored in model storage 745 and added to the recombination configuration application. The recombination configuration application may then use the one or more trained ML models 738, as well as additional processing logic, to implement an automatic mode in which user manual input of information is minimized, and in some cases, even eliminated.

[0077] For the apply model workflow 717, according to one embodiment, input data 762 may be input to a recombination configuration predictor 767, which may include a trained neural network. Based on the input data 762, the recombination configuration predictor 767 outputs an indication and a location where material should be disposed within the process chamber (e.g., recombination configuration data 769).

[0078] 8 is a flow diagram of one exemplary method 800 for predicting a recombination configuration for a processing chamber, according to some embodiments of the present disclosure. Method 800 is performed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, etc.), software (such as that running on a general-purpose computer system or a dedicated machine), or any combination thereof. In one embodiment, the method is performed using server 612 and model 690 of FIG. 6, although in some other embodiments, one or more blocks of FIG. 8 may be performed by one or more other machines not shown in the figure.

[0079] At block 802, processing logic obtains a process result profile for a first substrate having a set of first substrate thickness values ​​measured after processing the first substrate in a processing chamber having a chamber body including walls having a first material having a first set of plasma recombination coefficients. The processing chamber may include one or more features and / or aspects of the processing systems 100, 200 of Figures 1 and 2. The process result profile may include one or more features and / or aspects of the process result profiles 300, 350 of Figures 3A and 3B.

[0080] At block 804, the processing logic determines that the process result profile includes a first thickness value for the first location on the first substrate that deviates from a first reference thickness value. The reference thickness value may be associated with a process result criterion (e.g., a process uniformity requirement). For example, the reference thickness may be an average thickness or a process control limit associated with the processing chamber.

[0081] At block 806, the processing logic determines a first material including a second set of plasma recombination coefficients different from the first set of plasma recombination coefficients and a second location along the chamber body proximate to the first location on the first substrate. The processing logic may further determine a configuration for the first material. For example, the first material may be disposed in the processing chamber in concentric rings or disks.

[0082] In another example, the first material can be disposed along the walls of the interior space of the process at a location proximate to a central region of a substrate disposed within the process chamber and can affect process results of the substrate at and / or near the center of the substrate. For example, in an embodiment where the first material has one or more recombination coefficients higher than the material of the walls of the chamber, the addition of the first material can enhance process results near the center of a substrate processed in the process chamber. In another example, in an embodiment where the first material has one or more recombination coefficients lower than the material of the walls of the chamber, the addition of the first material reduces process results near the center of a substrate when processed in the process chamber.

[0083] In some embodiments, the surface material may be movable within the process chamber. For example, the processing chamber may include an actuator coupled to the first material. The first material may be coupled to a wall of the processing chamber and disposed at a distance from the wall. The actuator may be utilized to change the distance between the first material(s) and the wall. For example, the first material may be coupled to a moveable platform that is fixed (e.g., clamped and / or affixed) to the wall. The actuator may move the surface material closer to or farther away from the substrate.

[0084] In some embodiments, the first material may be deployed in the processing chamber using a liner and / or a film. In some embodiments, the first material may be disposed in the processing chamber as a foldable diaphragm structure. The foldable diaphragm structure may include multiple movable blocking panels or leaves that can change its shape (e.g., increase / decrease the exposed surface area), which results in a greater or lesser effect on the process results in areas proximate to the foldable diaphragm. In some embodiments, the processing chamber includes a mechanism (e.g., a moving plate, a rotating plate, etc.) for changing the position of the foldable diaphragm structure in the processing chamber.

[0085] In some embodiments, the processing chamber includes a mechanism for heating and / or cooling the first material in the processing chamber. The method 800 may further include heating and / or cooling the first material. The resulting heating and / or cooling may modify one or more recombination coefficients of the first material. The heating and / or cooling may expand the usable range of the recombination coefficients (e.g., without changing the first material).

[0086] In some embodiments, the processing logic further includes using the first process result profile as an input to a machine learning model. The method further includes obtaining one or more outputs of the machine learning model. The one or more outputs are indicative of the first material and the second location. The machine learning model may include one or more features and / or aspects of model 690 of FIG. 6.

[0087] At block 808, processing logic optionally determines that the process result profile includes a second thickness value for a third location on the first substrate that deviates from the second reference thickness. At block 910, processing logic optionally determines a second material and a fourth location that includes a third set of plasma recombination coefficients different from the first set of plasma recombination coefficients. In some embodiments, one or more plasma recombination coefficients of the second set are greater than corresponding plasma recombination coefficients of the first set and the third set. The plasma recombination coefficients of the third set are less than corresponding plasma recombination coefficients of the second set.

[0088] At block 812, the method 800 includes processing a second substrate in the processing chamber with the first material disposed along the chamber body at a second location. In some embodiments, different combinations of materials having varying sets of plasma recombination coefficients may be disposed at different points in the processing chamber along the chamber body proximate different regions of the substrate to affect process results of a substrate processed in the chamber with an associated surface material configuration.

[0089] 9 illustrates a block diagram of an example computing device 900 capable of plasma delivery and / or processing, operating according to one or more aspects of the present disclosure. In various illustrative examples, various components of the computing device 900 may represent various components of the computing device (e.g., modeling system 610 of FIG. 6), a training engine, a validation engine, and / or a test engine described in connection with FIG.

[0090] The exemplary computing device 900 may be connected to other computer devices in a LAN, an intranet, an extranet, and / or the Internet. The computing device 900 may operate in the capacity of a server in a client-server network environment. The computing device 900 may be a personal computer (PC), a set-top box (STB), a server, a network router, switch or bridge, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by the device. Furthermore, although only a single exemplary computing device is shown, the term "computer" shall also be taken to include any collection of computers that individually or together execute a set (or sets) of instructions to perform any one or more of the methodologies described herein.

[0091] The exemplary computing device 900 may include a processing device 902 (also referred to as a processor or CPU), which may communicate with each other via a bus 930, a main memory 904 (e.g., read only memory (ROM), flash memory, dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM), etc.), a static memory 906 (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory (e.g., a data storage device 918).

[0092] The processing device 902 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, etc. More specifically, the processing device 902 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. The processing device 902 may also be one or more special-purpose processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. According to one or more aspects of the present disclosure, the processing device 902 may be configured to execute instructions implementing the method 800 illustrated in FIG. 8.

[0093] The exemplary computing device 900 may further comprise a network interface device 908, which may be communicatively coupled to a network 920. The exemplary computing device 900 may further comprise a video display 910 (e.g., a liquid crystal display (LCD), touch screen, or cathode ray tube (CRT)), an alphanumeric input device 912 (e.g., a keyboard), a cursor control device 914 (e.g., a mouse), and an acoustic signal generating device 916 (e.g., a speaker).

[0094] The data storage device 918 may include a machine-readable storage medium (or more specifically, a non-transitory machine-readable storage medium) 928 having stored thereon one or more sets of executable instructions 922. According to one or more aspects of the present disclosure, the executable instructions 922 may comprise executable instructions associated with performing the method 800 illustrated in FIG.

[0095] The executable instructions 922 may also reside, completely or at least partially, within the main memory 904 and / or within the processing device 902 during execution of the executable instructions 922 by the exemplary computing device 900, with the main memory 904 and the processing device 902 also constituting computer-readable storage media. The executable instructions 922 may further be transmitted or received over a network via the network interface device 908.

[0096] Although computer readable storage medium 928 is illustrated in FIG. 9 as a single medium, the term "computer readable storage medium" should be interpreted to include a single medium or multiple media (e.g., centralized or distributed databases and / or associated caches and servers) that store one or more sets of operating instructions. The term "computer readable storage medium" should also be interpreted to include any medium that is capable of storing or encoding a set of instructions for execution by a machine that causes the machine to perform any one or more of the methodologies described herein. The term "computer readable storage medium" should therefore be interpreted to include, but is not limited to, solid-state memories and optical and magnetic media.

[0097] Some portions of the above detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of widespread usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0098] It should be borne in mind, however, that all of these and similar terms are associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless otherwise indicated, and as will be apparent from the following description, descriptions utilizing terms such as "identifying," "determining," "storing," "adjusting," "causing," "returning," "comparing," "creating," "stopping," "loading," "copying," "throwing," "replacing," "executing," and the like throughout the description refer to the actions and processes of a computer system or similar electronic computing device that manipulate data represented as physical (electronic) quantities in the computer system's registers and memory, and that transform that data into other data that are similarly represented as physical quantities in the computer system's memory or registers, or other such information storage, transmission, or display device.

[0099] An example of the present disclosure also relates to an apparatus for carrying out the methods described herein. This apparatus may be specially constructed for the required purpose, or this apparatus may be a general-purpose computer system selectively programmed by a computer program stored in the computer system. Such a computer program may be stored in a computer-readable storage medium, such as any type of disk, including, but not limited to, optical disks, compact disk read-only memories (CD-ROMs), and magnetic optical disks, read-only memories (ROMs), random access memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic disk storage media, optical storage media, flash memory devices, other types of machine-accessible storage media, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.

[0100] The methods and displays presented herein are not inherently related to a particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will be described in the description that follows. Moreover, the scope of the disclosure is not limited to any particular programming language. It will be appreciated that a variety of programming languages ​​may be used to implement the teachings of the present disclosure.

[0101] The preceding description describes numerous specific details, such as examples of specific systems, components, methods, etc., to provide a good understanding of some embodiments of the present disclosure. However, it will be apparent to those skilled in the art that at least some embodiments of the present disclosure may be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in a simple block diagram format to avoid unnecessarily obscuring the present disclosure. Thus, the specific details described are by way of example only. It is contemplated that certain embodiments may vary from these illustrative details and still fall within the scope of the present disclosure.

[0102] References throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or property described with respect to that embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification do not necessarily all refer to the same embodiment. Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". When the term "about" or "approximately" is used herein, it is intended to mean that the nominal value presented is accurate to within ±10%.

[0103] Although the operations of the methods herein are shown and described in a particular order, the order of the operations of each method may be changed such that some operations may be performed in reverse order or some operations may be performed, at least in part, simultaneously with other operations. In another embodiment, separate process instructions or sub-operations may be in an intermittent and / or alternating manner.

[0104] It should be understood that the above description is illustrative, and not restrictive. Many other embodiments will be apparent to those of ordinary skill in the art upon reading and understanding the above description. The scope of the present disclosure should therefore be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. a processing chamber comprising a chamber body having a wall including a first material that encloses an interior space; a plasma source configured to expose a substrate disposed in the processing chamber to a plasma-related flux, the first material having a first set of recombination coefficients associated with the plasma-related flux; a second material disposed along a first region of the chamber body, the first material having a second set of plasma recombination coefficients associated with the plasma-related flux, the second set of plasma recombination coefficients being different from the first set of plasma recombination coefficients; A plasma processing system comprising:

2. a third material disposed along a second region of the chamber body, the third material having a third set of plasma recombination coefficients associated with the plasma-related flux, the second set of plasma recombination coefficients being different from the first set of plasma recombination coefficients and the second set of plasma recombination coefficients. The plasma processing system of claim 1 , further comprising:

3. one or more of the plasma recombination coefficients of the second set are greater than a corresponding plasma recombination coefficient of the first set; one or more of the plasma recombination coefficients of the third set are less than a corresponding plasma recombination coefficient of the first set; 3. The plasma processing system of claim 2.

4. the chamber body further comprising a support structure for supporting the substrate; the second material is disposed along a surface of the support structure; 10. The plasma processing system of claim 1 .

5. 2. The plasma processing system of claim 1, wherein the second material is disposed along the first region in a plurality of concentric rings.

6. 10. The plasma processing system of claim 1, wherein the wall comprises quartz and the first material comprises at least one of a borosilicate based material, titanium, or stainless steel.

7. 10. The plasma processing system of claim 1, further comprising an actuator coupled to the second material, the actuator configured to vary a first distance between the second material and the chamber body.

8. 2. The plasma processing system of claim 1, wherein at least one of the first set of plasma recombination coefficients or at least one of the second set of plasma recombination coefficients is associated with silicon nitridation occurring in the processing chamber.

9. the processing chamber comprising an annular plasma injection site formed between a first radius and a second radius of a first surface of the chamber body, the annular plasma injection site configured to deliver plasma from the plasma source to the interior volume of the processing chamber; the first material is disposed along the first surface of the chamber body within the first radius; 10. The plasma processing system of claim 1 .

10. acquiring a process result profile of a first substrate, the process result profile including a plurality of thickness values ​​of the first substrate measured after processing the first substrate in a processing chamber having a chamber body including a wall comprising a first material having a first set of plasma recombination coefficients; determining that the process result profile includes a first thickness value for a first location on the first substrate that deviates from a first reference thickness value; in response to determining that the process result profile includes the first thickness value that deviates from the first reference thickness value, determining a second material having a second set of plasma recombination coefficients different from the first set of plasma recombination coefficients and a second location along the chamber body proximate to the first location on the first substrate; processing a second substrate in the processing chamber with the second material disposed along the chamber body at the second location; A method comprising:

11. determining that the process result profile includes a second thickness value for a third location on the first substrate that deviates from a second reference thickness value; in response to determining that the process result profile includes the second thickness value that deviates from the second reference thickness value, determining a second material including a third set of plasma recombination coefficients different from the first set of plasma recombination coefficients and a fourth location along the chamber body proximate to the third location; 11. The method of claim 10, further comprising: wherein the second substrate is processed with the second material disposed along the chamber body at the fourth location.

12. one or more of the plasma recombination coefficients of the second set are greater than a corresponding plasma recombination coefficient of the first set; one or more of the plasma recombination coefficients of the third set are less than a corresponding plasma recombination coefficient of the first set; The method of claim 11.

13. The method of claim 10 , wherein the second material is disposed along the chamber body in a configuration having concentric rings.

14. causing operation of an actuator to vary a first distance between the second material and the chamber body to position the second material at the second location. The method of claim 10 further comprising:

15. using the process result profile as an input to a machine learning model; obtaining one or more outputs of the machine learning model, the one or more outputs being indicative of the second material and the second location; The method of claim 10 further comprising:

16. The method of claim 10 , further comprising: effecting heating or cooling of the second material to change the second plasma recombination coefficient to a third plasma recombination coefficient.

17. a chamber body having a wall including a first material enclosing an interior space, the first material having a first set of plasma recombination coefficients; a second material disposed along a first region of the chamber body, the first material having a second set of plasma recombination coefficients different from the first set of plasma recombination coefficients; a third material disposed along a second region of the chamber body, the third material having a third set of plasma recombination coefficients different from the first set of plasma recombination coefficients and the second set of plasma recombination coefficients; and A processing chamber apparatus comprising:

18. one or more of the plasma recombination coefficients of the second set are greater than a corresponding plasma recombination coefficient of the first set; one or more of the plasma recombination coefficients of the third set are less than a corresponding plasma recombination coefficient of the first set; 20. The processing chamber apparatus of claim 17.

19. the chamber body further comprising a support structure for supporting a substrate; the second material is disposed along a surface of the support structure; 20. The processing chamber apparatus of claim 17.

20. 20. The processing chamber apparatus of claim 17, wherein at least one of the first set of plasma recombination coefficients or the second set of plasma recombination coefficients is associated with silicon nitridation occurring in the processing chamber apparatus.

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