Plasma processing system and processing chamber apparatus

Abstract

A system, method, and apparatus for processing a substrate. A plasma processing system includes a processing chamber having a chamber body with walls enclosing an internal volume, the walls being made of a first material. The plasma processing system further includes a plasma source designed to expose a substrate disposed within the processing chamber to a plasma-related flux. The first material has a first set of recombination coefficients related to 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 related to the plasma-related flux. The second set of plasma recombination coefficients differs from the first set of plasma recombination coefficients.

Description

[Technical Field]

[0001] This specification relates to methods and systems for controlling plasma treatment. Specifically, this specification relates to the uniformity control of plasma treatment using plasma species composites on walls (e.g., plasma free radical wall composites). [Previous Technology]

[0002] Plasma processing is widely used in the semiconductor industry. Plasma can modify the chemical properties of the processing gas (e.g., generate ions, free radicals, etc.), creating new species without temperature-dependent limitations, thereby generating ion flux to the wafer with energies ranging from a few tenths of an electron volt (eV) to thousands of electron volts. Various plasma sources are available (e.g., capacitively coupled plasma (CCP), inductively coupled plasma (ICP), microwave-generated plasma, electron cyclotron resonance (ECR), etc.), covering a wide range of operating processes from a few millitors to several tors.

[0003] The commonly used plasma process specification today is a high degree of uniformity in the processed product (e.g., uniformity across the entire wafer up to the wafer edge). For example, the uniformity requirements in semiconductor manufacturing today might include a tolerance of approximately 1%-2% across the entire wafer, but excluding a tolerance of 1-3mm at the edges. These stringent constraints become even more stringent as researchers seek new methods to control process uniformity and / or discover improvements to existing methods for controlling process uniformity. Different uniformity control methods may be effective for some processes but completely ineffective for others. [Summary of the Invention]

[0004] The following is a simplified overview of this disclosure to provide a basic understanding of some aspects of this disclosure. This disclosure is not a comprehensive summary of this disclosure. It is not intended to identify key or essential elements of this disclosure, nor to depict any category of specific embodiments of this disclosure or any category of the scope of the claims. Its sole purpose is to present some concepts of this disclosure in a simplified form as an introduction to the more detailed description that follows.

[0005] In an exemplary embodiment, the plasma processing system includes a processing chamber comprising a chamber body having a first wall material enclosing an internal volume, the first wall material having a first set of composite coefficients for a set of plasma species. The plasma processing system further includes a plasma source configured to expose a substrate disposed within the processing chamber to plasma-related flux. The plasma processing system may further include a second material disposed along a second region of the chamber body, the second material having a second set of composite coefficients related to the plasma-related flux. The second set of plasma composite coefficients differs from the first set of plasma composite coefficients.

[0006] In an exemplary embodiment, a method includes obtaining a processing result distribution of a first substrate. The processing result distribution 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, the walls of the chamber body having a first set of plasma recombination coefficients. The method further includes determining a first thickness value on the first substrate at a location deviating from a first reference thickness value in the processing result distribution. The method further includes 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 adjacent 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 processing result distribution includes a first thickness value deviating from a reference thickness value. The method further includes processing a second substrate within a processing chamber, wherein the second material is disposed along the chamber body at the second location.

[0007] In an exemplary embodiment, the processing chamber device includes a chamber body having walls that enclose an internal volume, the walls being made of a first material. The first material has a first set of plasma species recombination coefficients. The processing chamber device 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 different from the first set of plasma recombination coefficients. The processing chamber device 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 different from both the first and second sets of plasma recombination coefficients.

Implementation Method

[0019] A commonly used plasma process parameter today is the high uniformity of the processed product (e.g., uniformity throughout the wafer up to the wafer edge). This parameter is often difficult to achieve because it involves many factors, many of which can interfere with each other. Plasma uniformity, chamber design, wafer temperature distribution, and bias electrode design are only some of these factors. The highest level of processing uniformity is achieved through the fabrication and assembly of radio frequency (RF) antennas and processing chambers.

[0020] One factor affecting the uniformity of processing within the processing chamber is the wall recombination of free radicals in the chamber body. Conventional systems typically ignore the effect of wall recombination on plasma processing unless the recombination coefficient on the walls is a few percent or higher. For certain processes, especially slow ones, the effect of wall recombination of reactive species is greater than previously thought, even if the recombination coefficient is much less than one percent. Manipulating the surface material within the chamber body of the processing chamber can alter the processing distribution by changing the plasma recombination rate along the surface of the chamber body. For example, selecting surface materials, placing pads or other elements made of materials with selected properties, and / or using films can have specific effects on portions of the processing distribution. Furthermore, the placement of each material within the chamber affects different processing results on the surface of the treated substrate. Because the recombination coefficient of ions is constant (e.g., approximately 1), wall recombination can be effective for processes where free radicals reacting with the substrate have a significant or dominant effect.

[0021] This disclosure provides methods, systems, and apparatus for improving control over plasma processing within a processing chamber. For example, as described herein, methods, systems, and apparatus utilize various materials having plasma recombination coefficients with multiple sets of plasma radicals within the chamber to modify (e.g., correct) defects in the plasma process. For example, a first material may be placed adjacent to the substrate edge and improve processing uniformity defects appearing towards the substrate edge region. This disclosure introduces a novel processing chamber surface material configuration, in some embodiments of which includes actuators, couplers, or other means for placing materials with different plasma recombination rates to selectively modify plasma processing outcomes (e.g., improve uniformity across the entire wafer). In some embodiments, this disclosure (e.g., using modeling techniques) identifies materials and locations within the processing chamber to place materials to improve processing outcomes (e.g., more precisely meet processing outcome requirements or reference processing outcome distributions).

[0022] In an exemplary embodiment, the plasma processing system includes a processing chamber comprising a chamber body having walls having an enclosed internal volume, the walls having a first material having a first set of plasma recombination coefficients. The plasma processing system further includes a plasma source configured to expose a substrate disposed within the processing chamber to plasma-related flux. The first set of plasma recombination coefficients is related to 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 related to the plasma-related flux. The second set of plasma recombination coefficients differs from the first set of plasma recombination coefficients.

[0023] In an exemplary embodiment, a method includes obtaining a processing result distribution of a first substrate. The processing result distribution 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 and walls having a first material having a first set of plasma recombination coefficients. The method further includes determining a first thickness value on the first substrate at a first location that deviates from a first reference thickness value in the processing result distribution. The method further includes determining a second material and a second location having a second set of plasma recombination coefficients different from the first set of plasma recombination coefficients, and the second location being along the chamber body adjacent to the first location on the first substrate. The second material and the second location are determined in response to determining that the first processing result distribution includes a first thickness value that deviates from the first reference thickness value. The method further includes processing a second substrate within a processing chamber, wherein the second material is disposed at the second location along the chamber body.

[0024] In an exemplary embodiment, the processing chamber device includes a chamber body having a wall enclosing an internal volume, the wall having a first material. The processing chamber device further includes a second material disposed along a first region of the chamber body. The first material has a second set of plasma composite coefficients different from the first set of plasma composite coefficients. The processing chamber device further includes a third material disposed along a second region of the chamber body. The third material has a third set of plasma composite coefficients different from both the first and second sets of plasma composite coefficients.

[0025] Figure 1 illustrates a processing system 100 according to 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., maintained at atmospheric pressure), a gas inlet 112, and a gas distribution volume defined by the wall. The processing chamber 120 includes a wall 111, which is internally maintained under vacuum and provides support for the plasma source 110, a substrate support 116, and a gas outlet 114. The gas inlet 112 and gas outlet 114 may provide a feed gas flow through the processing system at a processing gas pressure. The feed gas may include any one 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 deliver (e.g., generate or facilitate inflow) plasma into the processing chamber 120. The plasma source delivers plasma through plasma injection points 118A-B. Processing chamber 120 accommodates substrate 130 to be processed by processing system 100. Processing system 200 may be a plasma chamber, including etching chambers and deposition chambers (including atomic layer deposition, chemical vapor deposition, and physical vapor deposition). For example, plasma chambers may be chambers used for plasma etchers, plasma cleaners, etc.

[0026] In some embodiments, as shown in Figure 1, plasma may be injected into the processing chamber 120 through an annular opening (e.g., an annular plasma injection point). In some embodiments, the processing system 100 may include other plasma injection configurations, such as openings using circular, linear, and / or other geometries. In another embodiment, plasma may be injected into the processing chamber 120 using multiple plasma injection points, each plasma injection point including one or more previously described geometric configurations or other configurations not described herein.

[0027] The processing system 100 includes a first surface material structure. In some embodiments, the wall 111 of the processing system 100 has a material containing a set of composite coefficients related to the reaction rate and combination of free radicals (e.g., nitrogen atoms N combine to form nitrogen molecules N2). In some embodiments, the processing apparatus may include one or more surface materials (e.g., pads, films, plates, etc.) having a set of composite coefficients different from those of the wall 111. In some embodiments, the processing system 100 includes an initial surface material configuration or an uncorrected surface material configuration that can be used to process a substrate and obtain (e.g., using substrate measurement) an initial processing result distribution (e.g., processing result distribution 300 in Figure 3A). By updating the initial surface material configuration to a corrected or updated surface material configuration (e.g., adding surface material, positioning or repositioning surface material, etc.), the initial processing result can be further used to refine the processing result distribution (e.g., improve processing uniformity). The processing system 100 can be further used to process new substrates to obtain (e.g., using substrate measurement) an updated processing result distribution.

[0028] Figure 2 illustrates a processing system 200 according to certain embodiments, having a chamber body with a surface material configuration. The processing system 200 may include a processing chamber 220 and a plasma source 210. The plasma source includes a wall 202 (e.g., maintaining atmospheric pressure), a gas inlet 212, and a gas distribution volume defined by the wall. The processing chamber 220 includes a wall 211 that maintains 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 conjunction with the processing chamber in other embodiments. The gas inlet 212 and gas outlet 214 may provide a feed gas flow through the processing system at a processing gas pressure. The feed gas may include any one of air, O2, N2, Ar, NH3, He, and / or other suitable processing gases. The plasma source 210 may include a gas expansion volume of a gas injector (e.g., without plasma). Plasma source 210 is designed to deliver plasma to processing chamber 220 (e.g., generate plasma or facilitate plasma flow into processing chamber 220) and process substrate 230 disposed within processing chamber 220.

[0029] In some embodiments, as shown in Figure 2, plasma can be injected into the processing chamber 220 through an annular opening. In some embodiments, the processing system 200 may include other plasma injection configurations, such as openings using circular, linear, and / or other geometries. In another embodiment, plasma may be injected into the processing chamber 220 using multiple plasma injection points, each plasma injection point including one or more previously described geometric configurations or other configurations not described herein.

[0030] As shown in Figure 2, the processing system 200 includes a surface material configuration. In some embodiments, the wall 211 of the processing chamber 220 has a material having a composite coefficient related to the reaction rate and combination of free radicals (e.g., nitrogen atoms N combine to form nitrogen molecules N2). In some embodiments, the processing apparatus may include one or more surface materials 232A-C (e.g., pads, films, plates, etc.) having a composite coefficient different from that of the wall 211.

[0031] The surface material may include a pad or other element made of a material having selected properties (e.g., related to a set of plasma species recombination rates or coefficients). In some embodiments, the material may be (e.g., as a membrane) disposed on the chamber body. The surface material may have a first set of plasma recombination coefficients (e.g., each coefficient is related to various plasma species). The plasma recombination coefficients may include the rate at which reactive plasma species recombine or interact at or near the surface of the chamber body 220. The plasma recombination coefficients may correspond to the plasma or plasma-related flux generated by the plasma source 210.

[0032] For many processes, especially slow processes (e.g., processes with a processing yield below the critical time limit, or processes whose total process or individual process steps (e.g., process steps) include processing times exceeding the time limit), wall recombination of reactive species plays a greater role, even when the recombination coefficient is much less than 1%. Therefore, manipulating the material of the chamber body surface of the processing chamber can alter the processing distribution. For example, selecting surface materials, placing pads or other elements made of materials with selected properties, or using films, can produce specific effects on certain portions of the processing distribution. Furthermore, the placement of each material within the chamber affects different processing results across the entire surface of the processed substrate.

[0033] As shown in Figure 2, the processing chamber 220 may include a first surface material 232A disposed along the wall 211 of the main body of the processing chamber 220. The first surface material 232A may include a material having a first set of recombination rates (e.g., recombination coefficients) lower than the recombination rate of the wall 211. The processing chamber 220 may include a second surface material 232B having a set of recombination rates (e.g., recombination coefficients) lower than the recombination rate of the wall 211. The processing chamber may include a third surface material, which may include a material having a recombination rate (e.g., a set of recombination coefficients) higher than the recombination rate of the wall. The surface material configuration shown in Figure 2 is purely exemplary and illustrates exemplary locations where surface materials 232A-C may be distributed within the processing chamber.

[0034] In some embodiments, surface materials 232A-C are disposed adjacent to regions of substrate 230 and can affect the local processing results of substrates adjacent to surface materials. For example, as shown in Figure 2, surface materials 232A and 232B are disposed within processing chamber 220 near the edge of substrate 230. Based on the relative composite of the materials, surface materials 232A-B affect the processing results of substrate edge regions. For example, in embodiments where surface materials 232A-B have one or more composite coefficients higher than the corresponding composite coefficient of wall material 211, adding surface materials 232A-B improves the processing results near the edge of substrate 230 when the substrate is processed within processing chamber 220. In another example, in embodiments where surface materials 232A-B have one or more composite coefficients lower than the composite coefficient of wall material 211, adding surface materials 232A-B reduces the processing results near the edge of substrate 230 when the substrate is processed within processing chamber 220.

[0035] In another example, surface material 232C is disposed along wall 211 in a location adjacent to the central region of substrate 330 and may affect the processing results of substrate 230 at and / or near the center of substrate 230. For example, in an embodiment where surface material 232C has a higher composite coefficient than the material of wall 211 (e.g., and / or support structure 216), adding surface material 232C improves the processing results near the center of substrate 230 processed within processing chamber 230. In another example, in an embodiment where surface material 232C has a lower composite coefficient than the material of wall 211 (e.g., and / or support structure 216), adding surface material 232C reduces the processing results near the center of substrate 230 when the substrate is processed in processing chamber 220.

[0036] In some embodiments, the surface material may be movable within a processing chamber. For example, the processing chamber may include actuators coupled to surface materials 232A-C. Surface materials 232A-C may be coupled to wall 211 and positioned at a distance from wall 211. The actuators may be used to change the distance between surface materials 232A-C. For example, the surface material may be coupled to a translational platform fixed (e.g., fastened and / or adhered) to wall 211. The actuators may translate the surface material to bring it closer to or further away from the substrate. For example, surface materials 232A and 232C may be translated to be lowered or raised, thereby bringing them closer to or further away from substrate 230. In some embodiments, surface materials 232A-C are substantially parallel to one or more of the walls 211.

[0037] In some embodiments, the substrate support structure 216 is movable (e.g., translated) to move the substrate closer to one or more surface materials 232A-C. For example, the substrate support structure 216 may be translated upward or downward to raise or lower the substrate and increase or decrease the distance between the substrate and the surface materials.

[0038] In some embodiments, in addition to or instead of using a liner and / or membrane deployment surface material, the surface material may be deployed as a foldable diaphragm structure within the processing chamber. The foldable diaphragm structure may include a plurality of movable interceptor plates or blades that can alter the shape of the structure (e.g., increase / decrease the exposed surface area), thereby having a greater or lesser impact on the processing outcome of the region adjacent to the foldable diaphragm. In some embodiments, the processing chamber includes a mechanism (e.g., a translation plate, a rotation 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 a translation plate.

[0039] In some embodiments, the processing chamber includes mechanisms for heating and / or cooling the surface materials 232A-C within the processing chamber. Heating and / or cooling of the surface materials 232A-C can alter one or more associated composite coefficients of the surface materials. Heating and / or cooling can expand the available range of composite coefficients (e.g., without changing the manufacturing equipment).

[0040] In some embodiments, the chamber body includes a wall 211 having one or more of a first material and surface materials 232A-C, the first material having a first set of plasma recombination coefficients (e.g., for various plasma species), and one or more of the 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 two or more materials having multiple recombination coefficients may be used along the entire internal volume of the chamber body. In some embodiments, the surface materials 232A-C may be disposed along or adjacent to the substrate support assembly 216.

[0041] In some embodiments, the recombination coefficient of the surface materials 232A-C and / or the wall 211 of the chamber body may be related to the silicon nitriding that occurs within the processing chamber 220. Silicon nitriding uses nitrogen atoms N (free radicals), which are typically obtained in plasma discharge through the dissociation of nitrogen molecules N2. Nitriding can be a slow process, so the free radical flux and its distribution on the wafer may be entirely defined by faster processes, such as free radical generation, free radical flow from the generation region (e.g., plasma source 210) to the exhaust port (e.g., exhaust port 214), and free radical diffusion to and recombination on the walls (including the substrate). Placing materials (e.g., surface materials 232A-C) in regions where the recombination rate of certain plasma species is high or low (e.g., along the surface of the chamber body) can alter the local recombination rate of free radicals (e.g., for various plasma species) and obtain a processed substrate with improved uniformity (e.g., a substrate with smaller processing deviations, such as film thickness across the entire substrate surface).

[0042] In some embodiments, portions of the wall 211 of the chamber body may include areas or portions of a surface having a material with high and / or low recombination rates for various plasma species. A first surface material 232A having a low recombination coefficient (e.g., a good reactant of free radicals, i.e., less than the value of the wall 211 of the chamber body) may be disposed near one or more regions on the substrate where the processing value (e.g., film thickness, critical dimension, etc.) is lower than a reference processing result (e.g., desired thickness or processing result uniformity). For example, if the processing distribution (e.g., thickness distribution) of a substrate processed in a manufacturing chamber with quartz walls has a relatively high (e.g., thicker) central region and a lower (e.g., thinner) edge region, a surface material with a low recombination rate may be disposed to cover the walls (e.g., pads, films, 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, borosilicate coatings, and boron nitride films can have nitrogen recombination coefficients that are 3-10 times lower than those of quartz typically used for chamber walls. A different material, such as titanium or stainless steel, with a nitrogen recombination coefficient 3-10 times higher than that of quartz, can be placed near the wafer center to reduce processing results (e.g., thickness) near the center.

[0043] In some embodiments, plasma and flow simulations may be used to determine the size and location of surface materials 232A-C to set up manufacturing chamber 100 for processing the substrate. This results in a higher level of processing uniformity than alternative surface material configurations and / or the absence of surface material 126. In embodiments, surface material 128 may include discs, rings, coatings, films, and / or other components.

[0044] Figure 3A illustrates a processing result distribution 300 according to certain embodiments. The processing result distribution may include an initial processing distribution or an uncorrected processing result distribution of a substrate processed within a processing system (e.g., a plasma source, processing chamber, etc.) (e.g., using the surface material configuration shown in Figure 1 and discussed in the corresponding description). The processing result distribution may plot processing result parameters (e.g., thickness, critical dimensions, etc.). A first axis 304 is associated with a position on the wafer surface. For example, the processing distribution may be measured radially from a first edge to a second edge, and this processing distribution approximately travels through the center of the substrate. A second axis 302 represents a processing result value (e.g., a thickness value). In some embodiments, this value may be normalized or otherwise displayed as a relative value of the processing result (e.g., a percentage of the maximum value) relative to another value or reference value.

[0045] The processing result distribution 300 may indicate the portion of the processing result that is greater than (e.g., thicker than) and / or less than (e.g., thinner than) a threshold processing result value (e.g., average processing result value, processing control limit, statistical values ​​such as deviation or variation). For example, a first region 308 of the processing result distribution 300 represents a region on the substrate surface (e.g., the center of the substrate). As shown in Figure 3A, the processing result distribution 300 indicates that the first region 308 includes processing result values ​​greater than the average processing result. The values ​​within the first region 308 may be reduced to reduce variation among other processing result values ​​in other regions of the substrate (e.g., improve processing uniformity). A second region 306 of the processing result distribution 300 represents a region on the substrate surface (e.g., the edge of the substrate). As shown in Figure 3A, the processing result distribution 300 indicates that the second region 306 includes processing result values ​​less than the average processing result. The values ​​within the second region 306 may be increased to reduce variation among other processing result values ​​in other regions of the substrate (e.g., improve processing uniformity).

[0046] As further discussed in the embodiments, different surface material configurations (e.g., materials with different composite coefficient groups disposed at different locations within the processing chamber) can be deployed during substrate processing and can affect the processing results (e.g., thickness) across the entire substrate surface. For example, different surface material configurations can increase the processing result of the second region 306 and decrease the processing result value of the first region 308, as shown in Figure 3A (e.g., to improve processing uniformity).

[0047] Figure 3B illustrates a processing result distribution 350 according to certain embodiments. The processing result distribution may include a modified or altered processing result distribution of a substrate processed within a processing system (e.g., a plasma source, processing chamber, etc.) (e.g., using the surface material configuration shown in Figure 2, discussed in the corresponding description). The processing result distribution may plot processing result parameters (e.g., thickness, critical dimensions, etc.). A first axis 354 is associated with a position on the wafer surface. For example, the processing distribution may be radially truncated from a first edge to a second edge and may travel near the center of the substrate. A second axis 352 indicates a processing result value (e.g., a thickness value). In some embodiments, this value may be normalized or otherwise displayed as a relative value of the processing result (e.g., a percentage of the maximum value) relative to another value or reference value. The processing result distribution 350 may include one or more features and / or patterns of the processing result distribution 250.

[0048] As further discussed in other embodiments, different surface material configurations (e.g., materials with different recombination coefficients placed at different locations within the processing chamber) can be deployed during substrate processing and can affect the processing result values ​​of the substrate. For example, comparing processing result distribution 350 with processing result distribution 250 of Figure 2B, different surface material configurations can increase the processing result value of the second region 256 to the updated second region 356 and decrease the processing result value of the first region 258 to the updated first region 358. The following corrections can be made in response to the use of surface material configurations as shown and described in conjunction with Figure 2. Processing result distributions 250 and 350 are merely used to illustrate how recombination configurations (e.g., placement of materials with different recombination rates) can be updated within the chamber to process the substrate, thereby producing a generally more uniform processing result.

[0049] Figure 4 is a top view of a substrate support structure 400 according to certain embodiments. As shown in Figure 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 walls 211 and 311 in Figures 2 and 3, 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 patterns of surface materials 332A-C. The surface material 404 may have a second set of recombination rates 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.

[0050] In some embodiments, the surface material 404 is disposed as a single disc having a hollow center designed to fit the substrate 406. In some embodiments, the surface material 404 is disposed as a plurality of discs or rings. For example, the surface material 404 may be disposed as a concentric ring centered on 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 to the edge of the support surface 402 and / or the edge of the substrate 406; 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.

[0051] Figure 5 shows a bottom view of a chamber body 500 within a processing chamber including a plasma injection point 504 according to certain embodiments. As shown in Figure 5, the chamber body 500 includes wall structures 504A-C, surface materials 502A-B, and plasma injection points 506. The wall structures 504A-C may include a first material having a first set of composite coefficients. The wall structures 504A-C form openings to create the plasma injection points 506. The plasma injection points 506 are designed to deliver (e.g., facilitate inflow) plasma into the processing chamber. In some embodiments, the plasma injection point 506 may include an annular opening. In some embodiments, the chamber body 500 may include other plasma injection configurations, such as openings using circular, straight, and / or other geometries. In another embodiment, a plurality of plasma injection points may be used to inject plasma into the processing chamber, each plasma injection point including one or more previously described geometric configurations or other configurations not described herein.

[0052] In some embodiments, the chamber body 500 includes a first surface material 502A disposed along a wall structure 504A within a first region of the chamber body 500, the wall structure 504A being located within the plasma between openings of the plasma injection points 506. In some embodiments, the first surface material 502A is disposed in a circular configuration; in other embodiments, the first surface material 502A is disposed as a set of concentric rings or discs. In some embodiments, the surface material 502A covers the entire first region of the chamber body 500 (e.g., the edge adjacent to the plasma injection point).

[0053] In some embodiments, the chamber includes a second region disposed between the wall structures 504B-C. This second region may be disposed outside the radius or periphery of the plasma injection point 506 to the edge of the chamber body 500. The chamber body may include a second surface material 502B disposed within the second region along the wall structures 504B-C. The second surface material 502B may have a different set of composite coefficients than the wall structures 504A-C. In some embodiments, the first surface material 502A has the same composite coefficient as the second surface material 502B. In some embodiments, one or both of the composite coefficients of the first surface material 502A or the second surface material 502B may be greater than or less than the corresponding composite coefficients of the materials of the wall structures 504A-C.

[0054] Figure 6 is a block diagram illustrating an exemplary system architecture 600 in which embodiments of this disclosure may operate. Manufacturing chamber 100 includes a client device 620, manufacturing equipment 624, metering equipment 628, server 612, and data storage 640. Server 612 may be part of modeling system 610. Modeling system 610 may further include server machines 670 and 680.

[0055] Manufacturing equipment 624 (e.g., in relation to the production of corresponding products (such as wafers) through manufacturing equipment 624) may include one or more processing chambers 626.

[0056] The client device 620, manufacturing equipment 624, metering equipment 628, server 612, data storage 640, server machine 670 and server machine 680 can be coupled to each other through network 630 for modeling plasma composite and determining composite configuration (e.g., for improving the processing uniformity of substrate processing in processing chamber 626).

[0057] In some embodiments, network 630 is a public network that provides client device 620 with access to server 612, data storage 640, and / or other publicly available computing devices. In some embodiments, network 630 is a private network that provides client device 620 with access to manufacturing equipment 624, metering equipment 628, data storage 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), 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.

[0058] The client device 620 may include computing devices such as personal computers (PCs), laptops, mobile phones, smartphones, tablets, netbooks, network-connected televisions ("smart TVs"), network-connected media players (e.g., Blu-ray players), video converters, over-the-top (OTT) streaming devices, controller boxes, etc. The client device 620 may include a composite component 622. The composite component 622 may receive data, such as processing result data, from the metering device 628 and display the processing result data on the client device (e.g., in the form of processing result distributions (e.g., processing result distributions 250 and 350 for Figures 2B and 3B, respectively)). The composite component 622 may interact with one or more elements of the modeling system 610 to determine one or more configurations (e.g., materials with different composite coefficient groups and the locations where the materials will be placed) of the surface material of the substrate to be placed in the processing chamber 626 to process the substrate that meets the threshold value criteria (e.g., processing uniformity requirements).

[0059] The data storage device 640 may be a memory (e.g., random access memory), a drive (e.g., a hard disk drive, a flash memory drive), a database system, or another type of component or device capable of storing data. The data storage device 640 may store one or more historical data 642, including processing result data 644 and / or surface material configuration data 646. In some embodiments, the historical data 642 may be used to train, validate, and / or test the machine learning model 690 of the modeling system 610.

[0060] The modeling system 610 may include one or more computing devices, such as rack-mount servers, router computers, server computers, personal computers, mainframe computers, laptop computers, tablet computers, desktop computers, etc. In some embodiments, the modeling system 610 may include a prediction component 616. The prediction component 616 may acquire data from a metering device 628 to generate composite configuration data. The prediction component receives metering data from the metering device 628. The metering data may include the distribution of processing results associated with a substrate processed in the processing chamber 626. The prediction component determines (e.g., using model 690) the composite configuration. The composite configuration may include one or more materials having a set of composite coefficients, which are disposed in predetermined locations within the processing chamber 626. For example, a substrate processed in a processing chamber having surface materials disposed according to the composite configuration may produce a processed substrate having processing results that meet threshold criteria (e.g., processing uniformity requirements).

[0061] In some embodiments, the prediction component 616 may use historical data 642 to determine a composite configuration that, when applied to a processing chamber, causes the substrate processed in that chamber to meet a threshold criterion (e.g., processing uniformity requirements). In some embodiments, the prediction component 616 may use a model 690 (e.g., a trained machine learning model) to identify composite configurations that, when used in a processing chamber, produce processing results that satisfy threshold conditions (e.g., processing uniformity requirements). Model 690 may use historical data to determine the composite configuration.

[0062] In some embodiments, the modeling system 610 further includes server machines 670 and 680. Server machines 670 and 680 may be one or more computing devices (such as rack servers, router computers, server computers, personal computers, mainframe computers, laptop computers, tablet computers, desktop computers, etc.), data storage (e.g., hard disks, memory databases), networks, software components, or hardware components.

[0063] Server machine 670 may include dataset generator 672, which is capable of generating datasets (e.g., a set of data inputs and a set of target outputs) to train, validate or test machine learning models.

[0064] Server machine 680 includes a training engine 682, a validation engine 684, and a testing engine 686. The training engine 682 can train a model 690 (e.g., a machine learning model) using one or more processing result data 644 and surface material configuration data 646. The validation engine 684 can determine the accuracy of each model 690 based on the corresponding feature set of each training set. The validation engine 684 can discard models 690 whose accuracy does not meet the threshold accuracy. The testing engine 686 can determine the model 690 with the highest accuracy among all trained machine learning models based on the test (and, if applicable, validation) set.

[0065] In some embodiments, training data is provided to train model 690 such that the trained machine learning model will receive new inputs having new metrics including the distribution of processing results, and generate new outputs based on the new inputs, the new outputs indicating new composite configurations, wherein the new composite configurations indicate at least one new surface material (e.g., having a composite coefficient) and the location in which the new material will be placed within the processing chamber, such that the processing chamber of the substrate processed with the new composite configuration produces a substrate having a substrate that meets a threshold value criterion (e.g., processing uniformity requirements).

[0066] Model 690 may refer to a model created by training engine 182 using a training set, which includes data inputs and corresponding target outputs (historical results of cell culture under parameters associated with the target inputs). Patterns in the data set that map the data inputs to the target outputs (e.g., identifying a relationship between portions of cell growth data and the yield of the target product) can be found, and mappings capturing these patterns are provided to machine learning model 690. Machine learning model 690 may use one or more of logistic regression, parsing, decision trees, or support vector machines (SVMs). Machine learning may consist of single-level linear or nonlinear operations (e.g., SVMs) and / or may be neural networks.

[0067] Confidence data may include or indicate the confidence levels of one or more composite configurations that, when the substrate is processed according to the composite configuration, will result in the substrate having a processing result that meets a threshold value criterion (e.g., processing uniformity requirement). In a non-limiting example, the confidence level is a real number between 0 and 1 (inclusive), where 0 indicates no confidence in one or more specified actions, and 1 indicates absolute confidence in the specified actions.

[0068] For illustrative and not limiting purposes, this disclosure describes various aspects of training machine learning models and using trained learning models by employing information related to historical data 642. In other embodiments, heuristic or rule-based models are used to determine prescribed actions. In some embodiments, model 690 includes physics-based elements or derives predictions through physics-based principles. For example, model 690 may include a physics-based model based on plasma and flow equations, principles, and / or simulations.

[0069] In some embodiments, the functionality of client device 620, server 612, data storage 640, and modeling system 610 may be provided by a fewer number of machines than those shown in Figure 6. For example, in some embodiments, server machines 670 and 680 may be integrated into a single machine, while in some other embodiments, server machines 670 and 680 and server 612 may be integrated into a single machine.

[0070] Generally, functions described in one embodiment as being performed by the client device 620, data storage 640, metering system 628, manufacturing equipment 624, and modeling system 610 may also be performed on the server 612 in other embodiments (if applicable). Furthermore, functions belonging to a particular component may be performed by different or multiple components operating together.

[0071] In this embodiment, a "user" may be represented as a single individual. However, other embodiments of this disclosure include a "user" as an entity controlled by multiple users and / or automation sources. For example, a group of individual users who collectively become a group of administrators may be considered as a single "user".

[0072] Figure 7 illustrates a model training workflow 705 and a model application workflow 717 for surface material configuration (e.g., plasma composite configuration) according to an embodiment of this disclosure. In the embodiment, the model training workflow 705 may be executed on a server that may or may not include a composite configuration application, and the trained model is provided to the composite configuration application (e.g., on the client device 620 of Figure 6), which can execute the model application workflow 717. The model training workflow 705 and the model application workflow 717 may be executed by processing logic executed by the processor of a computing device. One or more of these workflows 705, 717 may be implemented, for example, by one or more machine learning modules implemented by the server 612 of Figure 6.

[0073] Model training workflow 705 is used to train one or more machine learning models (e.g., deep learning models) to perform one or more tasks related to the composite configuration predictor, such as classification, segmentation, detection, identification, and decision-making. Model application workflow 717 applies one or more trained machine learning models to perform tasks such as classification, segmentation, detection, identification, and decision-making to identify surface material configurations (e.g., plasma composite configurations). One or more of the machine learning models can receive and process result data (e.g., metrological data of processed wafers) and composite configuration data.

[0074] This document describes various machine learning outputs. Specific numbers and arrangements of machine learning models are described and shown. However, it should be understood that the number and type of machine learning models used, as well as the arrangement of these models, can be modified to achieve the same or similar final results. Therefore, the described and shown arrangements of machine learning models are merely examples and should not be construed as limitations.

[0075] 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 task or a subset of tasks. Additionally or alternatively, different machine learning models may be trained to perform different combinations of tasks. In one instance, one or a few machine learning models may be trained, wherein the trained ML model is a single common neural network with multiple common layers and multiple higher-level different output layers, wherein each output layer outputs different predictions, classifications, recognitions, etc. The tasks that one or more trained machine learning models may be trained to perform are as follows:

[0076] Composite Configuration Predictor: As previously described, the relationship between plasma composite configurations (e.g., the placement of surface materials disposed at predetermined locations along the surface of the chamber body and the determined plasma composite configuration) can be used to predict composite configurations that, when used within a processing chamber, result in a processing outcome that satisfies threshold criteria (e.g., processing uniformity requirements) on the substrate processed within the processing chamber. The composite configuration predictor receives data indicating the distribution of processing outcomes and outputs a first material having a first set of plasma composite coefficients and a first position along the chamber body adjacent to a corresponding location on the first substrate.

[0077] A machine learning model that can be used to perform some or all of the tasks described above is an artificial neural network, such as a deep neural network. Artificial neural networks typically include feature representation components with classifier or regression layers that map features to a desired output space. For example, a convolutional neural network (CNN) has multiple layers of convolutional filters. Polling is performed at the lower layers, which can handle non-linearity, and multiple perceptrons are typically added above these lower layers to map the top-level features extracted by the convolutional layers to a decision (e.g., classification output). Deep learning is a class of machine learning algorithms that use cascaded, multi-layered non-linear processing units for feature extraction and transformation. Each subsequent layer uses the output of 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 hierarchical structure, where different layers learn different levels of representation 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. Notably, the deep learning process can learn on its own which features are best placed at which level. In "deep learning," the "deep" refers to the number of transformation layers the data undergoes. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. CAP is the chain of transformations from input to output. CAP describes the underlying causal relationship between input and output. For feedforward neural networks, the CAP depth can be the network depth and can be the number of hidden layers plus one. For recursive neural networks where signals may propagate through more than one layer, the CAP depth can be infinite.

[0078] Training neural networks can be achieved through supervised learning, which involves: feeding the network a training dataset consisting of labeled inputs, observing its output, defining the error (by measuring the difference between the output and the labeled value), and using techniques such as deep gradient descent and backpropagation to tune the network's weights across all layers and nodes to minimize the error. In many applications, repeating this process with numerous labeled inputs in the training dataset produces a network that can produce the correct output when an input different from the inputs present in the training dataset appears.

[0079] For the model training workflow 705, a training dataset containing hundreds, thousands, tens of thousands, hundreds of thousands, or more processing result data 710 (e.g., processing result distribution, thickness distribution) should be used to form the training dataset. In embodiments, the training dataset may also include relevant composite configuration data 712 for forming the training dataset, wherein each data point and / or relevant composite configuration may include various labels or classifications of one or more classes of useful information. This data may be processed to generate one or more training datasets 636 for training one or more machine learning models.

[0080] In one embodiment, generating one or more training datasets 636 includes collecting one or more processing result measurements (e.g., metrological data) of processed substrates processed in chambers, with different composite configurations disposed on the chamber walls of the respective chambers.

[0081] To perform training, the processing logic inputs the training dataset 736 into one or more untrained machine learning models. The machine learning models can be initialized before the first input is input into them. The processing logic trains the untrained machine learning models based on the training dataset to produce one or more trained machine learning models that perform the various operations described above.

[0082] Training can be performed by inputting one or more of the processing result data 710 and composite configuration data 712 one at a time into a machine learning model. In some embodiments, training the machine learning model includes tuning the model to receive the processing result data 710 (e.g., processing result distribution, thickness distribution of the processed substrate) and output composite configuration predictions (e.g., one or more materials having a set of composite coefficients, and the corresponding one or more materials being placed at corresponding positions within the processing chamber). The machine learning model processes the inputs to produce an output. The artificial neural network includes an input layer consisting of values ​​from data points. The next layer is called a hidden layer, and each node in the hidden layer receives one or more input values. Each node contains parameters (e.g., weights) to be applied to the input values. Thus, each node essentially feeds the input values ​​into a multivariate function (e.g., a nonlinear mathematical transformation) to produce an output value. The next layer can 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 that equivalent value and then produces its own output value. This step can be performed at each layer. The final layer is the output layer, where each class, prediction, and / or output that the machine learning model can produce has a node.

[0083] Therefore, the output may include one or more predictions or inferences. For example, the output predictions or inferences may include a composite configuration of decisions. The processing logic may use the composite configuration to process the substrate and receive an updated thickness distribution. The processing logic may compare the updated thickness distribution with a target thickness distribution and determine whether a threshold criterion is met (e.g., the thickness value measured on the wafer surface falls within a target threshold window). The processing logic determines an error (i.e., classification error) based on the difference between the updated thickness distribution and the target thickness distribution. The processing logic adjusts the weights of one or more nodes in the machine learning model based on the error. An error term or increment 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 of one or more inputs to the node) for one or more of its nodes. The parameters may be updated in a backpropagation manner, such that the nodes of the highest layer are updated first, then the nodes of the next layer are updated, and so on. The artificial neural network contains multiple layers of "neurons," where each layer receives input values ​​from neurons in the previous layer. The parameters of each neuron include weights associated with the values ​​received from each neuron in the previous layer. Therefore, adjusting parameters may include adjusting the weights assigned to each input of one or more neurons in one or more layers of an artificial neural network.

[0084] Once the model parameters have been optimized, model validation can 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 can determine whether a stopping criterion has been met. The stopping criterion can be a target accuracy level, a target number of processed images from the training dataset, a target change in parameters on one or more previous data points, a combination of the above, 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 reached. The threshold accuracy can be, for example, 70%, 80%, or 90% accuracy. In one embodiment, the stopping criterion is met if the accuracy of the machine learning model has stopped improving. If the stopping criterion has not been met, further training is performed. If the stopping criterion has been met, training can be completed. Once the machine learning model has been trained, a reserved portion of the training dataset can be used to test the model.

[0085] As an example, in one embodiment, a machine learning model (e.g., a composite configuration predictor 767) is trained to determine composite configurations (e.g., the location of materials with multiple sets of composite coefficients and the materials to be arranged in the cavity to process the substrate to meet threshold criteria (e.g., processing uniformity requirements)). A similar process can be performed to train the machine learning model to perform other tasks, such as those described above. A large set of (e.g., thousands to millions) processing result distributions (e.g., thickness distributions) can be collected, and composite configurations (e.g., surface material configuration within the processing cavity) can be determined.

[0086] Once one or more trained machine learning models 738 are generated, they can be stored in model storage 745 and added to the composite configuration application. The composite configuration application can then use one or more trained ML models 738 and additional processing logic to implement an automatic mode in which user-inputted information is minimized or, in some cases, eliminated.

[0087] For the model application workflow 717, according to one embodiment, input data 862 may be input into a composite configuration predictor 767, which may include a trained neural network. Based on the input data 762, the composite configuration predictor 767 outputs information indicating the location where materials will be placed within the processing chamber (e.g., composite configuration data 769).

[0088] Figure 8 illustrates a flowchart of an example method 800 for predicting a composite configuration of a processing chamber according to some embodiments of this disclosure. Method 800 is performed by processing logic that may include hardware (e.g., circuitry, dedicated logic, etc.), software (such as software running on a general-purpose computer system or a dedicated machine), or any combination thereof. In one embodiment, the method is performed using the server 612 and model 690 of Figure 6, while in some other embodiments, one or more blocks of Figure 8 may be performed by one or more other machines not shown in the figure.

[0089] In block 802, the processing logic obtains a processing result distribution of a first substrate, the first substrate having a set of thickness values ​​of the first substrate measured after processing the first substrate in a processing chamber, the processing chamber having a chamber body, the walls of the chamber body having a first material, the first material having a first set of plasma recombination coefficients. The processing chamber may include one or more features and / or states of the processing systems 200, 300 of Figures 2A and 3A. The processing result distribution may include one or more features and / or states of the processing result distributions 250, 350 of Figures 2B and 3B.

[0090] In block 804, the processing logic determines that the processing result distribution includes a first thickness value at a first location on the first substrate, which deviates from a first reference thickness value. The reference thickness value may be associated with a processing result criterion (e.g., processing uniformity requirements). For example, the reference thickness may be an average thickness or a processing control limit associated with a processing chamber.

[0091] In block 806, the processing logic determines a first material having a second set of plasma recombination coefficients different from the first set of plasma recombination coefficients, and a second position along the chamber body adjacent to a first position on the first substrate. The processing logic may further determine the configuration of the first material. For example, the first material may be arranged in the processing chamber in the form of concentric rings or discs.

[0092] In another example, the first material may be disposed along the wall of the internal volume of the process, adjacent to the central region of a substrate disposed within the processing chamber, and may affect the processing 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 higher composite coefficients than the chamber wall material, adding the first material may improve the processing results near the center of the substrate being processed within the processing chamber. In another example, in an embodiment where the first material has one or more lower composite coefficients than the chamber wall material, adding the first material reduces the processing results near the center of the substrate when the substrate is processed in the processing chamber.

[0093] In some embodiments, the surface material may be movable within a processing chamber. For example, the processing chamber may include an actuator coupled to a first material. The first material may be coupled to a wall of the processing chamber and positioned at a distance from the wall. The actuator may be used to change the distance between the first material and the wall. For example, the first material may be coupled to a translational platform fixed (e.g., fastened and / or adhered) to the wall. The actuator may translate the surface material to bring it closer to or further away from the substrate.

[0094] In some embodiments, a liner and / or membrane may be used to deploy the first material within the processing chamber. In some embodiments, the first material may be disposed within the processing chamber as a foldable diaphragm structure. The foldable diaphragm structure may include a plurality of movable interceptor plates or blades that may alter the shape of the structure (e.g., increase / decrease the exposed surface area) to have a greater or lesser impact on the processing outcome of the region adjacent to the foldable diaphragm. In some embodiments, the processing chamber includes a mechanism (e.g., a translation plate, a rotation plate, etc.) for changing the position of the foldable diaphragm structure within the processing chamber.

[0095] In some embodiments, the processing chamber includes mechanisms for heating and / or cooling a first material within the processing chamber. Method 800 may further include heating and / or cooling the first material. The resulting heating and / or cooling may alter one or more composite coefficients of the first material. Heating and / or cooling can expand the available range of composite coefficients (e.g., without changing the first material).

[0096] In some embodiments, the processing logic further includes using a first processing result distribution as 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 indicate the first material and the second location. The machine learning model may include one or more features and / or patterns of model 690 in Figure 6.

[0097] In block 808, the processing logic determines, as appropriate, that the processing result distribution includes a second thickness value at a third location on the first substrate, which deviates from a second reference thickness. In block 910, the processing logic determines, as appropriate, a second material having a third set of plasma recombination coefficients different from the first set, and a fourth location. In some embodiments, one or more plasma recombination coefficients of the second set are greater than the corresponding plasma recombination coefficients of the first set and the plasma recombination coefficients of the third set. The plasma recombination coefficients of the third set are less than the corresponding plasma recombination coefficients of the second set.

[0098] In block 812, method 800 includes processing a second substrate within a processing chamber, wherein a first material is disposed at a second location along the chamber body. In some embodiments, various combinations of materials with different plasma recombination coefficient groups may be disposed at various points in various regions adjacent to the substrate within the processing chamber to affect the processing outcome of the substrate processed within the chamber having an associated surface material configuration.

[0099] Figure 9 illustrates a block diagram of an example computing device 900 capable of performing plasma delivery and / or processing operations according to one or more states of this disclosure. In various illustrative examples, various components of the computing device 900 may represent various components of the computing device (e.g., the modeling system 610 of Figure 6), in conjunction with the training engine, verification engine, and / or test engine described in Figure 6.

[0100] The example computing device 900 can be connected to other computer devices in a LAN, intranet, extranet, and / or the Internet. The computing device 900 can operate as a server in a client-server network environment. The computing device 900 can be a personal computer (PC), a set-top box (STB), a server, a network router, a switch, or a bridge, or any device capable of executing a set of instructions specifying the actions (sequence or other order) to be taken by the device. Furthermore, although only a single example computing device is shown, the term "computer" should also be understood to include any collection of computers that individually or jointly execute one (or more) sets of instructions to perform any one or more methods discussed herein.

[0101] Example computing device 900 may include processing device 902 (also referred to as processor or CPU), main memory 904 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM)), static memory 906 (e.g., flash memory, static random access memory (SRAM)), and auxiliary memory (e.g., data storage device 918), which can communicate with each other via bus 930.

[0102] Processing device 902 represents one or more general-purpose processing devices, such as microprocessors, central processing units, etc. More specifically, 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. Processing device 902 may also be one or more special-purpose processing devices, such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), network processors, etc. According to one or more embodiments of this disclosure, processing device 902 may be configured to execute instructions implementing method 800 shown in Figure 8.

[0103] The example computing device 900 may further include a network interface device 908 communicatively coupled to a network 920. The example computing device 900 may further include a video display 910 (e.g., a liquid crystal display (LCD), a touch screen, or a cathode ray tube (CRT)), an alphanumeric input device 912 (e.g., a keyboard), a cursor control device 914 (e.g., a mouse), and an audio signal generating device 916 (e.g., a speaker).

[0104] The data storage device 918 may include a machine-readable storage medium (or, more specifically, a non-transitory machine-readable storage medium) 928 thereon storing one or more sets of executable instructions 922. According to one or more embodiments of this disclosure, the executable instructions 922 may include executable instructions associated with performing the method 800 shown in Figure 8.

[0105] The executable instructions 922 may also reside wholly or at least partially in the main memory 904 and / or the processing device 902 during execution by the example computing device 900, which also constitute computer-readable storage media. The executable instructions 922 may further be sent or received over a network via a network interface device 908.

[0106] Although computer-readable storage medium 928 is shown as a single medium in Figure 9, the term "computer-readable storage medium" should be understood to include single 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 understood to include any medium capable of storing or encoding a set of instructions executable by a machine, which causes the machine to perform any one or more of the methods described herein. The term "computer-readable storage medium" should be correspondingly considered to include, but is not limited to, solid-state memory and optical and magnetic media.

[0107] Some parts of the above detailed description are presented based on algorithms and symbolic representations of operations on data bits within computer memory. These algorithmic descriptions and representations are means by which those skilled in the art of data processing most effectively communicate their work to others skilled in the art. Algorithms are generally considered herein as a self-consistent sequence of steps that produce the desired result. These steps require physical operations on physical quantities. Typically, although not always necessary, these quantities take the form of electrical or magnetic signals that can be stored, transmitted, combined, compared, and otherwise manipulated. Primarily for general reasons, it is sometimes convenient to refer to these signals as bits, values, elements, symbols, characters, items, numbers, etc.

[0108] However, it should be remembered that all such and similar terms are associated with the appropriate physical quantity and are merely convenient labels for application to that quantity. Unless otherwise stated, it will be apparent from the following discussion that throughout the description, the use of terms such as “identify,” “determine,” “store,” “adjust,” “cause,” “return,” “compare,” “create,” “stop,” “load,” “copy,” “throw,” “replace,” “execute,” etc., refers to the actions and processes of a computer system or similar electronic computing device that manipulate and convert data represented as physical (electronic) quantities in the registers and memory of the computer system into similarly represented other data, which are represented as physical quantities in the computer system's memory or registers or other such information storage, transmission, or display devices.

[0109] Examples of this disclosure also relate to apparatus for performing the methods described herein. The apparatus may be specifically constructed for the desired purpose, or it may be a general-purpose computer system designed by selective programming of computer programs stored in a computer system. Such computer programs may be stored in computer-readable storage media, such as, but not limited to, any type of hard disk, including optical discs, compact disc read-only memory (CD-ROM), and magneto-optical discs, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), 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.

[0110] The methods and demonstrations described herein are not inherently related to any particular computer or other device. Various general-purpose systems can be used with programs based on the teachings herein, or it may prove convenient to construct more specialized devices to perform the required method steps. The necessary structures for various such systems will be described below. Furthermore, the scope of this disclosure is not limited to any particular programming language. It should be understood that the teachings of this disclosure can be implemented using a variety of programming languages.

[0111] The foregoing description has set forth numerous specific details, such as examples of specific systems, components, methods, etc., to provide a good understanding of several embodiments of this disclosure. However, it will be apparent to those skilled in the art that at least some embodiments of this disclosure can be practiced without such specific details. In other instances, well-known components or methods have not been described in detail or presented in a simple block diagram format to avoid unnecessarily obscuring this disclosure. Therefore, the specific details set forth are merely exemplary. Specific implementations may differ from these exemplary details and are still considered to be within the scope of this disclosure.

[0112] Throughout this specification, references to "an embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment. Therefore, the appearance of the phrase "in an embodiment" or "in an embodiment" throughout this specification does not necessarily refer to the same embodiment. Furthermore, the term "or" is intended to mean an inclusive "or," not an exclusive "or." When the terms "about" or "approximately" are used herein, this is intended to mean that a given nominal value is accurate to within ±10%.

[0113] Although the operations of the methods are shown and described herein in a specific order, the order of operations of each method may be changed, such that some operations may be performed in reverse order, or that some operations may be performed at least partially concurrently with other operations. In another embodiment, instructions or sub-operations of different operations may be performed intermittently and / or alternately.

[0114] It should be understood that the above description is intended to be illustrative and not limiting. Many other embodiments will become apparent to those skilled in the art upon reading and understanding the above description. Therefore, the scope of this disclosure should be determined by reference to the appended claims and the full scope of their equivalents. [Simplified Explanation of the Diagram]

[0008] The present disclosure is shown in the accompanying drawings by way of example rather than limitation.

[0009] Figure 1 illustrates the processing system for various states according to this disclosure.

[0010] Figure 2 illustrates a processing system according to certain embodiments, wherein the chamber body has a surface material configuration.

[0011] Figure 3A illustrates the distribution of processing results according to certain embodiments.

[0012] Figure 3B illustrates the distribution of processing results according to certain embodiments.

[0013] Figure 4 is a top view of a substrate support structure according to certain embodiments.

[0014] Figure 5 shows a bottom view of the chamber body within a processing chamber including a plasma injection point according to certain embodiments.

[0015] Figure 6 is a block diagram illustrating an exemplary system architecture in which embodiments of the present disclosure may be operated.

[0016] Figure 7 illustrates a model training workflow and a model application workflow for surface material configuration according to an embodiment of this disclosure.

[0017] Figure 8 illustrates a flowchart of an example method for predicting a composite configuration of a processing chamber according to some embodiments of this disclosure.

[0018] Figure 9 illustrates a block diagram of an example computing device capable of plasma delivery and / or processing, operating in one or more modes according to this disclosure. [Biomaterial Storage]

[0116] Domestic storage information (please note in order of storage institution, date, and number): None. International storage information (please note in order of storage country, institution, date, and number): None.

Claims

1. A plasma treatment system, comprising: A processing chamber includes a chamber body having a wall that encloses an internal volume, the wall having a first material; A plasma source is configured to expose a substrate disposed within the processing chamber to plasma-related fluxes, wherein a first material has a first set of composite coefficients related to the plasma-related fluxes; and a second material disposed along a first region of the chamber body, the second material having a second set of plasma composite coefficients related to the plasma-related fluxes, wherein the second set of plasma composite coefficients differs from the first set of plasma composite coefficients, and wherein the first material comprises: quartz, titanium, or stainless steel, and the second material differs from the first material and is selected from a list including: borosilicate glass, boron nitride, quartz, titanium, and stainless steel.

2. The plasma processing system as claimed in claim 1, further comprising: A third material is disposed along a second region of the main body of the chamber, the third material having a third set of plasma composite coefficients related to the plasma-related flow rates, wherein the third set of plasma composite coefficients is different from the first set of plasma composite coefficients and the second set of plasma composite coefficients.

3. The plasma processing system as claimed in claim 2, wherein: One or more of the plasma recombination coefficients in the second group are greater than the corresponding plasma recombination coefficients in the first group; and one or more of the plasma recombination coefficients in the third group are less than the corresponding plasma recombination coefficients in the first group.

4. The plasma processing system as claimed in claim 1, wherein: The chamber body further includes a support structure that supports the substrate; and the second material is disposed along a surface of the support structure.

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

6. The plasma processing system as claimed in claim 1, wherein the wall comprises quartz, and the first material comprises one of borosilicate glass, titanium, or stainless steel.

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

8. The plasma treatment system of claim 1, wherein at least one of the first set of plasma composite coefficients or at least one of the second set of plasma composite coefficients is associated with silicon nitridation occurring within the treatment chamber.

9. The plasma processing system as claimed in claim 1, wherein: The processing chamber includes an annular plasma injection point formed between a first radius and a second radius on a first surface of the chamber body, the annular plasma injection point being configured to deliver plasma from the plasma source to the internal volume of the processing chamber; and the first material being disposed along the first surface of the chamber body within the first radius.

10. The plasma processing system as claimed in claim 1, wherein the first region comprises: A ring concentric with an annular plasma injection point, wherein the plasma source includes the annular plasma injection point.

11. The plasma processing system as claimed in claim 2, wherein the first region of the chamber body includes: The first ring, and the second region of the chamber body includes a second ring concentric with the first ring.

12. The plasma processing system as claimed in claim 1, wherein the first region of the chamber body includes: A first part consisting of a disc, and a second part consisting of a ring concentric with the disc.

13. A processing chamber apparatus, comprising: A chamber body having walls enclosing an internal volume, the walls having a first material, wherein the first material has a first set of plasma composite coefficients; a second material disposed along a first region of the chamber body, the second material having a second set of plasma composite coefficients different from the first set of plasma composite coefficients; and a third material disposed along a second region of the chamber body and different from the second material, the third material having a third set of plasma composite coefficients different from both the first and second sets of plasma composite coefficients, wherein the second and third materials are selected from a list including: borosilicate glass, boron nitride, quartz, titanium, and stainless steel.

14. The processing chamber apparatus as claimed in claim 13, wherein: One or more of the plasma composite coefficients in the second group are greater than the corresponding plasma composite coefficients in the first group; and one or more of the plasma composite coefficients in the third group are less than the corresponding plasma composite coefficients in the first group.

15. The processing chamber apparatus as claimed in claim 13, wherein: The chamber body further includes a support structure supporting a substrate; and the second material is disposed along a surface of the support structure.

16. The processing chamber apparatus as claimed in claim 13, 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 within the processing chamber apparatus.

Images