Patent Publication Number: US-2022228265-A1

Title: System and method for dynamically adjusting thin-film deposition parameters

Description:
BACKGROUND 
     Technical Field 
     The present disclosure relates to the field of thin-film deposition. 
     Description of the Related Art 
     There has been a continuous demand for increasing computing power in electronic devices including smart phones, tablets, desktop computers, laptop computers and many other kinds of electronic devices. Integrated circuits provide the computing power for these electronic devices. One way to increase computing power in integrated circuits is to increase the number of transistors and other integrated circuit features that can be included for a given area of semiconductor substrate. 
     To continue decreasing the size of features in integrated circuits, various thin-film deposition techniques are implemented. These techniques can form very thin films. However, thin-film deposition techniques also face serious difficulties in ensuring that the thin films are properly formed. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1  is an illustration of a thin-film deposition system, according to one embodiment. 
         FIG. 2  is a block diagram of a control system of a thin-film deposition system, according to one embodiment. 
         FIG. 3A  is flow diagram of a process for training an analysis model of a control system, according to one embodiment. 
         FIG. 3B  is a block diagram illustrating operational and training aspects of an analysis mode, according to one embodiment. 
         FIG. 4  is a flow diagram of a process for performing a thin-film deposition process in conjunction with an analysis model, according to one embodiment. 
         FIG. 5  is a flow diagram of a method for forming a thin film, according to one embodiment. 
         FIG. 6  is a flow diagram of a method for forming a thin film, according to one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, many thicknesses and materials are described for various layers and structures within an integrated circuit die. Specific dimensions and materials are given by way of example for various embodiments. Those of skill in the art will recognize, in light of the present disclosure, that other dimensions and materials can be used in many cases without departing from the scope of the present disclosure. 
     The following disclosure provides many different embodiments, or examples, for implementing different features of the described subject matter. Specific examples of components and arrangements are described below to simplify the present description. These are, of course, merely examples and are not intended to be limiting. For example, the formation of a first feature over or on a second feature in the description that follows may include embodiments in which the first and second features are formed in direct contact, and may also include embodiments in which additional features may be formed between the first and second features, such that the first and second features may not be in direct contact. In addition, the present disclosure may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. 
     Further, spatially relative terms, such as “beneath,” “below,” “lower,” “above,” “upper” and the like, may be used herein for ease of description to describe one element or feature&#39;s relationship to another element(s) or feature(s) as illustrated in the figures. The spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. The apparatus may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein may likewise be interpreted accordingly. 
     In the following description, certain specific details are set forth in order to provide a thorough understanding of various embodiments of the disclosure. However, one skilled in the art will understand that the disclosure may be practiced without these specific details. In other instances, well-known structures associated with electronic components and fabrication techniques have not been described in detail to avoid unnecessarily obscuring the descriptions of the embodiments of the present disclosure. 
     Unless the context requires otherwise, throughout the specification and claims that follow, the word “comprise” and variations thereof, such as “comprises” and “comprising,” are to be construed in an open, inclusive sense, that is, as “including, but not limited to.” 
     The use of ordinals such as first, second and third does not necessarily imply a ranked sense of order, but rather may only distinguish between multiple instances of an act or structure. 
     Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure or characteristic described in connection with the 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 are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. 
     As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the content clearly dictates otherwise. It should also be noted that the term “or” is generally employed in its sense including “and/or” unless the content clearly dictates otherwise. 
     Embodiments of the present disclosure provide thin films of reliable thickness and composition. Embodiments of the present disclosure utilize machine learning techniques to adjust thin-film deposition process parameters between deposition processes or even during deposition processes. Embodiments of the present disclosure utilize machine learning techniques to train an analysis model to determine process parameters that should be implemented for a next thin-film deposition process or even for a next phase of a current thin-film deposition process. The result is that thin-film deposition processes produce thin films having thicknesses and compositions that reliably fall within target specifications. Integrated circuits that include the thin films will not have performance problems that can result if the thin films are not properly formed. Furthermore, batches of semiconductor wafers will have improved yields and fewer scrapped wafers. 
       FIG. 1  is a block diagram of a thin-film deposition system  100 , according to one embodiment. The thin-film deposition system  100  includes a thin-film deposition chamber  102  including an interior volume  103 . A support  106  is positioned within the interior volume  103  and is configured to support a substrate  104  during a thin-film deposition process. The thin-film deposition system  100  is configured to deposit a thin film on the substrate  104 . The thin-film deposition system  100  includes a control system  124  that dynamically adjusts thin-film deposition parameters. Details of the control system  124  are provided after description of the operation of the thin-film deposition system  100 . 
     In one embodiment, the thin-film deposition system  100  includes a first fluid source  108  and a second fluid source  110 . The first fluid source  108  supplies a first fluid into the interior volume  103 . The second fluid source  110  supplies a second fluid into the interior volume  103 . The first and second fluids both contribute in depositing a thin film on the substrate  104 . While  FIG. 1  illustrates fluid sources  108  and  110 , in practice, the fluid sources  108  and  110  may include or supply materials other than fluids. For example, the fluid sources  108  and  110  may include material sources that provide all materials for the deposition process. 
     In one embodiment, the thin-film deposition system  100  is an atomic layer deposition (ALD) system that performs ALD processes. The ALD processes form a seed layer on the substrate  104 . The seed layer is selected to chemically interact with a first precursor gas, such as the first fluid supplied by the first fluid source  108 . The first fluid is supplied into the interior volume  103 . The first fluid reacts with the seed layer to form new compounds with each atom or molecule of the surface of the seed layer. This corresponds to the deposition of a first layer, or a first step in deposition of the first layer of the thin film.  FIG. 1  and the figures herein are described primarily with reference to an ALD system. However, other types of thin-film deposition systems can be utilized without departing from the scope of the present disclosure. These and other types of thin-film deposition systems can include chemical vapor deposition systems, physical vapor deposition systems, or other types of deposition systems. 
     The reaction between the seed layer and the first fluid results in a byproduct. After flowing the first fluid for a selected amount of time, a purge gas is supplied into the interior volume to purge the byproducts of the first fluid, as well as the unreacted portions of the first fluid, from the interior volume  103  through the exhaust channel  120 . 
     After the first fluid has been purged, a second precursor gas, such as the second fluid is supplied into the interior volume from the second fluid source  110 . The second fluid reacts with the first layer to form a second layer on top of the first layer. Alternatively, the flow of the second fluid can complete the formation of the first layer by reacting with the first portion of the first layer. This reaction can also result in byproducts. A purge gas is again supplied into the interior volume  103  to purge the byproducts of the second fluid, as well as the unreacted portions of the second fluid, from the interior volume  103 . This sequence of supplying the first fluid, purging, supplying the second fluid, and purging again is repeated until the thin film has a selected thickness. 
     The parameters of a thin film generated by the thin-film deposition system  100  can be affected by large number of process conditions. The process conditions can include, but are not limited to, an amount of fluid or material remaining in the fluid sources  108 ,  110 , a flow rate of fluid or material from the fluid sources  108 ,  110 , the pressure of fluids provided by the fluid sources  108  and  110 , the length of tubes or conduits that carry fluid or material into the deposition chamber  102 , the age of an ampoule defining or included in the deposition chamber  102 , the temperature within the deposition chamber  102 , the humidity within the deposition chamber  102 , the pressure within the deposition chamber  102 , light absorption a reflection within the deposition chamber  102 , surface features of the semiconductor wafer  104 , the composition of materials provided by the fluid sources  108  and  110 , the phase of materials provided by the fluid sources  108  and  110 , the duration of the deposition process, the duration of individual phases of the deposition process, and various other factors, including factors not specifically listed above. 
     The combination of the various process conditions during the deposition process determines the thickness, composition, or crystal structure, or other parameters of a thin film formed by the deposition process. It is possible that process conditions may result in thin films that do not have parameters that fall within target parameters. If this happens, then integrated circuits formed from the semiconductor wafer  104  may not function properly. The quality of batches of semiconductor wafers may suffer. In some cases, some semiconductor wafers may need to be scrapped. 
     The thin-film deposition system  100  utilizes the control system  124  to dynamically adjust process conditions to ensure that deposition processes result in thin films having parameters or characteristics that fall within target parameters or characteristics. The control system  124  is connected to processing equipment associated with the thin-film deposition system  100 . The processing equipment can include components shown in  FIG. 1  and components not shown in  FIG. 1 . The control system  124  can control the flow rate of material from the fluid sources  108  and  110 , the temperature of materials supplied by the fluid sources  108  and  110 , the pressure of fluids provided by the fluid sources  108  and  110 , the flow rate of material from purge sources  112  and  114 , the duration of flow of materials from the fluid sources  108  and  110  and the purge sources  112  of  114 , the temperature within the deposition chamber  102 , the pressure within the deposition chamber  102 , the humidity within the deposition chamber  102 , and other aspects of the thin-film deposition process. The control system  124  controls these process parameters so that the thin-film deposition process results in a thin-film having target parameters such as a target thickness, a target composition, a target crystal orientation, etc. 
     The control system  124  utilizes machine learning processes in order to dynamically adjust process parameters to ensure the quality of thin films. As will be described in greater detail in relation to  FIG. 2 , the control system  124  utilizes a large amount of data related to a large number of historical thin-film deposition processes. The data includes historical process parameters and the parameters of the resulting thin films. The machine learning process trains an analysis model to predict thin-film characteristics based on a set of process parameters. After the analysis model has been trained, the control system  124  is able to dynamically select process parameters for future thin-film deposition processes. 
     In some cases, the thin-film deposition process can be very sensitive to concentrations or flow rates of the first and second fluids at the various stages during the thin-film deposition processes. If the concentration or flow rate of the first or second fluid is not sufficiently high at particular stages, then the thin film may not be formed properly on the substrate  104 . For example, the thin film may not have a desired composition or thickness if the concentration or flow rate of the first or second fluid is not sufficiently high. 
     The amount of fluid remaining in the first and second fluid sources  108  and  110  can affect the flow rate or concentration of the first and second fluids in the deposition chamber  102 . For example, if the first fluid source  108  has a low amount of the first fluid remaining, then the flow rate of the first fluid from the first fluid source  108  may be low. If the first fluid source  108  is empty and does not include any more of the first fluid, then there will be no flow of the first fluid from the first fluid source  108 . The same considerations apply to the second fluid source  110 . Low or nonexistent flow rates can result in a thin film that is not properly formed. 
     In one embodiment, the thin-film deposition system  100  includes an exhaust channel  120  communicatively coupled to the interior volume  103  of the deposition chamber  102 . Exhaust products from the thin-film deposition process flow out of the interior volume  103  via the exhaust channel  120 . The exhaust products can include unreacted portions of the first and second fluids, byproducts of the first and second fluids, purge fluids used to purge the interior volume  103 , or other fluids or materials. 
     The thin-film deposition system  100  may include a byproduct sensor coupled to the exhaust channel  120 . The byproduct sensor  122  is configured to sense the presence and/or concentration of byproducts from one or both of the first and second fluids in the exhaust fluids flowing through the exhaust channel  120 . The first and second fluids interact together to form the thin film on the substrate  104 . The deposition process also results in byproducts from the first and second fluids. The concentration of these byproducts is indicative of the concentration or flow rate of one or both of the first and second fluids during deposition. The byproduct sensor  122  senses the concentration of the byproducts in the exhaust fluids flowing from the interior volume  103  through the exhaust channel  120 . 
     In one embodiment, the thin-film deposition system  100  includes a control system  124 . The control system  124  is coupled to the byproduct sensor  122 . The control system  124  receives the sensor signals from the byproduct sensor  122 . The sensor signals from the byproduct sensor  122  are indicative of the concentration of byproducts of one or both of the first and second fluids in the exhaust fluid. The control system  124  can analyze the sensor signals and determine a flow rate or concentration of one or both of the first and second fluid sources  108 ,  110  during particular stages of the deposition process. The control system  124  can also determine a remaining level of the first fluid in the first fluid source  108  and/or of the second fluid in the second fluid source  110 . 
     The control system  124  can include one or more computer readable memories. The one or more memories can store software instructions for analyzing sensor signals from the byproduct sensor  122  and for controlling various aspects of the thin-film deposition system  100  based on the sensor signals. The control system  124  can include one or more processors configured to execute the software instructions. The control system  124  can include communication resources that enable communication with the byproduct sensor  122  and other components of the thin-film deposition system  100 . 
     In one embodiment, the control system  124  is communicatively coupled to the first and second fluid sources  108 ,  110  via one or more communication channels  125 . The control system  124  can send signals to the first fluid source  108  and the second fluid source  110  via the communication channels  125 . The control system  124  can control functionality of the first and second fluid sources  108 ,  110  responsive, in part, to the sensor signals from the byproduct sensor  122 . 
     In one embodiment, the byproduct sensor  122  senses a concentration of byproducts in the exhaust fluid. The byproduct sensor  122  sends sensor signals to the control system  124 . The control system  124  analyzes the sensor signals and determines that a recent flow rate of the first fluid from the first fluid source  108  was lower than expected, based on the sensor signals from the byproduct sensor  122 . The control system  124  sends control signals to the first fluid source  108  commanding the first fluid source  108  to increase a flow rate of the first fluid during a subsequent deposition cycle. The first fluid source  108  increases the flow rate of the first fluid into the interior volume  103  of the deposition chamber  102  responsive to the control signals from the control system  124 . The byproduct sensor  122  can again generate sensor signals indicative of the concentration of byproducts of the first fluid during the subsequent deposition cycle. The control system  124  can determine whether the flowrate of the first fluid needs to be adjusted based on the sensor signals from the byproduct sensor  122 . In this way, the byproduct sensor  122 , the control system  124 , and the first fluid source  108  make up a feedback loop for adjusting the flowrate of the first fluid. The control system  124  can also control the second fluid source  110  in the same manner as the first fluid source  108 . Furthermore, the control system  124  can control both the first fluid source  108  and the second fluid source  110 . 
     In one embodiment, the thin-film deposition system  100  can include one or more valves, pumps, or other flow control mechanisms for controlling the flow rate of the first fluid from the first fluid source  108 . These flow control mechanisms may be part of the fluid source  108  or may be separate from the fluid source  108 . The control system  124  can be communicatively coupled to these flow control mechanisms or to systems that control these flow control mechanisms. The control system  124  can control the flowrate of the first fluid by controlling these mechanisms. The control system  100  may include valves, pumps, or other flow control mechanisms that control the flow of the second fluid from the second fluid source  110  in the same manner as described above in reference to the first fluid and the first fluid source  108 . 
     In one embodiment, the control system  124  can determine how much of the first fluid remains in the first fluid source  108  based on the sensor signals from the byproduct sensor  122 . The control system  124  may analyze the sensor signals to determine that the first fluid source  108  is empty or is nearly empty. The control system  124  can provide an indication to technicians or other personnel indicating that the first fluid source  108  is empty or nearly empty and that the first fluid source  108  should be refilled or replaced. These indications can be displayed on a display, can be transmitted via email, instant message, or other communication platforms that enable technicians or other experts or systems to understand that one or both of the first and second fluid sources  108 ,  110  are empty or nearly empty. 
     In one embodiment, the thin-film deposition system  100  includes a manifold mixer  116  and a fluid distributor  118 . The manifold mixer  116  receives the first and second fluids, either together or separately, from the first fluid source  108  and the second fluid source  110 . The manifold mixer  116  provides either the first fluid, the second fluid, or a mixture of the first and second fluids to the fluid distributor  118 . The fluid distributor  118  receives one or more fluids from the manifold mixer  116  and distributes the one or more fluids into the interior volume  103  of the thin-film deposition chamber  102 . 
     In one embodiment, the first fluid source  108  is coupled to the manifold mixer  116  by a first fluid channel  130 . The first fluid channel  130  carries the first fluid from the fluid source  108  to the manifold mixer  116 . The first fluid channel  130  can be a tube, pipe, or other suitable channel for passing the first fluid from the first fluid source  108  to the manifold mixer  116 . The second fluid source  110  is coupled to the manifold mixer  116  by second fluid channel  132 . The second fluid channel  132  carries the second fluid from the second fluid source  110  to the manifold mixer  116 . 
     In one embodiment, the manifold mixer  116  is coupled to the fluid distributor  118  by a third fluid line  134 . The third fluid line  134  carries fluid from the manifold mixer  116  to the fluid distributor  118 . The third fluid line  134  may carry the first fluid, the second fluid, a mixture of the first and second fluids, or other fluids, as will be described in more detail below. 
     The first and second fluid sources  108 ,  110  can include fluid tanks. The fluid tanks can store the first and second fluids. The fluid tanks can selectively output the first and second fluids. 
     In one embodiment, the thin-film deposition system  100  includes a first purge source  112  and the second purge source  114 . The first purge source is coupled to the first fluid line  130  by first purge line  136 . The second purge source is coupled to the fluid line  132  by second purge line  138 . In practice, the first and second purge sources may be a single purge source. 
     In one embodiment, the first and second purge sources  112 ,  114  supply a purging gas into the interior volume  103  of the deposition chamber  102 . The purge fluid is a fluid selected to purge or carry the first fluid, the second fluid, byproducts of the first or second fluid, or other fluids from the interior volume  103  of the deposition chamber  102 . The purge fluid is selected to not interact with the substrate  104 , the thin-film layer deposited on the substrate  104 , the first and second fluids, and byproducts of this first or second fluid. Accordingly, the purge fluid may be an inert gas including, but not limited to, Ar or N2. 
     After a cycle of flowing one or both of the first or second fluids into the interior volume  103 , the thin-film deposition system  100  purges the interior volume  103  by flowing the purge fluid into the interior volume  103  and through the exhaust channel  120 . The control system  124  can be communicatively coupled to the first and second purge sources  112 ,  114 , or flow mechanisms that control the flow of the purge fluid from the first and second purge sources  112 ,  114 . The control system  124  can purge the interior volume  103  after or between deposition cycles, as will be explained in more detail below. 
     In one embodiment, the first and second purge lines  136 ,  138  join the first and second fluid lines  130 ,  132  at selected angles. The angles are selected to ensure that the purge fluid flows toward the manifold mixer  116  and not toward the first or second fluid sources  108 ,  110 . Likewise the angle helps ensure that the first and second fluids will flow from the first and second fluid sources  108 ,  110  toward the manifold mixer  116  and not toward the first and second purge sources  112 ,  114 . 
     While  FIG. 1  illustrates a first fluid source  108  and a second fluid source  110 , in practice the thin-film deposition system  100  can include other numbers of fluid sources. For example, the thin-film deposition system  100  may include only a single fluid source or more than two fluid sources. Accordingly, the thin-film deposition system  100  can include a different number than two fluid sources without departing from the scope of the present disclosure. 
     Furthermore, the thin-film deposition system  100  has been described, in one embodiment, as an ALD system, the thin-film deposition system  100  can include other types of deposition systems without departing from the scope of the present disclosure. For example, the thin-film deposition system  100  can include a chemical vapor deposition system, a physical vapor deposition system, a sputtering system, or other types of thin-film deposition systems without departing from the scope of the present disclosure. A byproduct sensor  122  can be utilized to determine the flowrate or concentration of deposition fluids as well as how much deposition fluid remains in a deposition fluid source. 
     In one embodiment, the first fluid source  108  includes H 2 O in gas or liquid form. The second fluid source  110  includes HfCL 4  fluid. The HfCL 4  fluid may be a gas. The first and second fluids can be used to form a hafnium-based high-K gate dielectric layer for CMOS transistors. 
     During a first period of time, the first fluid (H 2 O) is output from the first fluid source  108  into the interior volume  103 . In one example, the first fluid flows for about 10 seconds, though other lengths of time can be used without departing from the scope of the present disclosure. 
     After the first period of time, a purge gas is output from the purge source  112  into the interior volume  103  during a second period of time. The purge gas may include nitrogen molecules (N 2 ) or another nonreactive gas. In one example, purge gas flows for 2-10 seconds, though other lengths of time can be used without departing from the scope of the present disclosure. The purge gas can flow from the purge source  112  or from both the purge source  112  and the purge source  114 . 
     During a third period of time after the second period of time, HfCL 4  is output from the second fluid source  110  into the interior volume  103 . In one example, the HfCL 4  flows for about 1-10s, though other lengths of time can be used without departing from the scope of the present disclosure. 
     During a fourth period of time after the third period of time, a purge gas is output from the purge source  112  into the interior volume  103 . In one example, purge gas flows for 1-10s, though other lengths of time can be used without departing from the scope of the present disclosure. The purge gas can flow from the purge source  114  or from both the purge source  112  and the purge source  114 . 
     In one embodiment, the seed layer includes functionalized oxygen atoms. When the first fluid (H 2 O) is provided into the interior volume  103 , the H 2 O molecules react with the functionalized oxygen atoms of the seed layer to form OH from each functionalized oxygen atom. The byproducts of this reaction, as well as any remaining H 2 O molecules, are purged from the interior volume  103  via the exhaust channel  120  by flow of the purge gas. The HfCl 4  is then provided into the interior volume  103 . The HfCl 4  reacts with the OH compounds to form, on the substrate  104 , Hf—O—HfCl 3 . One of the byproducts of this reaction is HCl. The purge gas flows again, followed by H 2 O. The H 2 O reacts with the Hf—O—HfCl 3  to form, on the substrate  104 , Hf—OH 3 . A byproduct of this reaction is HCl. The purge gas then flows again. The cycle can be repeated multiple times, as described above. 
     The control system  124  can utilize machine learning processes to dynamically adjust parameters of the ALD process between cycles and between depositions. Dynamically adjusting parameters can include adjusting the duration of time of the various fluid flow and purge cycles. Dynamically adjusting parameters can include adjusting the flow rate of fluids from the fluid sources  108  and  110  and from the purge sources  112  and  114 . 
       FIG. 2  is a block diagram of the control system  124 , according to one embodiment. The control system  124  of  FIG. 2  is configured to control operation of an ALD system  100 , according to one embodiment. The control system  124  utilizes machine learning to adjust parameters of the ALD system  100 . The control system  124  can adjust parameters of the ALD system  100  between ALD runs or even between ALD cycles in order to ensure that a thin-film layer formed by the ALD process falls within selected specifications. 
     In one embodiment, the control system  124  includes an analysis model  140  and a training module  141 . The training module trains the analysis model  140  with a machine learning process. The machine learning process trains the analysis model  140  to select parameters for an ALD process that will result in a thin film having selected characteristics. Although the training module  141  is shown as being separate from the analysis model  140 , in practice, the training module  141  may be part of the analysis model  140 . 
     The control system  124  includes, or stores, training set data  142 . The training set data  142  includes historical thin-film data  144  and historical process conditions data  146 . The historical thin-film data  144  includes data related to thin films resulting from ALD processes. The historical process conditions data  146  includes data related to process conditions during the ALD processes that generated the thin films. As will be set forth in more detail below, the training module  141  utilizes the historical thin-film data  144  and the historical process conditions data  146  to train the analysis model  140  with a machine learning process. 
     In one embodiment, the historical thin-film data  144  includes data related to the thickness of previously deposited thin films. For example, during operation of a semiconductor fabrication facility, thousands or millions of semiconductor wafers may be processed over the course of several months or years. Each of the semiconductor wafers may include thin films deposited by ALD processes. After each ALD process, the thicknesses of the thin films are measured as part of a quality control process. The historical thin-film data  144  includes the thicknesses of each of the thin films deposited by ALD processes. Accordingly, the historical thin-film data  144  can include thickness data for a large number of thin films deposited by ALD processes. 
     In one embodiment, the historical thin-film data  144  may also include data related to the thickness of thin films at intermediate stages of the thin-film deposition processes. For example, an ALD process may include a large number of deposition cycles during which individual layers of the thin film are deposited. The historical thin-film data  144  can include thickness data for thin films after individual deposition cycles or groups of deposition cycles. Thus, the historical thin-film data  144  not only includes data related to the total thickness of a thin film after completion of an ALD process, but may also include data related to the thickness of the thin film at various stages of the ALD process. 
     In one embodiment, the historical thin-film data  144  includes data related to the composition of the thin films deposited by ALD processes. After a thin film is deposited, measurements can be made to determine the elemental or molecular composition of the thin films. Successful deposition of the thin films results in a thin film that includes particular proportions of certain elements or compounds. Unsuccessful depositions may result in a thin film that does not include the specified proportions of elements or compounds. The historical thin-film data  144  can include data from measurements indicating the elements or compounds that make up the various thin films. 
     In one embodiment, the historical thin-film data  144  includes data related to crystal structures of thin films deposited by ALD processes. Successful deposition of a thin film may result in a particular crystal structure. X-ray crystallography measurements may be made on the thin films to determine the crystal structures of the various thin films. The historical thin-film data  144  can include crystal structure data for the various thin films. 
     In one embodiment, the historical process conditions  146  include various process conditions or parameters during ALD processes that produce the thin films associated with the historical thin-film data  144 . Accordingly, for each thin film having data in the historical thin-film data  144 , the historical process conditions data  146  can include the process conditions or parameters that were present during deposition of the thin film. For example, the historical process conditions data  146  can include data related to the pressure, temperature, and fluid flow rates within the process chamber during ALD processes. 
     The historical process conditions data  146  can include data related to remaining amounts of precursor material in the fluid sources during ALD processes. The historical process conditions data  146  can include data related to the age of the deposition chamber  102 , the number of deposition processes that have been performed in the deposition chamber  102 , a number of deposition processes that have been performed in the process chamber  102  since the most recent cleaning cycle of the process chamber  102 , or other data related to the process chamber  102 . The historical process conditions data  146  can include data related to compounds or fluids introduced into the process chamber  102  during the deposition process. The data related to the compounds can include types of compounds, phases of compounds (solid, gas, or liquid), mixtures of compounds, or other aspects related to compounds or fluids introduced into the process chamber  102 . The historical process conditions data  146  can include data related to the humidity within the process chamber  102  during ALD processes. The historical process conditions data  146  can include data related to light absorption, light adsorption, and light reflection related to the process chamber  102 . The historical process conditions data  126  can include data related to the length of pipes, tubes, or conduits that carry compounds or fluids into the process chamber  102  during ALD processes. The historical process conditions data  146  can include data related to the condition of carrier gases that carry compounds or fluids into the process chamber  102  during ALD processes. 
     In one embodiment, historical process conditions data  146  can include process conditions for each of a plurality of individual cycles of a single ALD process. Accordingly, the historical process conditions data  146  can include process conditions data for a very large number of ALD cycles. 
     In one embodiment, the training set data  142  links the historical thin-film data  144  with the historical process conditions data  146 . In other words, the thin-film thickness, material composition, or crystal structure associated with a thin film in the historical thin-film data  144  is linked to the process conditions data associated with that deposition process. As will be set forth in more detail below, the labeled training set data can be utilized in a machine learning process to train the analysis model  140  to predict semiconductor process conditions that will result in properly formed thin films. 
     In one embodiment the analysis model  140  includes a neural network. Training of the analysis model  140  will be described in relation to a neural network. However, other types of analysis models or algorithms can be used without departing from the scope of the present disclosure. The training module  141  utilizes the training set data  142  to train the neural network with a machine learning process. During the training process, the neural network receives, as input, historical process conditions data  146  from the training set data. During the training process, the neural network outputs predicted thin-film data. The predicted thin-film data predicts characteristics of a thin film that would result from the historical process conditions data. The training process trains the neural network to generate predicted thin-film data. The predicted thin-film data can include thin-film thickness, thin-film density, thin film composition, or other thin film parameters. 
     In one embodiment, the neural network includes a plurality of neural layers. The various neural layers include neurons that define one or more internal functions. The internal functions are based on weighting values associated with neurons of each neural layer of the neural network. During training, the control system  124  compares, for each set of historical process conditions data, the predicted thin-film data to the actual historical thin-film data associated with the thin film that resulted from those process conditions. The control system generates an error function indicating how closely the predicted thin-film data matches the historical thin-film data. The control system  124  then adjusts the internal functions of the neural network. Because the neural network generates predicted thin-film data based on the internal functions, adjusting the internal functions will result in the generation of different predicted thin-film data for a same set of historical process conditions data. Adjusting the internal functions can result in predicted thin-film data that produces larger error functions (worse matching to the historical thin-film data  144 ) or smaller error functions (better matching to the historical thin-film data  144 ). 
     After adjusting the internal functions of the neural network, the historical process conditions data  146  is again passed to the neural network and the analysis model  140  again generates predicted thin films data. The training module  141  again compares the predicted thin-film data to the historical thin-film data  144 . The training module  141  again adjusts the internal functions of the neural network. This process is repeated in a very large number of iterations of monitoring the error functions and adjusting the internal functions of the neural network until a set of internal functions is found that results in predicted thin-film data that matches the historical thin-film data  144  across the entire training set. 
     At the beginning of the training process, the predicted thin-film data likely will not match the historical thin-film data  144  very closely. However, as the training process proceeds through many iterations of adjusting the internal functions of the neural network, the errors functions will trend smaller and smaller until a set of internal functions is found that results in predicted thin-film data that match the historical thin-film data  144 . Identification of a set of internal functions that results in predicted thin-film data that matches the historical thin-film data  144  corresponds to completion of the training process. Once the training process is complete, the neural network is ready to be used to adjust thin-film deposition process parameters. 
     In one embodiment, after the analysis model  140  has been trained, the analysis model  140  can be utilized to generate sets of process conditions that will result in thin films having selected characteristics. For example, the control system  124  can provide the analysis model  140  with target thin-film parameters corresponding to desired parameters of a thin film. The target parameters can include a thickness of the film, a composition of the thin film, a crystal structure of the thin film, or other target parameters. As is set forth in more detail below, the analysis model  140  identifies a set of process parameters that will result in a thin film having the target parameters. In particular, the analysis model  140  generates process adjustment data indicating process parameters that should be utilized for the next thin-film deposition process or the next phase in the thin-film deposition process. 
     In one embodiment, the analysis model  140  utilizes current process parameter data to assist in generating process adjustment data. The current process parameter data includes data related to current conditions of processing equipment associated with the thin-film deposition processes. For example, the current process conditions data can include a current age of an ampoule that will be utilized in a thin-film deposition process. The current age of the ampoule can indicate one or both of an actual age of the ampoule and a number of deposition processes that have been performed with the ampoule. The current process parameter data can include remaining material levels in the fluid sources  108  and  110  or the purge sources  112  and  114 . The current process parameter data can include the types of materials that will be utilized in the thin-film deposition process. The current process conditions data can include data related to a phase of materials that will be utilized in the thin-film deposition process. The current process conditions data can include the lengths of pipes, conduits, or tubes that will carry fluids or materials into the deposition chamber. 
     The current process conditions data can include data related to the semiconductor wafer or other target that will be utilized in the next deposition process. For example, the current process conditions data can include an effective exposed playing area of the semiconductor wafer. The current process conditions data can include a crystal orientation of the exposed effective playing area of the semiconductor wafer. The current process conditions data can include a roughness index of the exposed effective plain area. The current process conditions data can include an exposed effective sidewall tilting associated with surface features of the semiconductor wafer or other target. The current process conditions data can include a thin-film function group associated with the exposed surface of the semiconductor wafer or other target. The current process conditions data can include the function group of an exposed sidewall of a feature of a semiconductor wafer or other target. The current process conditions data can include the wafer rotation or tilt parameters associated with the semiconductor wafer or other target. 
     Accordingly, the current process conditions data can include fixed conditions for the next thin-film deposition process or phase of the next phase of a thin-film deposition process. The current process conditions data can include many of the same types of data included in the historical process conditions data  146 . 
     In one embodiment, the current process conditions data can include data related to the temperature within the deposition chamber during the deposition process. The current process conditions data can include data related to the pressure within the deposition chamber during the deposition process. The current process conditions data can include data related to the humidity within the deposition chamber. 
     In one embodiment, the analysis model  140  utilizes the current process conditions data and the target thin-film parameter data in order to generate process adjustment data. The process adjustment data identifies process parameters that should be utilized for the next thin-film deposition process or for the next phase of a thin-film deposition process based on the current process conditions data and the target thin-film parameter data. The process adjustment data corresponds to conditions or parameters that can be changed or adjusted for the next deposition process or next thin-film deposition process. Examples of parameters that may be adjusted include the flow rate of fluids or materials into the deposition chamber from the fluid sources  108  and  110 , the temperature within the deposition chamber, the pressure within the deposition chamber, the time duration of the deposition process or phase of a deposition process, voltage levels to be applied during the thin-film deposition process, or other aspects that can be dynamically adjusted between thin-film deposition processes or between phases of a thin-film deposition process. The analysis model  140  can identify values for these parameters that will result in a thin film having the target thin-film parameters such as target thickness, target composition, target crystal structure, or other characteristics. 
     In one embodiment, the analysis model  140  generates process adjustment data by passing the current process conditions data to the analysis model  140 . The analysis model  140  will then select trial values for dynamic process conditions that can be adjusted. The analysis model  140  will then generate predicted thin-film data based on the current process conditions data and the trial values for dynamic process conditions. The predicted thin-film data includes a predicted thickness, a predicted composition, a predicted crystal structure, or other predicted characteristics of a thin film that would result from a thin-film deposition process based on the current process conditions data and the dynamic process conditions data. If the predicted thin-film data falls within the target thin-film parameters, then the analysis model  140  can generate process adjustment data specifying values for the dynamic process conditions data. The specified values will be utilized for the next thin-film deposition process or the next phase of the thin-film deposition process. If the predicted thin-film data does not fall within the target thin-film parameters, then the analysis model  140  selects other trial values for the dynamic process conditions data and generates predicted thin-film data based on the new trial values. This process is repeated in iterations until values for the dynamic process conditions are found that result in predicted thin-film data that falls within the target thin-film parameters. 
     In one embodiment, because the analysis model  140  has been trained with a machine learning process that trains the analysis model  140  to generate thin-film data based on process conditions data, the analysis model  140  is able to identify process adjustment data that will result in a thin-film having parameters that fall within the target thin-film parameters. The analysis model  140  can generate process adjustment data in a very short amount of time. For example, the analysis model  140  can generate process adjustment data in less than three seconds, though other values are possible without departing from the scope of the present disclosure. Accordingly, the analysis model  140  can be run between each thin-film deposition process or between each phase of a thin-film deposition process. 
     In one embodiment, the control system  124  includes processing resources  148 , memory resources  150 , and communication resources  152 . The processing resources  148  can include one or more controllers or processors. The processing resources  148  are configured to execute software instructions, process data, make thin-film deposition control decisions, perform signal processing, read data from memory, write data to memory, and to perform other processing operations. The processing resources  148  can include physical processing resources  148  located at a site or facility of the thin-film deposition system  100 . The processing resources can include virtual processing resources  148  remote from the site thin-film deposition system  100  or a facility at which the thin-film deposition system  100  is located. The processing resources  148  can include cloud-based processing resources including processors and servers accessed via one or more cloud computing platforms. 
     In one embodiment, the memory resources  150  can include one or more computer readable memories. The memory resources  150  are configured to store software instructions associated with the function of the control system and its components, including, but not limited to, the analysis model  140 . The memory resources  150  can store data associated with the function of the control system  124  and its components. The data can include the training set data  142 , current process conditions data, and any other data associated with the operation of the control system  124  or any of its components. The memory resources  150  can include physical memory resources located at the site or facility of the thin-film deposition system  100 . The memory resources can include virtual memory resources located remotely from site or facility of the thin-film deposition system  100 . The memory resources  150  can include cloud-based memory resources accessed via one or more cloud computing platforms. 
     In one embodiment, the communication resources can include resources that enable the control system  124  to communicate with equipment associated with the thin-film deposition system  100 . For example, the communication resources  152  can include wired and wireless communication resources that enable the control system  124  to receive the sensor data associated with the thin-film deposition system  100  and to control equipment of the thin-film deposition system  100 . The communication resources  152  can enable the control system  124  to control the flow of fluids or other material from the fluid sources  108  and  110  and from the purge sources  112  and  114 . The communication resources  152  can enable the control system  124  to control heaters, voltage sources, valves, exhaust channels, wafer transfer equipment, and any other equipment associated with the thin-film deposition system  100 . The communication resources  152  can enable the control system  124  to communicate with remote systems. The communication resources  152  can include, or can facilitate communication via, one or more networks such as wire networks, wireless networks, the Internet, or an intranet. The communication resources  152  can enable components of the control system  124  to communicate with each other. 
     In one embodiment, the analysis model  140  is implemented via the processing resources  148 , the memory resources  150 , and the communication resources  152 . The control system  124  can be a dispersed control system with components and resources and locations remote from each other and from the thin-film deposition system  100 . 
       FIG. 3A  is a flow diagram of a process  300  for training an analysis model to identify process conditions that will result in proper deposition of a thin film, according to one embodiment. One example of an analysis model is the analysis model  140  of  FIG. 2 . The various steps of the process  300  can utilize components, processes, and techniques described in relation to  FIGS. 1-2 . Accordingly,  FIG. 3A  is described with reference to  FIGS. 1-2 . 
     At  302 , the process  300  gathers training set data including historical thin-film data and historical process conditions data. This can be accomplished by using a data mining system or process. The data mining system or process can gather training set data by accessing one or more databases associated with the thin-film deposition system  100  and collecting and organizing various types of data contained in the one or more databases. The data mining system or process, or another system or process, can process and format the collected data in order to generate a training set data. The training set data  142  can include historical thin-film data  144  and historical process conditions data  146  as described in relation to  FIG. 2 . 
     At  304 , the process  300  inputs historical process conditions data to the analysis model. In one example, this can include inputting historical process conditions data  146  into the analysis model  140  with the training module  141  as described in relation to  FIG. 2 . The historical process conditions data can be provided in consecutive discrete sets to the analysis model  140 . Each district set can correspond to a single thin-film deposition process or a portion of a single thin-film deposition process. The historical process conditions data can be provided as vectors to the analysis model  140 . Each set can include one or more vectors formatted for reception processing by the analysis model  140 . The historical process conditions data can be provided to the analysis model  140  in other formats without departing from the scope of the present disclosure. 
     At  306 , the process  300  generates predicted thin-film data based on historical process conditions data. In particular, the analysis model  140  generates, for each set of historical thin-film conditions data  146 , predicted thin-film data. The predicted thin-film data corresponds to a prediction of characteristics of a thin film that would result from that particular set of process conditions. The predicted thin-film data can include thickness, uniformity, composition, crystal structure, or other aspects of a thin film. 
     At  308 , the predicted thin-film data is compared to the historical thin-film data  144 . In particular, the predicted thin-film data for each set of historical process conditions data is compared to the historical thin-film data  144  associated with that set of historical process conditions data. The comparison can result in an error function indicating how closely the predicted thin-film data matches the historical thin-film data  144 . This comparison is performed for each set of predicted thin-film data. In one embodiment, this process can include generating an aggregated error function or indication indicating how the totality of the predicted thin-film data compares to the historical thin-film data  144 . These comparisons can be performed by the training module  141  or by the analysis model  140 . The comparisons can include other types of functions or data than those described above without departing from the scope of the present disclosure. 
     At  310 , the process  300  determines whether the predicted thin-film data matches the historical thin-film data based on the comparisons generated at step  308 . In one example, if the aggregate error function is less than an error tolerance, then the process  300  determines that the thin-film data does not match the historical thin-film data. In one example, if the aggregate error function is greater than an error tolerance, then the process  300  determines that the thin-film data does match the historical thin-film data. In one example, the error tolerance can include a tolerance between 0.1 and 0. In other words, if the aggregate percentage error is less than 0.1, or 10%, then the process  300  considers that the predicted thin-film data matches the historical thin-film data. If the aggregate percentage error is greater than 0.1 or 10%, then the process  300  considers that the predicted thin-film data does not match the historical thin-film data. Other tolerance ranges can be utilized without departing from the scope of the present disclosure. Error scores can be calculated in a variety of ways without departing from the scope of the present disclosure. The training module  141  or the analysis model  140  can make the determinations associated with process step  310 . 
     In one embodiment, if the predicted thin-film data does not match the historical thin-film data  144  at step  310 , then the process proceeds to step  312 . At step  312 , the process  300  adjusts the internal functions associated with the analysis model  140 . In one example, the training module  141  adjusts the internal functions associated with the analysis model  140 . From step  312 , the process returns to step  304 . At step  304 , the historical process conditions data is again provided to the analysis model  140 . Because the internal functions of the analysis model  140  have been adjusted, the analysis model  140  will generate different predicted thin-film data that in the previous cycle. The process proceeds to steps  306 ,  308  and  310  and the aggregate error is calculated. If the predicted thin-film data does not match the historical thin-film data, then the process returns to step  312  and the internal functions of the analysis model  140  are adjusted again. This process proceeds in iterations until the analysis model  140  generates predicted thin-film data that matches the historical thin-film data  144 . 
     In one embodiment, if the predicted thin-film data matches the historical thin-film data then process step  310 , in the process  300 , proceeds to  314 . At step  314  training is complete. The analysis model  140  is now ready to be utilized to identify process conditions and can be utilized in thin-film deposition processes performed by the thin-film deposition system  100 . The process  300  can include other steps or arrangements of steps than shown and described herein without departing from the scope of the present disclosure. 
       FIG. 3B  is a block diagram  350  illustrating operational aspects and training aspects of analysis model  140 , according to one embodiment. As described previously, the training set data  142  includes data related to a plurality of previously performed thin-film deposition processes. Each previously performed thin-film deposition process took place with particular process conditions and resulted in a thin-film having a particular characteristics. The process conditions for each previously performed thin-film deposition process are formatted into a respective process conditions vector  352 . The process conditions vector includes a plurality of data fields  354 . Each data field  354  corresponds to a particular process condition. 
     The example of  FIG. 3B  illustrates a single process conditions vector  352  that will be passed to the analysis model  140  during the training process. In the example of  FIG. 3B , the process conditions vector  352  includes nine data fields  354 . A first data field  354  corresponds to the temperature during the previously performed thin-film deposition process. A second data field  356  corresponds to the pressure during the previously performed thin-film deposition process. A third data field  354  corresponds to the humidity during the previously performed thin-film deposition process. The fourth data field  354  corresponds to the flow rate of deposition materials during the previously performed thin-film deposition process. The fifth data field  354  corresponds to the phase (liquid, solid, or gas) of deposition materials during the previously performed thin-film deposition process. The sixth data field  354  corresponds to the age of the ampoule used in the previously performed thin-film deposition process. The seventh data field  354  corresponds to a size of a deposition area on a wafer during the previously performed thin-film deposition process. The eighth data field  354  corresponds to the density of surface features of the wafer utilized during the previously performed thin-film deposition process. The ninth data field corresponds to the angle of sidewalls of surface features during the previously performed thin-film deposition process. In practice, each process conditions vector  352  can include more or fewer data fields than are shown in  FIG. 3B  without departing from the scope of the present disclosure. Each process conditions vector  352  can include different types of process conditions without departing from the scope of the present disclosure. The particular process conditions illustrated in  FIG. 3B  are given only by way of example. Each process condition is represented by a numerical value in the corresponding data field  354 . For condition types that are not naturally represented in numbers, such as material phase, a number can be assigned to each possible phase. 
     The analysis model  140  includes a plurality of neural layers  356   a - e . Each neural layer includes a plurality of nodes  358 . Each node  358  can also be called a neuron. Each node  358  from the first neural layer  356   a  receives the data values for each data field from the process conditions vector  352 . Accordingly, in the example of  FIG. 3B , each node  358  from the first neural layer  356   a  receives nine data values because the process conditions vector  352  has nine data fields. Each neuron  358  includes a respective internal mathematical function labeled F(x) in  FIG. 3B . Each node  358  of the first neural layer  356   a  generates a scaler value by applying the internal mathematical function F(x) to the data values from the data fields  354  of the process conditions vector  352 . Further details regarding the internal mathematical functions F(x) are provided below. 
     Each node  358  of the second neural layer  356   b  receives the scaler values generated by each node  358  of the first neural layer  356   a . Accordingly, in the example of  FIG. 3B  each node of the second neural layer  350   6 B receives four scaler values because there are four nodes  358  in the first neural layer  356   a . Each node  358  of the second neural layer  356   b  generates a scaler value by applying the respective internal mathematical function F(x) to the scalar values from the first neural layer  356   a.    
     Each node  358  of the third neural layer  356   c  receives the scaler values generated by each node  358  of the second neural layer  356   b . Accordingly, in the example of  FIG. 3B  each node of the third neural layer  356   c  receives five scaler values because there are five nodes  358  in the second neural layer  356   b . Each node  358  of the third neural layer  356   c  generates a scaler value by applying the respective internal mathematical function F(x) to the scalar values from the nodes  358  of the second neural layer  356   b.    
     Each node  358  of the neural layer  356   d  receives the scaler values generated by each node  358  of the previous neural layer (not shown). Each node  358  of the neural layer  356   d  generates a scaler value by applying the respective internal mathematical function F(x) to the scalar values from the nodes  358  of the second neural layer  356   b.    
     The final neural layer includes only a single node  358 . The final neural layer receives the scalar values generated by each node  358  of the previous neural layer  356   d . The node  358  of the final neural layer  356   e  generates a data value  368  by applying a mathematical function F(x) to the scaler values received from the nodes  358  of the neural layer  356   d.    
     In the example of  FIG. 3B , the data value  368  corresponds to the predicted thickness of a thin film generated by process conditions data corresponding to values included in the process conditions vector  352 . In other embodiments, the final neural layer  356   e  may generate multiple data values each corresponding to a particular thin-film characteristic such as thin-film crystal orientation, thin-film uniformity, or other characteristics of a thin film. The final neural layer  356   e  will include a respective node  358  for each output data value to be generated. In the case of a predicted thin film thickness, engineers can provide constraints that specify that the predicted thin film thickness  368  must fall within a selected range, such as between 0 nm and 50 nm, in one example. The analysis model  140  will adjust internal functions F(x) to ensure that the data value  368  corresponding to the predicted thin film thickness will fall within the specified range. 
     During the machine learning process, the analysis model compares the predicted thickness in the data value  368  to the actual thickness of the thin-film as indicated by the data value  370 . As set forth previously, the training set data  142  includes, for each set of historical process conditions data, thin-film characteristics data indicating the characteristics of the thin-film that resulted from the historical thin-film deposition process. Accordingly, the data field  370  includes the actual thickness of the thin-film that resulted from the deposition process reflected in the process conditions vector  352 . The analysis model  140  compares the predicted thickness from the data value  368  to the actual thickness from the data value  370 . The analysis model  140  generates an error value  372  indicating the error or difference between the predicted thickness from the data value  368  and the actual thickness from the data value  370 . The error value  372  is utilized to train the analysis model  140 . 
     The training of the analysis model  140  can be more fully understood by discussing the internal mathematical functions F(x). While all of the nodes  358  are labeled with an internal mathematical function F(x), the mathematical function F(x) of each node is unique. In one example, each internal mathematical function has the following form: 
         F ( X )= x   1   *w   1   +x   2   *w   2   + . . . x   n   *w   1   +b.    
     In the equation above, each value x 1 -x n  corresponds to a data value received from a node  358  in the previous neural layer, or, in the case of the first neural layer  356   a , each value x 1 -x n  corresponds to a respective data value from the data fields  354  of the process conditions vector  352 . Accordingly, n for a given node is equal to the number of nodes in the previous neural layer. The values w 1 -w n  are scalar weighting values associated with a corresponding node from the previous layer. The analysis model  140  selects the values of the weighting values w 1 -w n . The constant b is a scalar biasing value and may also be multiplied by a weighting value. The value generated by a node  358  is based on the weighting values w 1 -w n . Accordingly, each node  358  has n weighting values w 1 -w n . Though not shown above, each function F(x) may also include an activation function. The sum set forth in the equation above is multiplied by the activation function. Examples of activation functions can include rectified linear unit (ReLU) functions, sigmoid functions, hyperbolic tension functions, or other types of activation functions. 
     After the error value  372  has been calculated, the analysis model  140  adjusts the weighting values w 1 -w n  for the various nodes  358  of the various neural layers  356   a - 356   e . After the analysis model  140  adjusts the weighting values w 1 -w n , the analysis model  140  again provides the process conditions vector  352  to the input neural layer  356   a . Because the weighting values are different for the various nodes  358  of the analysis model  140 , the predicted thickness  368  will be different than in the previous iteration. The analysis model  140  again generates an error value  372  by comparing the actual thickness  370  to the predicted thickness  368 . 
     The analysis model  140  again adjusts the weighting values w 1 -w n  associated with the various nodes  358 . The analysis model  140  again processes the process conditions vector  352  and generates a predicted thickness  368  and associated error value  372 . The training process includes adjusting the weighting values w 1 -w n  in iterations until the error value  372  is minimized. 
       FIG. 3B  illustrates a single process conditions vector  352  being passed to the analysis model  140 . In practice, the training process includes passing a large number of process conditions vectors  352  through the analysis model  140 , generating a predicted thickness  368  for each process conditions vector  352 , and generating associated error value  372  for each predicted thickness. The training process can also include generating an aggregated error value indicating the average error for all the predicted thicknesses for a batch of process conditions vectors  352 . The analysis model  140  adjusts the weighting values w 1 -w n  after processing each batch of process conditions vectors  352 . The training process continues until the average error across all process conditions vectors  352  is less than a selected threshold tolerance. When the average error is less than the selected threshold tolerance, the analysis model  140  training is complete and the analysis model is trained to accurately predict the thickness of thin films based on the process conditions. The analysis model  140  can then be used to predict thin-film thicknesses and to select process conditions that will result in a desired thin-film thickness. During use of the trained model  140 , a process conditions vector, representing current process condition for a current thin film deposition process to be performed, and having the same format at the process conditions vector  352 , is provided to the trained analysis model  140 . The trained analysis model  140  can then predict the thickness of a thin film that will result from those process conditions. 
     A particular example of a neural network based analysis model  140  has been described in relation to  FIG. 3B . However, other types of neural network based analysis models, or analysis models of types other than neural networks can be utilized without departing from the scope of the present disclosure. Furthermore, the neural network can have different numbers of neural layers having different numbers of nodes without departing from the scope of the present disclosure. 
       FIG. 4  is a flow diagram of a process  400  for dynamically selecting process conditions for thin-film deposition process and for performing a thin-film deposition process, according to one embodiment. The various steps of the process  400  can utilize components, processes, and techniques described in relation to  FIGS. 1-3B . Accordingly,  FIG. 4  is described with reference to  FIGS. 1-3B . 
     At  402 , the process  400  provides target thin-film conditions data to the analysis model  140 . The target thin-film conditions data identifies selected characteristics of a thin film to be formed by thin-film deposition process. The target thin-film conditions data can include a target thickness, a target composition, target crystal structure, or other characteristics of the thin film. The target thin-film conditions data can include a range of thicknesses. The target condition or characteristics that can be selected are based on thin film characteristic(s) utilized in the training process. In the example of  FIG. 3B , the training process focused on thin film thickness. 
     At  404 , the process  400  provides static process conditions to the analysis model  140 . The static process conditions include process conditions that will not be adjusted for a next thin-film deposition process. The static process conditions can include the target device pattern density indicating the density of patterns on the wafer on which the thin-film deposition process will be performed. The static process conditions can include an effective plan area crystal orientation, an effective plan area roughness index, an effective sidewall area of the features on the surface of the semiconductor wafer, an exposed effective sidewall tilt angle, an exposed surface film function group, an exposed sidewall film function group, a rotation or tilt of the semiconductor wafer, process gas parameters (materials, phase of materials, and temperature of materials), a remaining amount of material fluid in the fluid sources  108  and  110 , a remaining amount of fluid in the purge sources  112  and  114 , a humidity within a deposition chamber, an age of an ampoule utilized in the deposition process, light absorption or reflection within the deposition chamber, the length of pipes or conduits that will provide fluids to the deposition chamber, or other conditions. The static process conditions can include conditions other than those described above without departing from the scope of the present disclosure. Furthermore, in some cases, some of the static process conditions listed above may be dynamic process conditions subject to adjustment as will be described in more detail below. In the example of  FIG. 3B , dynamic process conditions include temperature, pressure, humidity, and flow rate. Static process conditions include phase, ampoule age, deposition area, deposition density, and sidewall angle. 
     At  406 , the process  400  selects dynamic process conditions for the analysis model, according to one embodiment. The dynamic process conditions can include any process conditions not designated as static process conditions. For example, the training set data may include a large number of various types of process conditions data in the historical process conditions data  146 . Some of these types of process conditions will be defined the static process conditions and some of these types of process conditions will be defined as dynamic process conditions. Accordingly, when the static process conditions are supplied at step  404 , the remaining types of process conditions can be defined as dynamic process conditions. The analysis model  140  can initially select initial values for the dynamic process conditions. After the initial values have been selected for the dynamic process conditions, the analysis model has a full set of process conditions to analyze. In one embodiment, the initial values for the dynamic process conditions may be selected based on previously determined starter values, or in accordance with other schemes. 
     The dynamic process conditions can include the flow rate of fluids or materials from the fluid sources  108  and  110  during the deposition process. The dynamic process conditions can include the flow rate of fluids or materials from the purge sources  112  and  114 . The dynamic process conditions can include a pressure within the deposition chamber, a temperature within the deposition chamber, a humidity within the deposition chamber, durations of various steps of the deposition process, or voltages or electric field generated within the deposition chamber. The dynamic process conditions can include other types of conditions without departing from the scope of the present disclosure. 
     At  408 , the analysis model  140  generates predicted thin-film data based on the static and dynamic process conditions. The predicted thin-film data includes the same types of thin-film characteristics established in the target thin-film conditions data. In particular, the predicted thin-film data includes the types of predicted thin-film data from the training process described in relation to  FIGS. 3A and 3B . For example, the predicted thin-film data can include thin-film thickness, film composition, or other parameters of thin films. 
     At  410 , the process compares the predicted thin-film data to the target thin-film data. In particular, the analysis model  140  compares the predicted thin-film data to the target thin-film data. The comparison indicates how closely the predicted thin-film data matches the target thin-film data. The comparison can indicate whether or not predicted thin-film data falls within tolerances or ranges established by the target thin-film data. For example, if the target thin-film thickness is between 2 nm and 4 nm, then the comparison will indicate whether the predicted thin-film data falls within this range. 
     At  412 , if the predicted thin-film data does not match the target thin-film data, then the process proceeds to  414 . At  414 , the analysis model  140  adjusts the dynamic process conditions data. From  414  the process returns to  408 . At  408 , the analysis model  140  again generates predicted thin-film data based on the static process conditions and the adjusted dynamic process conditions. The analysis model then compares the predicted thin-film data to the target thin-film data at  410 . At  412 , if the predicted thin-film data does not match the target thin-film data, then the process proceeds to  414  and the analysis model  140  again adjusts the dynamic process conditions. This process proceeds until predicted thin-film data is generated that matches the target thin-film data. If the predicted thin-film data matches the target thin-film data  412 , then the process proceeds to  416 . 
     At  416 , the process  400  adjusts the thin-film process conditions of the thin-film deposition system  100  based on the dynamic process conditions that resulted in predicted thin-film data within the target thin-film data. For example, the control system  124  can adjust fluid flow rates, deposition step durations, pressure, temperature, humidity, or other factors in accordance with the dynamic process conditions data. 
     At  418 , the thin-film deposition system  100  performs a thin-film deposition process in accordance with the adjusted dynamic process conditions identified by the analysis model. In one embodiment, the thin-film deposition process is an ALD process. However, other thin-film deposition processes can be utilized without departing from the scope of the present disclosure. In one embodiment, the thin-film deposition system  100  adjusts the process parameters based on the analysis model between individual deposition stages in a thin-film deposition process. For example, in an ALD process, the thin-film is deposited one layer at a time. The analysis model  140  can identify parameters to be utilized for deposition of the next layer. Accordingly, the thin-film deposition system can adjust deposition conditions between the various deposition stages. 
       FIG. 5  is a flow diagram of a thin-film deposition method  500 , according to one embodiment. At  502 , the method  500  includes providing static process conditions data to an analysis model. One example of an analysis model is the analysis model  140  of  FIG. 2 . At  504 , the method  500  includes selecting, with the analysis model, first dynamic process conditions data. At  506 , the method  500  includes generating, with the analysis model, first predicted thin-film data based on the static process conditions data and the first dynamic process conditions data. At  508 , the method  500  includes comparing the first predicted thin-film data to target thin-film data. At  510 , the method  500  includes, if the first predicted thin-film data matches the target thin-film data, performing a thin-film deposition process with deposition process conditions based on the static process conditions data and the first dynamic process conditions data. At  512 , the method  500  includes, if the first predicted thin-film data does not match the target thin-film data, adjusting the first dynamic process conditions data. 
       FIG. 6  is a flow diagram of a thin-film deposition method  600 , according to one embodiment. At  602 , the method  600  includes training an analysis model with a machine learning process to predict characteristics of thin films. One example of an analysis model is the analysis model  140  of  FIG. 2 . At  604 , the method  600  includes after training the analysis model, providing target thin-film data to the analysis model. At  606 , the method  600  includes identifying, with the analysis model, process conditions data that results in predicted thin-film data that complies with the target thin-film data. At  608 , the method  600  includes performing a thin-film deposition process on a semiconductor wafer with deposition process conditions in accordance with the process conditions data. One example of a semiconductor wafer is the semiconductor wafer  104  of  FIG. 1 . 
     In one embodiment, a thin-film deposition method includes providing static process conditions data to an analysis model. The method includes selecting, with the analysis model, first dynamic process conditions data. The method includes generating, with the analysis model, first predicted thin-film data based on the static process conditions data and the first dynamic process conditions data. The method includes comparing the first predicted thin-film data to target thin-film data. The method includes if the first predicted thin-film data matches the target thin-film data, performing a thin-film deposition process with deposition process conditions based on the static process conditions data and the first dynamic process conditions data. The method includes if the first predicted thin-film data does not match the target thin-film data, adjusting the first dynamic process conditions data. 
     In one embodiment, a thin-film deposition method includes training an analysis model with a machine learning process to predict characteristics of thin films. The method includes, after training the analysis model, providing target thin-film data to the analysis model. The method includes identifying, with the analysis model, process conditions data that results in predicted thin-film data that complies with the target thin-film data. The method includes performing a thin-film deposition process on a semiconductor wafer with deposition process conditions in accordance with the process conditions data. 
     In one embodiment, a thin-film deposition system includes a thin-film deposition chamber, a support configured to support a substrate within the thin-film deposition chamber, and a fluid source configured to provide a fluid into the thin-film deposition chamber during a thin-film deposition process. The system includes a control system configured to identify process conditions data for the thin-film deposition process based on a machine learning process and to control the first fluid source during the thin-film deposition process in accordance with the process conditions data. 
     Embodiments of the present disclosure provide thin films of reliable thickness and composition. Embodiments of the present disclosure dynamically adjust process parameters to ensure that thin films have desired properties. The various embodiments described above can be combined to provide further embodiments. All U.S. patent application publications and U.S. patent applications referred to in this specification and/or listed in the Application Data Sheet are incorporated herein by reference, in their entirety. Aspects of the embodiments can be modified, if necessary, to employ concepts of the various patents, applications and publications to provide yet further embodiments. 
     These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.