Patent Publication Number: US-11392040-B2

Title: System and method for performing extreme ultraviolet photolithography processes

Description:
BACKGROUND 
     Technical Field 
     The present disclosure relates to the field of photolithography. The present disclosure relates more particularly to extreme ultraviolet photolithography. 
     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. 
     The features on an integrated circuit die are produced, in part, with the aid of photolithography. Traditional photolithography techniques include generating a mask outlining the pattern of features to be formed on an integrated circuit die. The photolithography light source irradiates the integrated circuit die through the mask. The size of the features that can be produced via photolithography of the integrated circuit die is limited, in part, on the lower end, by the wavelength of light produced by the photolithography light source. Smaller wavelengths of light can produce smaller feature sizes. 
     Extreme ultraviolet light is used to produce particularly small features due to the relatively short wavelength of extreme ultraviolet light. For example, extreme ultraviolet light is typically produced by irradiating droplets of selected materials with a laser beam. The energy from the laser beam causes the droplets to enter a plasma state. In the plasma state, the droplets emit extreme ultraviolet light. The extreme ultraviolet light travels toward a collector with an elliptical or parabolic surface. The collector reflects the extreme ultraviolet light to a scanner. The scanner illuminates the target with the extreme ultraviolet light via a mask. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1A  is a side view of a photolithography system, according to one embodiment. 
         FIG. 1B  is a side view of the photolithography system of  FIG. 1A  in an operational state, according to one embodiment. 
         FIG. 1C  is a bottom view of a fluid distributor of the photolithography system of  FIG. 1A , according to one embodiment. 
         FIG. 1D  is a top view of a portion of the photolithography system of  FIG. 1A , according to one embodiment. 
         FIG. 2  is a top view of a portion of a photolithography system, according to one embodiment. 
         FIG. 3A  is a side view of a portion of a photolithography system, according to one embodiment. 
         FIG. 3B  is a bottom view of a fluid distributor of the photolithography system of  FIG. 3A , according to one embodiment. 
         FIG. 4A  is a side view of a photolithography system, according to one embodiment. 
         FIG. 4B  is a side view of a photolithography system, according to one embodiment. 
         FIG. 4 c    is a top view of the photolithography system of  FIG. 4B , according to one embodiment. 
         FIG. 5  is a block diagram of a control system of a photolithography system, according to one embodiment. 
         FIG. 6  is a block diagram of an analysis model of a control system, according to one embodiment. 
         FIG. 7  is a flow diagram of a method for operating a photolithography system, according to one embodiment. 
         FIG. 8  is a flow diagram of a method for operating a photolithography system, according to one embodiment. 
         FIG. 9  is a flow diagram of a method for operating a photolithography system, according to one embodiment. 
         FIG. 10  is a flow diagram of a method for operating a photolithography system, 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 many benefits to extreme ultraviolet radiation photolithography systems. Embodiments of the present disclosure reduce contamination of components of the extreme ultraviolet photolithography systems. Embodiments of the present disclosure dynamically adjust the flow of a buffer fluid into an extreme ultraviolet radiation generation chamber. The buffer fluid helps to prevent and remove contamination of sensitive components of the extreme ultraviolet radiation generation chamber. This helps to ensure that photolithography processes have adequate extreme ultraviolet radiation. Furthermore, because contamination is removed and prevented, sensitive components of the extreme ultraviolet radiation systems do not have to be replaced as frequently. The sensitive components can be extremely expensive. Accordingly embodiments of the present disclosure not only enhance the effectiveness of photolithography processes, but they also reduce the cost of operating a photolithography systems because components need to be replaced less frequently. 
       FIG. 1A  is a side view of an extreme ultraviolet (EUV) photolithography system  100 , in accordance with one embodiment.  FIG. 1A  illustrates the EUV photolithography system  100  in a non-operational state. The EUV photolithography system  100  includes a collector mirror  102  and a shield  104 . The collector mirror  102  and the shield  104  are coupled together to form an EUV generation chamber  101 . EUV light is generated in the EUV generation chamber  101 . The EUV light is passed from the EUV generation chamber  101  to the scanner  103 . The EUV light irradiates a photolithography target in the scanner  103  via a mask. 
     In one embodiment, the photolithography system  100  is a laser produced plasma (LPP) EUV radiation photolithography system. The photolithography system  100  includes a laser  106 , a droplet generator  114 , and a droplet receiver  116 . The laser  106 , the collector mirror  102 , and the droplet generator  114  cooperate to generate EUV radiation within the EUV generation chamber  101 . 
     As will be described in greater detail in relation to  FIG. 1B , the droplet generator  114  generates and outputs a stream of droplets. The droplets can include, in one example, liquid (melted) tin. Other materials can be used for the droplets without departing from the scope of the present disclosure. The droplets move at a high rate of speed toward the droplet receiver  116 . The photolithography system  100  utilizes the droplets to degenerate EUV light for photolithography processes. Extreme ultraviolet light typically corresponds to light with wavelengths between 1 nm and 125 nm. 
     The photolithography system  100  includes a fluid distributor  108 . The fluid distributor  108  includes a plurality of inlets  110  and an outlet  112 . The fluid distributor  108  includes an inner wall  111  and an outer wall  115 . The inner wall  111  and the outer wall  115  define a fluid chamber  113 . As will be described in more detail in relation to  FIG. 1B  and other figures, the fluid distributor  108  receives a buffer fluid at the inlets  110 , the buffer fluid flows through the fluid chamber  113  and is supplied into the EUV generation chamber  101  via the outlet  112 . The buffer fluid helps to reduce and remove contamination of the collector mirror  102  and the shield  104 . 
     The photolithography system  100  includes a fluid source  118  and mass flow controllers  120 . The fluid source  118  stores the buffer fluid. The mass flow controllers  120  receive the buffer fluid from the fluid source  118  and supply the buffer fluid to the fluid distributor  108 . In one embodiment, each mass flow controller  120  is coupled to a respective inlet  110  of the fluid distributor  108  via a respective fluid line  136 . Alternatively, the mass flow controllers  120  can be positioned in the inlets  110 . The mass flow controllers control the flow rate of the buffer fluid into the fluid distributor  108 . 
     The photolithography system  100  includes sensors  132 . The sensors  132  can be positioned external to the EUV generation chamber  101 , within the EUV generation chamber  101 , or partially within and partially outside of the EUV generation chamber  101 . The sensors  132  can include light sensors, electron sensors, plasma sensors, or other kinds of sensors for sensing conditions within the EUV generation chamber  101 . The photolithography system  100  can include an array of various types of sensors  132  positioned at various locations within and without the EUV generation chamber  101 . 
     The photolithography system  100  includes a control circuit  134 . The control circuit  134  is coupled to the droplet generator  114 , a droplet receiver  116 , the laser  106 , the fluid source  118 , and the mass flow controllers  120 . The control circuit  134  controls the various components of the photolithography system  100 . As will be described in more detail below, the control system  134  can operate the various components of the photolithography system  100  to reduce contamination of the reflector surface  128  of the collector mirror  102  and the interior surface  130  of the shield  104 . 
       FIG. 1B  is an illustration of the photolithography system  100  of  FIG. 1A  in an operational state, according to one embodiment. In the operational state, the droplet generator  114  generates and outputs a stream of droplets  140 . The droplets can include tin, though droplets of other material can be utilized without departing from the scope of the present disclosure. The droplets  140  move at a high rate of speed toward the droplet receiver  116 . 
     In one embodiment, the droplet generator  114  generates between 40,000 and 100,000 droplets per second. The droplets  140  have an initial velocity of between 60 m/s to 200 m/s. The droplets have a diameter between 10 μm and 200 μm. The droplet generator  114  can generate different numbers of droplets per second than described above without departing from the scope of the present disclosure. The droplet generator  114  can also generate droplets having different initial velocities and diameters than those described above without departing from the scope of the present disclosure. 
     The laser  106  is positioned behind the collector mirror  102 . During operation, the laser  106  outputs pulses of laser light  142 . The pulses of laser light  142  are focused on a point through which the droplets pass on their way from the droplet generator  114  to the droplet receiver  116 . Each pulse of laser light  142  is received by a droplet  140 . When the droplet  140  receives the pulse of laser light  142 , the energy from the laser pulse generates a high-energy plasma from the droplet  140 . The high-energy plasma outputs EUV radiation. 
     In one embodiment, the laser  106  is a carbon dioxide (CO 2 ) laser. The CO 2  laser emits radiation or laser light  142  with a wavelength centered around 9.4 μm or 10.6 μm. The laser  106  can include lasers other than carbon dioxide lasers and can output radiation with other wavelengths than those described above without departing from the scope of the present disclosure. 
     In one embodiment, the laser  106  irradiates each droplet  140  with two pulses. A first pulse causes the droplet  140  to flatten into a disk like shape. The second pulse causes the droplet  140  to form a high temperature plasma. The second pulse is significantly more powerful than the first pulse. The laser  106  and the droplet generator  114  are calibrated so that the laser  106  emits pairs of pulses such that each droplet  140  is irradiated with a pair of pulses. For example, if the droplet generator  114  outputs 50,000 droplets per second, the laser  106  will output 50,000 pairs of pulses per second. The laser  106  can irradiate droplets  140  in a manner other than described above without departing from the scope of the present disclosure. For example, the laser  106  may irradiate each droplet  140  with a single pulse or with more pulses than two. Moreover, the primary laser here can not only cause droplets to flatten into disk-like shape can be mist or vapor state. 
     In one embodiment, the droplets  140  are tin. When the tin droplets  140  are converted to a plasma, the tin droplets  140  output EUV radiation  146  with a wavelength centered between 10 nm and 15 nm. More particularly, in one embodiment, the tin plasma emits EUV radiation with a central wavelength of 13.5 nm. These wavelengths correspond to EUV radiation. Materials other than tin can be used for the droplets  140  without departing from the scope of the present disclosure. Such other materials may generate EUV radiation with wavelengths other than those described above without departing from the scope of the present disclosure. 
     In one embodiment, the radiation  146  output by the droplets scatters randomly in many directions. The photolithography system  100  utilizes the collector mirror  102  to collect the scattered EUV radiation  146  from the plasma and output the EUV radiation toward a photolithography target. 
     In one embodiment, the collector mirror  102  is a parabolic or elliptical mirror. The scattered radiation  146  is collected and reflected by the parabolic or elliptical mirror with a trajectory toward the scanner  103 . The scanner  103  utilizes a series of optical conditioning devices such as mirrors and lenses to direct the EUV radiation to the photolithography mask. The EUV radiation  146  reflects off of the mask onto a photolithography target. The EUV radiation  146  reflected from the mask patterns a photoresist or other material on a semiconductor wafer. For purposes of the present disclosure, particularities of the mask and the various configurations of optical equipment in the scanner  103  are not shown. 
     In one embodiment, the collector mirror  102  includes a central aperture  126 . The pulses of laser light  142  pass from the laser  106  through the central aperture  126  toward the stream of droplets  140 . This enables the collector mirror  102  to be positioned between the laser  106  and the photolithography target. 
     Because the droplets  140  traveled a high rate of speed are either in a liquid or plasma state, when the droplets  140  are received into the droplet receiver  116 , there may be a splash effect. It is possible that some splash back may occur. The splash back may result in material from the droplets  140  landing on the reflective surface  128  of the collector mirror  102  or on the interior surface  130  of the shield  104 . Additionally, it is possible that when the droplets  140  are irradiated with laser light  142 , that some plasma vapor may be generated. The plasma vapor  140  may eventually deposit on the surface  128  of the collector mirror  102  and on the interior surface  130  of the shield  104 . 
     The accumulation of material from the droplets  140  on the surface  128  of the mirror  102  and on the interior surface  103  of the shield  104  may result in poor performance of the photolithography system  100 . For example, the photolithography system  100  depends on a sufficient amount of EUV light  146  being reflected from the collector mirror  102  into the scanner  103 . If an insufficient amount of EUV light  146  is reflected from the collector mirror  102  into the scanner  103 , then the photolithography system  100  may not be able to properly perform a photolithography process. When material from the droplets  140  accumulates on the reflective surface  128  of the collector mirror  102 , then the reflectivity of the reflective surface  128  is reduced. This can result in an insufficient amount of EUV light  146  being provided to the scanner  103 . Furthermore, accumulation of material on the interior surface  130  of the shield  104  can also have an adverse impact on the performance of the photolithography system  100 . 
     The photolithography system  100  implements the fluid distributor  108  in order to reduce the accumulation of material from the droplets  140  on the surface  128  of the mirror  102  and on the interior surface  130  of the shield  104 . The fluid distributor  108  distributes a buffer fluid  148  into the EUV generation chamber  101 . The buffer fluid  148  is selected to remove accumulated droplet material from the surface  128  of the mirror  102  and the interior surface  130  of the shield  104 . The buffer fluid  148  is also selected to inhibit material from the droplets  140  from accumulating on the reflective surface  128  and on the interior surface  130 . 
     In one embodiment, the droplets  140  are tin and the buffer fluid  148  is a hydrogen gas. The hydrogen gas can include H2. The hydrogen gas reacts with the tin and etches accumulated tin material from the surfaces  128  and  130 . Accordingly, one effect of the hydrogen buffer fluid is to chemically react with the tin in order to remove the tin from the surfaces  128  and  130 . Another effect of the hydrogen buffer fluid  148  is to physically carry tin material away from the surfaces  128  and  130 . In other words, the flow of the hydrogen buffer fluid  148  can physically carry tin material away from the surfaces  128  and  130 . One example has been given in which the droplets  140  are tin in the buffer fluid  148  is hydrogen gas, other materials from the droplets  140  and other buffer fluids  148  can be used without departing from the scope of the present disclosure. 
     One challenge associated with reducing the accumulation of material on the surfaces  128  and  130  is the uneven flow of the buffer fluid  148  along the various regions of the surfaces  128  and  130 . If the buffer fluid  148  does not flow along all areas of the surfaces  128  and  130 , then it is possible that material from the droplets  140  may accumulate at these regions of buffer fluid flow. 
     In order to promote sufficient flow of the buffer fluid  140  along all areas of the surfaces  128  and  130 , the fluid distributor  108  includes a plurality of inlets  110 . In particular, the fluid distributor  108  includes four or more inlets  110 . The inlets  110  can be positioned evenly along the lower surface of the fluid distributor  108 . The large number of inlets  110  and the even spacing of the large number of inlets  110  can result in a more even flow of the buffer fluid  148  at all positions of the outlet  112 . This dynamic may be better understood with reference to  FIGS. 1C and 1D . 
       FIG. 1C  is a bottom view of the fluid distributor  108  of  FIGS. 1A and 1B , according to one embodiment. In the example of  FIG. 1C , the fluid distributor  108  includes eight inlets  110 . The eight inlets  110  each receive a flow of the buffer fluid  148  from a respective fluid line  136  (not shown in  FIG. 1C ). The fluid or sugar  108  includes a solid bottom surface  117 . The solid bottom surface  117  prevents flow of fluid into the fluid chamber  113  of the fluid distributor  108  except through the inlets  110 . Other numbers and shapes of inlets  110  can be utilized without departing from the scope of the present disclosure. In order to promote uniform coverage of gas flow on the various regions of the surfaces  128  and  130 , it can be beneficial to have a number of inlets  110  that enables symmetrical positioning of the inlets  110 . 
       FIG. 1D  is a top view of a portion of the fluid distributor  108  and the collector mirror  102  of  FIG. 1A , according to one embodiment. In the example of  FIG. 1D , the top of the fluid distributor  108  is entirely open between the inner wall  111  and the outer wall  115 . In other words, the top of the fluid distributor  108  corresponds to a single continuous outlet  112  that enables the buffer fluid  148  to flow from the fluid chamber  113  into the EUV generation chamber  101 . Other configurations of a fluid distributor  108  can be utilized without departing from the scope of the present disclosure. For example, the fluid distributor  108  may include a plurality of individual outlets  112  separated from each other by a solid top surface. The top view of  FIG. 1D  also illustrates the droplet generator  114  and the droplet receiver  116 . The top view of  FIG. 1B  does not illustrate the shield  104 . 
     The buffer fluid  148  flows into the fluid chamber  113  of the fluid distributor  108  via the inlets  110 . The buffer fluid  148  flows from the fluid chamber  138  into the EUV generation chamber  101  via the continuous outlet  112 . This configuration promotes substantial flow of the buffer fluid  148  along all regions of the surfaces  128  and  130   
     In one embodiment, the flow of the buffer fluid  148  from the fluid source  118  into each fluid line  136  may be controlled by a respective mass flow controller  120 . Each mass flow controller  120  can be selectively operated to provide a selected flow of the buffer fluid  148  into the respective inlet  110  via the corresponding fluid line  136 . In one embodiment, the mass flow controllers  120  are each positioned within a respective inlet  110 . In this case, the fluid lines  136  deliver fluid from the fluid source  118  to the inlets  110 . The mass flow controllers  120  control the flow rate of the buffer fluid  148  into the fluid chamber  113 . 
       FIG. 2  is a top view of a portion of a photolithography system  200 , according to one embodiment. The photolithography system  200  is substantially similar to the photolithography system  100  of  FIGS. 1A-1D , except that the fluid distributor  108  includes multiple mutually exclusive fluid chambers  113 . The mutually exclusive fluid chambers  113  are defined by internal partitions  150 . The internal partitions  150  extend from the bottom surface  117  (see  FIG. 1C ) of the fluid distributor  108  to a top of the fluid distributor  108 . Each fluid chamber  113  receives the buffer fluid  148  through a respective inlet  110 . The inlets  110  are shown in dashed lines. 
     The fluid distributor  108  of  FIG. 2  includes a plurality of outlets  112 . Each outlet  112  extends between two of the internal partitions  150 . Accordingly, each fluid chamber  113  outputs the buffer fluid  148  through a respective outlet  112 . 
     In one embodiment, the fluid distributor  108  of  FIG. 2  enables enhanced control of buffer fluid flow from particular regions of the fluid distributor  108 . As will be set forth in more detail below, the control circuit  134  may determine that flow rates of the buffer fluid  148  should vary at different output regions of the fluid distributor  108 . In this case, the control system  134  can selectively control the various mass flow controllers  120  can provide varying flow rates of the buffer fluid  148  into the individual fluid chambers  113  via the inlets  110 . Because the fluid chambers  113  are separated from each other by the internal partitions  150 , selectively controlling the mass flow controllers  120  enables selectively controlling the flow of the buffer fluid  148  from various parts of the fluid distributor  148 . 
       FIG. 3A  is an enlarged sectional view of a portion of a photolithography system  300 , according to one embodiment. The photolithography system  300  can be substantially similar to the photolithography system  100  of  FIGS. 1A-1D , except that the fluid distributor  108  of the photolithography system  300  includes some additional features with respect to the fluid distributor  108  of  FIGS. 1A-1D . In particular, the fluid distributor  108  includes an internal partition  180 . The internal partition  180  separates a first fluid chamber  113   a  from a second fluid chamber  113   b  within the fluid distributor  108 . The disposition of the internal partition  180  can be more fully understood with reference to  FIG. 3B . 
       FIG. 3B  is a bottom view of the fluid distributor  108  of  FIG. 3A , according to one embodiment.  FIG. 3B  illustrates that the internal partition  180  separates a plurality of inlets  110   a  from a plurality of inlets  110   b . Furthermore, the partition  180  is shown in dashed lines where the partition  180  is covered from view by the bottom surface  117  of the fluid distributor  108 . Accordingly, in one embodiment, the internal partition  180  extends in a circle between the inner wall  111  and the outer world wall  115 . Though not illustrated in  FIG. 3A , the fluid distributor  108  can also include internal partitions  150  as shown in  FIG. 2 . 
     Returning to  FIG. 3A , a mass flow controller  120   a  is positioned in the inlet  110   a . A mass flow controller  120   b  is positioned in the inlet  110   b . Each inlet  110   a  can include a mass flow controller  120   a . Each inlet  110   b  can include a mass flow controller  120   b . In an example in which the internal partitions  150  are not present, there is a single continuous first fluid chamber  113   a  and the single continuous second fluid chamber  113   b . Each of the inlets  110   a , with the mass flow controllers  120   a , provides the buffer fluid  148  into the first fluid chamber  113   a . Each of the inlets  110   b , with the mass flow controllers  120   b , provides the buffer fluid  148  into the second fluid chamber  113   b . As will be described in more detail below, the mass flow controllers  110   a ,  110   b  can be selectively operated to provide varying flow rates of the buffer fluid  148  into the first and second fluid chambers  113   a ,  113   b . Each of the inlets  110   a ,  110   b  can receive the buffer fluid  148  from respective fluid lines  136  (not shown). In some embodiments, the mass flow controllers  120   a ,  120   b  are positioned external to the inlets  110   a ,  110   b . The fluid distributor  108  includes a first outlet  112   a  that provides the buffer fluid  148  from the first fluid chamber  113   a  into the EUV generation chamber  101 . The fluid distributor  108  includes a second outlet  112   b  that provides the buffer fluid  148  from the second fluid chamber  113   b  into the EUV generation chamber  101 . 
     The photolithography system  300  includes a director vane  156 . The director vane  156  is rotatably coupled to a joint  154 . The director vane  156  can rotate through various positions via the joint  154 . The director vane  156  can help direct buffer fluid  148  along the surface  130  of the shield  104  and the surface  128  of the collector mirror  102 . As will be described in more detail below, the control system  134  can control the position of the director vane  156 . Accordingly, the director vane  156  can selectively affect the flow of the buffer fluid  148  from the outlets  112   a ,  112   b.    
     Though not shown in  FIG. 3A , the photolithography system  300  can include a plurality of director vanes  156  positioned around the top of the fluid distributor  108 . Each of the vanes  156  can be selectively controlled to direct the flow of buffer fluid  148 . In an example in which the internal partitions  150  are present, there may be a plurality of first fluid chambers  113   a  and second fluid chambers  113   b , and corresponding first outlets  112   a  and second outlets  112   b . There may be a respective director vane  156  for each pair of first and second outlets  112   a ,  112   b . The director vanes  156  may have a width corresponding to the distance between internal partitions  150 . 
       FIG. 4  is an illustration of a photolithography system  400 , according to one embodiment. The photolithography system  400  is substantially similar to the photolithography system  100  of  FIGS. 1A-1D , except that the photolithography system  400  includes the particular array of light sensors  162  and charge coupled devices  160 . Though not shown in  FIG. 4 , the photolithography system  400  also includes the droplet generator  114  and the droplet receiver  116 . 
     In one embodiment, the light sensors  162  are positioned to detect side scattering of EUV radiation from the plasmatized droplets  140 . The light sensors  162  can be part of a side scatter detection system  116  as described in relation to  FIG. 1 . 
     In one embodiment, the light sensors  162  collectively detect a current level of intensity of EUV light being generated in the EUV generation chamber  101 . A plurality of light sensors  162  can be positioned laterally around the outside of the shield  104  at various heights. The plurality of light sensors  162  are configured to sense EUV light. The light sensors  160  to provide sensor signals to the control system  134 . The control system  134  can analyze the sensor signals from the light sensors  162  to determine the reflectivity of various regions of the surface  128  of the collector mirror  102  and the surface  130  of the shield  104 . As will be set forth in more detail below, the control system  134  can utilize these determinations to selectively increase the flow of the buffer fluid  148  to regions of the surface  128  and the surface  130  on which are accumulated matter from the droplets  140 . The control system  134  can control the mass flow controllers  120 , the director vanes  156 , and other aspects of the photolithography system  400  in order to remove accumulated droplet material from the surfaces  128  and  130 . 
     In one embodiment, the light sensors  162  are utilized to detect Thomson scattering of EUV radiation from the plasmatized droplets. The Thomson scattering phenomenon is due to elastic scattering of electromagnetic radiation by a single free charged particle. This can be utilized as a high temperature plasma diagnostic technique. In particular, Thomson scattering measurements can be utilized to determine the ionization rate in the droplets. The intensity of scattered light is based, in part, on the extent of the laser to plasma interaction. Accordingly, the ionization rate can be retrieved from the intensity of scattered light. Thomson scattering intensity is independent of incident light wavelength. Thus, Thomson scattering can be useful to analyze the relationship between the electric field of incident light and electron density. The light sensors  162  generate signals indicative of the intensity of side scattered light and pass the signals to the control system  134 . 
     In one embodiment, the light received by the light sensors  162  can be utilized to determine a shape of the droplets  140  after being impacted by a first pulse of laser light  142  and the second pulse of laser light  144 . In general, the first pulse of laser light flattens the receiving droplet  140  into a pancake shape. The pancake shaped droplet  140  can be tilted at various angles relative to the direction of travel of the droplets  140 . The pancake shape angle of the droplet  140  can result in accumulation of droplet matter in particular locations on the surface  128  in the surface  130 . Accordingly, the control system  134  can analyze the light received by the light sensors  162  to determine the shape and angle of the droplet  140  after being impacted by the first pulse of laser light  142  and after being impacted by the second pulse of laser light  142 . 
     The total intensity of EUV light emitted by plasma can be estimated or calculated based on the amount of light received by the light sensors  162 . On average, the plasma will emit EUV light at the same rate in all directions, or with known relationships between various scattering directions. Accordingly, the total intensity of EUV light can be estimated or calculated based on the light received by the light sensors  162 . 
     In one embodiment, the light sensors  162  can be utilized to detect contamination of the surface  128  of the collector  102 . The reflectivity of the surface  102  will be reduced at contaminated areas. Accordingly, a subset of the light sensors  162  and/or other light sensors positioned in the scanner can be utilized to determine the reflectivity each of a plurality of areas on the surface  128  of the collector  102 . The light sensor information can be utilized to generate a contamination map. The contamination map indicates the contamination level of each area of the surface  128 . The control system  134  can generate the contamination map based on sensor signals received from the light sensors  162  and/or other sensors. 
     In one embodiment, the control system can adjust buffer fluid flow parameters to provide increased buffer fluid flow to those regions of the surface  128  that are more heavily contaminated. The control system  134  can selectively control the mass flow controllers  110  and the director vanes  156  to increase or decrease the flow of the buffer fluid onto the various areas of the surface  128  based on the contamination map. 
     In one embodiment, the photolithography system  400  includes a plurality of lenses  164 . Each lens  164  is positioned to focus light scattered from the plasmatized droplets  140  onto or into the light sensors  162 . Though the lenses  164  are shown as being positioned external to the EUV generation chamber  101 , in practice, the lenses  164  may be positioned in other locations or orientations than those shown in  FIG. 4 . 
     In one embodiment, the one or more lenses  164  are coupled to a rim of the collector mirror  102 . The lenses  164  can be positioned in a same lateral plane as the droplet generator  114  and droplet receiver  116 . Side scattered light from the plasma droplets passes through these lenses  164  and is focused onto the light sensors  162 . 
     In one embodiment, the one or more lenses  164  can correspond to windows in the shield  104  of the EUV generation chamber  101 . Accordingly, a shield  104  of the EUV generation chamber  101  can include windows or apertures. Lenses  164 , or lensing materials, can be positioned in the windows or apertures. When light is scattered from the plasma, the light passes through the windows and onto the light sensors. The lenses  164  positioned in the windows or apertures can focus the light onto the light sensors  162 . 
     In one embodiment, the light sensors  162  provide sensor signals to the control system  134 . The sensor signals are indicative of the intensity of light received by the light sensors  162 . The control system  134  receives the sensor signals and can adjust parameters of the photolithography system  400  responsive to the sensor signals. 
     In one embodiment, the control system  134  adjust parameters of the photolithography system  400  in order to more effectively prevent and remove accumulations of droplet matter on the surfaces  128  and  130 . In one embodiment, the control system  134  adjusts parameters of the photolithography system  400  in order to more effectively generate EUV radiation. The control system  134  can adjust one or more of buffer fluid flow rates into various inlets  110 , the positions of director vanes  156 , droplet speed, droplet size, laser pulse power, laser pulse timing, laser pulse train profile, initial droplet temperature, pressure within the EUV generation chamber, or other parameters. 
     In one embodiment, adjusting aspects of the laser pulses can include adjusting the plasma generation pulse that generates the plasma from the flattened droplet  140 . The plasma generation pulse is utilized to generate a plasma from the flattened droplet. The timing, pulse shape, and power of the plasma generation pulse can be adjusted by the control system  134  responsive to the sensor signals from the light sensors  162 . 
     In one embodiment, the photolithography system includes charged particle detectors  160 . The process of generating a plasma results in the generation of charged particles in the droplets  140 . Some of the charged particles may be ejected from or may otherwise travel away from the droplets. The characteristics of the charged particles ejected from the plasma are indicative of characteristics of the plasma itself. The characteristics of the charged particles can include velocity of the charged particles, the energy of the charged particles, the trajectory of the charged particles, the number of charged particles emitted per droplet, and other characteristics. 
     In one embodiment, the charged particle detectors  160  are coupled to the control system  134 . The charged particle detectors  160  are configured to generate sensor signals indicative of parameters of the charged particles. The charged particle detectors  160  pass the sensor signals to the control system  134 . 
     In one embodiment, an array of charged particle detectors  160  is positioned within the EUV generation chamber  101 . The array of charged particle detectors  160  can be positioned to detect a variety of charged particle trajectories within the EUV generation chamber  101 . In other words, the charged particle detectors  160  can be positioned in various locations throughout the EUV generation chamber  101 . Each of the charged particle detectors  160  detects impacts of charged particles on the charged particle detectors  160 . The charged particle detectors  160  pass sensor signals indicative of characteristics of the charged particles to the control system  134 . 
       FIG. 4A  illustrates the charged particle detectors  160  as being positioned on an exterior wall of the EUV generation chamber  101 . However, the charged particle detectors  160  can be positioned within the EUV generation chamber  101 . For example, the charged particle detectors  160  can be positioned on the interior surface  130  of the shield  104  of the EUV generation chamber  101 . Alternatively, the charged particle detectors  160  can be positioned, supported, or arranged in other ways in an interior or an exterior of the EUV generation chamber  101 . In one embodiment, the EUV generation chamber  101  can include apertures that permit charged particles to pass from an interior of the EUV generation chamber  101  to the charged particle detectors  160 . In one embodiment, may include charged particle lenses that direct charged particles into the charged particle detectors  160  via electromagnetic forces. 
     In one embodiment, the charged particle detectors  160  include charge coupled devices configured to detect impacts from charged particles. The charge coupled devices generate signals each time a charged particle impacts the charge coupled devices. The charge coupled devices then pass sensor signals to the control system  134 . 
     In one embodiment, the charge coupled devices for detecting charged particles include electron multiplying charge coupled devices. The electron multiplying charge coupled devices are frame transfer charge coupled devices that include an output register. The electron multiplying charge coupled device can include a fluorescent film or sheet positioned in front of a sensor area of the charge coupled device. When charged particles impact the florescent film, the florescent film emits light. The light is sensed by the charge coupled device and the charge coupled device counts the impact of the charged particle. 
     In one embodiment, the charged particle detectors can include Faraday cups. A Faraday cup is a conductive cup that is configured to detect or capture charged particles in a vacuum, such as a vacuum in the EUV generation chamber  101 . The Faraday cup generates a current based on the charged particles captured by the Faraday cup. This current can be utilized to determine the number of charged particles that impact the cup. The Faraday cups can provide sensor signals to the control system  134  indicative of the number of charged particles collected or captured by the Faraday cups. 
     In one embodiment, the control system  134  can adjust parameters of the photolithography system  100  responsive to the sensor signals from the charged particle detectors  160 . The control system  134  can adjust the same sorts of parameters of the photolithography system  400  as those described previously in relation to the light sensors  162 . The control system  134  can adjust the parameters of the photolithography system  200  in order to more effectively generate EUV radiation for performing photolithography. 
     In one embodiment, the control system  134  can generate a 3D model of the droplets  140  after the flattening pulse and/or the plasma generation pulse. Because the charged particle detectors  160  are positioned in various locations throughout the EUV generation chamber  101  and/or the scanner  103 , the sensor signals from the various charged particle detectors can be utilized to generate a 3D model of the droplets prior to injection of the charged particles. The 3D model can indicate a shape of the flattened droplets after the flattening pulse and before the plasma generation pulse. Alternatively, or additionally, the 3D model can indicate a shape of the flattened droplets after the plasma generation pulse. The control system  134  can analyze the 3D model in order to determine whether the flattening pulse, the plasma generation pulse, the droplet speed, droplet size, the initial droplet temperature, or other parameters should be adjusted in order to generate a plasma having a selected shape from the droplets. The 3D model can help predict where droplet material will accumulate on the surfaces  128  and  130 . Accordingly, the control system  134  can utilize the 3D model to determine how to direct the flow of the buffer fluid  148 . 
     In one embodiment, the control system  134  can adjust parameters of the photolithography system  400  responsive to sensor signals from the light sensors  162  and the charged particle detectors  160 . The control system  134  can generate a model of the flattened droplets  140 , the plasmatized droplets  140 , or of other aspects of the plasma or droplets  140  based on the combination of sensor signals from both the light sensors  162  and the charged particle sensors  130 ,  136 . 
     In one embodiment, the control system  134  utilizes machine learning to accurately adjust the parameters of the photolithography system  400  in order to avoid, reduce, or remove the accumulation of droplet matter on the surfaces  128  and  130 . Accordingly, the control system  134  can include a machine learning model that can be trained to adjust one or more parameters of buffer fluid flow, the laser pulses, or droplets  140  responsive to sensor signals received from the light sensors  162  and/or the charged particle detectors  160 . 
     In one embodiment, the machine learning model includes a neural network. The machine learning model can include one or more neural network-based supervised machine learning models. The machine learning model can include one or more unsupervised machine learning models. Other types of machine learning models can be utilized for controlling the speed of droplets without departing from the scope of the present disclosure. For example, machine learning models other than neural network-based machine learning models can be utilized by the control system  134 . 
     The image generated from electron multiplying type charge coupled devices may need post processing due to different electron energies with different deflective directions. The image can include energy (distribution on image) and counts (intensity on image) information. Therefore, to recover an XY plane image to resolve original distribution, corrections may be made. By known optics specification, the position in a volume with a particular geometry can be estimated. 
     In one embodiment, by recording the information from Thomson scattering phenomenon and electron distribution in space, the original electron density distribution from the plasma could be computed in multiple dimensions. From Thomson scattering theory, the relation between incident light intensity and the electron density distribution can also be retrieved. By analyzing the relation of electrons distribution in space and combining the results in three dimensions, the control system  134  can compose a 3D plasma model. 
     In one example, the X-Y distribution of the plasma can be calculated based on side-scattered light information. The side scattered light information can be collected by light sensors  162  positioned near the collector mirror. The Z distribution of the plasma can be calculated based on the charged particle data sensed by the charged particle sensors  160  further away from the collector  102  in the vertical direction. Some of the charged particle sensors  160  may be positioned in the scanner or near the scanner. The 3D model may correspond to calculating the X-Y and Z distribution of the plasma, based on these parameters. The 3D model indicates the quality of the plasma. Accordingly, the control system  134  can generate the 3D plasma model based on sensor signals from the light sensors  162  and the charged particle sensors  160 . The 3D models can be utilized to assist in identifying adjustments to the prepulse laser, the plasmatizing laser, and the droplets. 
       FIG. 4B  is an illustration of the EUV photolithography system  400  of  FIG. 4A  and including a plasmatized droplet  140 . The plasmatized droplet  140  has been plasmatized after receiving the plasma laser pulse from the laser  106 . Plasmatized droplet  140  is flattened. The flattened droplet may be called a plasma pancake. The flattened droplet  140  is tilted at an angle Theta with respect to horizontal. The angle of the flattened droplet  140  can affect the location of accumulated contamination  171  on the surface  128  of the collector  102 . The 3D model of the plasma described previously indicates the tilt angle Theta. Accordingly, the 3D model can be utilized to predict the location of accumulation of contamination  171 . 
       FIG. 4C  is a top view of the EUV photolithography system  400 , according to one embodiment. The top view of  FIG. 4C  illustrates that the light sensors  162  and the lenses  164  are positioned radially around the collector  102 . Though not shown in  FIG. 4C , the on the surface charged particle detectors  160  may also be radially arranged.  FIG. 4C  also illustrates the plasmatized droplet  140  of  FIG. 4B . The 3D model described previously can indicate the shape of the droplet  140  in the horizontal plane as seen from the top view of  FIG. 4C .  FIG. 4C  also shows the accumulated contamination  171  on the surface  128  of the collector  102 . 
       FIG. 5  is a block diagram of the control system  134 , according to one embodiment. The control system  134  of  FIG. 5  is configured to control operation of a EUV photolithography system, according to one embodiment. The control system  134  utilizes machine learning to predict droplet matter accumulation based on the plasma generation parameters and the buffer fluid flow parameters. 
     In one embodiment, the control system  134  includes an analysis model  180  and a training module  170 . The training module trains the analysis model  180  with a machine learning process. The machine learning process trains the analysis model  180  to predict droplet matter accumulation based on EUV system parameters including buffer fluid flow parameters and EUV generation parameters. Although the training module  170  is shown as being separate from the analysis model  180 , in practice, the training module  170  may be part of the analysis model  180 . 
     The control system  134  includes, or stores, training set data  172 . The training set data  172  includes historical EUV system parameters data  174  and contamination data  176 . The historical EUV system parameters data  174  includes EUV generation parameters and buffer flow parameters for a large number of historical EUV generation processes. The contamination data  176  includes, for each historical EUV generation process, data indicating the accumulation of droplet matter at various regions of the surface  128  and the surface  130 . As will be set forth in more detail below, the training module  170  utilizes the historical EUV system parameters data  174  and the contamination data  176  to train the analysis model  180  with a machine learning process. 
     In one embodiment, the historical EUV system parameters data  174  includes data related to plasma generation parameters and buffer fluid flow parameters. The plasma generation parameters can include droplet speed, droplet size, laser pulse energies, laser pulse timing, laser pulse location, 3D models of plasma generation, models of droplet shape after the first laser pulse and after the second laser pulse, and other parameters related to the generation of EUV radiation for photolithography processes. The buffer fluid flow parameters can include flow rates from individual mass flow controllers, the flow rates from the outlet  112  or individual outlets  112 , or other parameters related to the flow of the buffer fluid  148 . 
     In one embodiment, the contamination data  176  includes for each historical EUV system parameters in the historical EUV system parameters data  174 , a respective label. Each label indicates the accumulation of droplet material at various regions of the surfaces  128  and  130 . 
     In one embodiment the analysis model  180  includes a neural network. Training of the analysis model  180  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  170  utilizes the training set data  172  to train the neural network with a machine learning process. During the training process, the neural network receives, as input, historical EUV system parameters data  174  from the training set data  172 . During the training process, the neural network outputs predicted contamination data. The predicted contamination data predicts, for each set of historical EUV system parameters provided to the analysis model  180 , the contamination map corresponding to that set of data. The training process trains the neural network to generate predicted contamination data that matches the contamination data  176  for each set of historical EUV system 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  134  compares, for each set of historical EUV system parameters data, the predicted contamination data to the actual label from the contamination data  176 . The control system generates an error function indicating how closely the predicted contamination data matches the contamination data  176 . The control system  134  then adjusts the internal functions of the neural network. Because the neural network generates predicted contamination data based on the internal functions, adjusting the internal functions will result in the generation of different predicted contamination data for a same set of historical EUV system parameters data. Adjusting the internal functions can result in predicted contamination data that produces larger error functions (worse matching to the contamination data  176 ) or smaller error functions (better matching to the contamination data  176 ). 
     After adjusting the internal functions of the neural network, the historical EUV system parameters data  174  is again passed to the neural network and the analysis model  180  again generates predicted contamination data. The training module  170  again compares the predicted contamination data to the contamination data  176 . The training module  170  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 contamination data that matches the contamination data  176  across the entire training set. 
     At the beginning of the training process, the predicted contamination data likely will not match the contamination data  176  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 contamination data that match the contamination data  176 . Matching can be based on a selected threshold. For example, the selected threshold error can be 5%. In this case, if the error is less than 5%, then the predicted contamination data is considered to match the contamination data  176 . Identification of a set of internal functions that results in predicted contamination data that matches the contamination data  176  corresponds to completion of the training process. Once the training process is complete, the neural network is ready to be used to adjust EUV generation parameters and buffer fluid flow parameters. In one embodiment, after the analysis model  180  has been trained, the analysis model  180  can be utilized to analyze the EUV generation parameters and the buffer fluid flow parameters and predict droplet accumulation of various regions of the surfaces  128  and  130 . 
     In one embodiment, the control system  134  includes processing resources  182 , memory resources  184 , and communication resources  186 . The processing resources  182  can include one or more controllers or processors. The processing resources  182  are configured to execute software instructions, process data, make photolithography control decisions, perform signal processing, read data from memory, write data to memory, and to perform other processing operations. The processing resources  182  can include physical processing resources  182  located at a site or facility of the EUV photolithography system. The processing resources can include virtual processing resources  182  remote from the site EUV photolithography system or a facility at which the EUV photolithography system is located. The processing resources  182  can include cloud-based processing resources including processors and servers accessed via one or more cloud computing platforms. 
     In one embodiment, the memory resources  184  can include one or more computer readable memories. The memory resources  184  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  180 . The memory resources  184  can store data associated with the function of the control system  134  and its components. The data can include the training set data  172 , current process conditions data, and any other data associated with the operation of the control system  134  or any of its components. The memory resources  184  can include physical memory resources located at the site or facility of the EUV photolithography system  100 . The memory resources can include virtual memory resources located remotely from site or facility of the EUV photolithography system  100 . The memory resources  184  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  134  to communicate with equipment associated with the EUV photolithography system  100 . For example, the communication resources  186  can include wired and wireless communication resources that enable the control system  134  to receive the sensor data associated with the EUV photolithography system and to control equipment of the EUV photolithography system. The communication resources  186  can enable the control system  134  to control the various components of the EUV photolithography system. The communication resources  186  can enable the control system  134  to communicate with remote systems. The communication resources  186  can include, or can facilitate communication via, one or more networks such as wide networks, wireless networks, the Internet, or an intranet. The communication resources  186  can enable components of the control system  134  to communicate with each other. 
     In one embodiment, the analysis model  180  is implemented via the processing resources  182 , the memory resources  184 , and the communication resources  186 . The control system  134  can be a dispersed control system with components and resources and locations remote from each other and from the EUV photolithography system. 
     The components, functionality, and processes described in relation to the control system  134  and the analysis model  180  can be extended to the control systems and analysis models described in relation to  FIGS. 1-4 . 
       FIG. 6  is a block diagram of an analysis model  180 , according to one embodiment. The analysis model  180  can be part of the control system  134  of  FIGS. 1A and 5  and can operate in conjunction with the systems and processes described in relation to  FIGS. 1-6 , according to one embodiment. The analysis model  180  includes an encoder neural network  190  and a decoder neural network  192 . The analysis model  180  is trained with a machine learning process to identify recommended changes to plasma generation parameters based on sensed plasma and contamination qualities, such as those sensed by the light sensors  162  and the charged particle detectors  160 . The analysis model  180  of  FIG. 6  is only one example of an analysis model. Many other kinds of analysis models and training processes can be utilized without departing from the present disclosure. The training process utilizes a training set. The training set includes historical plasma generation conditions data. Each set of historical plasma generation conditions data includes, for a particular EUV generation process, the parameters of the flattening laser pulse, the parameters of the plasmatizing laser pulse, and the parameters of the droplets. The training set includes, for each set of historical plasma generation conditions, historical contamination data that resulted from the historical plasma generation conditions. 
     Each previously performed EUV generation process took place with particular plasma generation conditions and resulted in particular contamination patterns. The plasma generation conditions for each plasma data value are formatted into a respective plasma generation conditions vector  194 . The plasma generation conditions vector  194  includes a plurality of data fields  166 . Each data field  196  corresponds to a particular process condition. 
     The example of  FIG. 6  illustrates a single plasma generation conditions vector  194  that will be passed to the encoder  190  of the analysis model  180  during the training process. In the example of  FIG. 6 , the plasma generation conditions vector  194  includes three data fields  166 . A first data field  196  corresponds to the prepulse laser settings. In practice, there may be multiple data fields  166  for the prepulse laser settings, one for each of pulse power, pulse duration, pulse timing, etc. A second data field  196  corresponds to plasmatizing laser pulse settings. In practice, there may be multiple data fields  166  for each of a plurality of settings include pulse power, pulse duration, pulse timing, and other factors. A third data field  196  corresponds to the droplet settings. In practice, there may be multiple data fields  166  for each of a plurality of droplet settings including droplet speed, droplet size, droplet temperatures, etc. Each plasma generation conditions vector  194  can include different types of plasma generation conditions without departing from the scope of the present disclosure. The particular plasma generation conditions illustrated in  FIG. 6  are given only by way of example. Each process condition is represented by a numerical value in the corresponding data field  196 . 
     The encoder  190  includes a plurality of neural layers  198   a - c . Each neural layer includes a plurality of nodes  200 . Each node  200  can also be called a neuron. Each node  200  from the first neural layer  198   a  receives the data values for each data field from the plasma generation conditions vector  194 . Accordingly, in the example of  FIG. 6 , each node  200  from the first neural layer  198   a  receives three data values because the plasma generation conditions vector  194  has three data fields, though as mentioned above, in practice, the plasma generation conditions vector  194  may include many more data fields than 3. Each neuron  200  includes a respective internal mathematical function labeled F(x) in  FIG. 6 . Each node  200  of the first neural layer  198   a  generates a scalar value by applying the internal mathematical function F(x) to the data values from the data fields  166  of the plasma generation conditions vector  194 . Further details regarding the internal mathematical functions F(x) are provided below. 
     In the example of  FIG. 6 , each neural layer  198   a - 168   e  in both the encoder  190  and the decoder  192  are fully connected layers. This means that each neural layer has the same number of nodes as the succeeding neural layer. In the example of  FIG. 6 , each neural layer  198   a - 168   e  includes five nodes. However, the neural layers of the encoder  190  and the decoder  192  can include different numbers of layers than are shown in  FIG. 6  without departing from the scope of the present disclosure. 
     Each node  200  of the second neural layer  198   b  receives the scalar values generated by each node  200  of the first neural layer  198   a . Accordingly, in the example of  FIG. 6  each node of the second neural layer  198   b  receives five scalar values because there are five nodes  200  in the first neural layer  198   a . Each node  200  of the second neural layer  198   b  generates a scalar value by applying the respective internal mathematical function F(x) to the scalar values from the first neural layer  198   a.    
     There may be one or more additional neural layers between the neural layer  198   b  and the neural layer  198   c . The final neural layer  198   c  of the encoder  190  receives the five scalar values from the five nodes of the previous neural layer (not shown). The output of the final neural layer is the predicted contamination data  202 . In practice, the predicted contamination data may be a vector including many data fields. Each data field corresponds to a particular aspect of a sensed contamination map of the collector. The contamination map can indicate the contamination level at each of a plurality of surface zones of the collector. The contamination data vector includes data fields that indicate the contamination level at the various surface zones. 
     During the machine learning process, the analysis model compares the predicted contamination data  202  to the actual contamination data. The analysis model  180  generates an error value indicating the error or difference between the predicted contamination data  202  The error value is utilized to train the encoder  190 . 
     The training of the encoder  190  can be more fully understood by discussing the internal mathematical functions F(x). While all of the nodes  200  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  200  in the previous neural layer, or, in the case of the first neural layer  198   a , each value x 1 -x n  corresponds to a respective data value from the data fields  166  of the plasma generation conditions vector  194 . 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  180  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  200  is based on the weighting values w 1 -w n . Accordingly, each node  200  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. Each function F(x) may also include a transfer function.
 
     After the error value has been calculated, the analysis model  180  adjusts the weighting values w 1 -w n  for the various nodes  200  of the various neural layers  198   a - 168   c . After the analysis model  180  adjusts the weighting values w 1 -w n , the analysis model  180  again provides the plasma generation conditions vector  194  to the input neural layer  198   a . Because the weighting values are different for the various nodes  200  of the analysis model  180 , the predicted contamination data  202  will be different than in the previous iteration. The analysis model  180  again generates an error value by comparing the actual contamination data to the predicted contamination data  202 . 
     The analysis model  180  again adjusts the weighting values w 1 -w n  associated with the various nodes  200 . The analysis model  180  again processes the plasma generation conditions vector  194  and generates a predicted contamination data  202  and associated error value. The training process includes adjusting the weighting values w 1 -w n  in iterations until the error value is minimized. 
       FIG. 6  illustrates a single plasma generation conditions vector  194  being passed to the encoder  190 . In practice, the training process includes passing a large number of plasma generation conditions vectors  194  through the analysis model  180 , generating a predicted contamination data  202  for each plasma generation conditions vector  194 , and generating an associated error value for each predicted contamination data. The training process can also include generating an aggregated error value indicating the average error for all the predicted contamination data for a batch of plasma generation conditions vectors  194 . The analysis model  180  adjusts the weighting values w 1 -w n  after processing each batch of plasma generation conditions vectors  194 . The training process continues until the average error across all plasma generation conditions vectors  194  is less than a selected threshold tolerance. When the average error is less than the selected threshold tolerance, the training of the encoder  190  is complete and the analysis model is trained to accurately predict the contamination data based on the plasma generation conditions. 
     The decoder  192  operates and is trained in a similar manner as the encoder  190  as described above. During the training process of the decoder  192 , the decoder receives contamination data associated with a plasma generation conditions vector  194 . The contamination data is received by each node  200  of the first neural layer  198   d  of the decoder  192 . The nodes  200  and the first neural layer  198   d  apply their respective functions F(x) to the contamination data values and pass the resulting scalar values to the nodes  200  of the next neural layer  198   e . After the final neural layer  198   f  processes the scalar values received from the previous neural layer (not shown), the final neural layer  198   f  outputs a predicted plasma generation conditions vector  204 . The predicted plasma generation conditions vector  204  has the same form as the plasma generation conditions vector  194 . The data fields  205  of the predicted plasma generation conditions vector  204  represent the same parameters or conditions as the data fields  196  of the plasma generation conditions vector  194 . 
     The training process compares the predicted plasma generation conditions vector  204  to the plasma generation conditions vector  194  and determines an error value. The weighting parameters of the functions F(x) of the nodes  200  of the decoder  192  are adjusted and the contamination data is again provided to the decoder  192 . The decoder  192  again generates a predicted plasma generation conditions vector  204  and an error value is determined. This process is repeated for all of the plasma generation conditions vectors in the historical plasma generation conditions data and for all of the historical contamination data from the historical plasma data until the decoder  192  can generate, for each historical plasma data value, a predicted plasma generation conditions vector  172  that matches the corresponding plasma generation conditions vector  194 . The training process is complete when a prediction cumulative error value is lower than the threshold error value. 
     After the encoder  190  and the decoder  192  have been trained as described above, the analysis model  180  is ready to generate recommended plasma generation parameters to reduce contamination, and hence, the resulting EUV quality produced by the EUV photolithography systems described in relation to  FIGS. 1-5 . During operation, the analysis model receives a current plasma generation conditions vector representing current conditions or parameters of the EUV photolithography systems described in relation to  FIGS. 1-5 . The encoder  190  processes the current plasma generation conditions vector and generates predicted future contamination data based on the current plasma generation conditions vector. If the predicted future contamination data is less than desired, then the decoder  192  is utilized to generate a set of recommended plasma generation conditions that will result in reduced contamination. In particular, the decoder  192  receives contamination data reflecting reduced contamination. The decoder  192  then generates a predicted plasma generation conditions vector based on the reduced contamination values. 
     The predicted plasma generation conditions vector includes recommended plasma generation conditions values for certain of the plasma generation conditions types. For example, the predicted plasma generation conditions vector can include a recommended values for the various prepulse laser conditions, the plasmatizing laser pulse conditions, and the droplet conditions. These conditions can include laser prepulse and plasma pulse energy, laser position, laser stability, beam size, pulse duration, laser wavelength, laser pulse timing, droplet position, droplet stability, droplet timing, plasma ion density, plasma electron density, plasma temperature, plasma pancake angle, and plasma position. 
     Many other kinds of analysis models, training processes, and data forms can be utilized without departing from the scope of the present disclosure. 
     In another example, the contamination data  202  includes plasma quality data. In other words, the output of the encoder includes contamination data and plasma quality data. In this case, the training process includes not only historical contamination data, but also historical plasma quality data. The historical plasma quality data corresponds to characteristics of the plasmatized droplets that resulted from particular historical plasma generation conditions. The historical plasma quality can determined based on the output of the light sensors  162  and the charged particle detectors  160  as described previously. The training process trains the encoder  190  to predict both contamination data and plasma quality data based on the plasma generation conditions vectors  194 . The decoder  192 , in turn, is trained to predict plasma generation conditions that result in the plasma quality data and the contamination data. This results in the decoder  192  being able to generate recommended plasma generation conditions that will result in both improved contamination data and improved/maintained plasma quality. 
     In another example, the plasma generation conditions vectors  194  can include buffer fluid flow parameters. The buffer fluid flow parameters can indicate the flow rate of buffer fluid through various channels, as well as the position of director vanes. In this case, the analysis model  180  can be trained to suggest buffer fluid flow parameters that will result in reduced contamination using the same training principles described above but including buffer fluid flow data in the plasma conditions vectors  194 .  FIG. 7  is a flow diagram of a method  700  for operating a photolithography system, according to one embodiment. The method  700  can utilize the systems, components, and processes described in relation to  FIGS. 1-6 . At  702 , the method  700  includes detecting a drop in reflectivity of a collector mirror. One example of a collector mirror is the collector mirror  102  of  FIG. 1A . At  704 , the method  700  includes analyzing a contamination image of the reflector mirror. At  706 , the method  700  includes adjusting plasma generation conditions. At  708 , the method  700  includes calculating buffer fluid flow conditions for reducing contamination. At  710 , the method  700  incudes adjusting buffer fluid flow parameters. 
       FIG. 8  is a method  800  for operating a photolithography system, according to one embodiment. The method  800  can utilize the systems, components, and processes described in relation to  FIGS. 1-7 . At  802 , the method  800  includes monitoring a contamination image of a collector mirror. One example of a collector mirror is the collector mirror  102  of  FIG. 1A . At  804 , the method  800  includes providing the contamination image to an analysis model. One example of an analysis model is the analysis model  180  of  FIG. 5 . At  806 , the method  800  includes predicting a contamination image at a future time period with the analysis model based on current EUV generation parameters. At  808 , the method  800  includes generating, with the analysis model, new EUV generation parameters for reducing contamination. At  810 , the method  800  includes implementing the new EUV generation parameters. 
       FIG. 9  is a method  900  for reducing contamination in an EUV photolithography system, according to an embodiment. The method  900  can utilize the systems, components, and processes described in relation to  FIGS. 1-8 . At  902 , the method  900  includes performing a photolithography process by generating extreme ultraviolet radiation in an extreme ultraviolet radiation generation chamber. One example of an extreme ultraviolet radiation generation chamber is the extreme ultraviolet generation chamber  101  of  FIG. 1A . At  904 , the method  900  includes flowing a buffer fluid into the extreme ultraviolet radiation generation chamber. At  906 , the method  900  includes generating sensor signals indicative of contamination of the collector mirror in the extreme ultraviolet radiation generation chamber. One example of a collector mirror is the collector mirror  102  of  FIG. 1A . At  908 , the method  900  includes adjusting a flow of the buffer fluid into the extreme ultraviolet radiation generation chamber based on analysis of the sensor signals by the analysis model trained with a machine learning process. One example of an analysis model is the analysis model  180  of  FIG. 5 . 
       FIG. 10  is a method  1000  for reducing contamination in an EUV photolithography system, according to one embodiment. The method  1000  can utilize the systems, components, and processes described in relation to  FIGS. 1-9 . At  1002 , the method  1000  includes outputting a stream of droplets from a droplet generator. One example of droplets are the droplets  140  of  FIG. 1B . One example of a droplet generator is the droplet generator  114  of  FIG. 1B . At  1004 , the method  1000  includes generating, in an extreme ultraviolet radiation generation chamber, a plasma by irradiating the droplets with a laser. One example of an extreme ultraviolet radiation generation chamber is the extreme ultraviolet radiation generation chamber  101  of  FIG. 1B . One example of a laser is the laser  106  of  FIG. 1B . At  1006 , the method  1000  includes reflecting the extreme ultraviolet radiation with a collector mirror. One example of the collector mirror is the collector mirror  102  of  FIG. 1B . At  1008 , the method  1000  includes receiving a buffer fluid into a fluid distributor via a plurality of mass flow controllers each coupled to a respective inlet of the fluid distributor. One example of a fluid distributor is the fluid distributor  108  of  FIG. 1B . One example of mass flow controllers are the mass flow controllers  1020  of  FIG. 1B . One example of an inlet is the inlets  110  of  FIG. 1B . At  1010 , the method  1000  includes flowing the buffer fluid into the extreme ultraviolet radiation generation chamber from the fluid distributor. 
     In one embodiment, a photolithography system includes a shield including an interior surface and a collector mirror coupled to the shield and including a reflective surface. The collector mirror and the shield define an extreme ultraviolet radiation generation chamber. The system includes a fluid source configured to hold a buffer fluid, a plurality of mass flow controllers each configured to receive the buffer fluid from the fluid source, and a fluid distributor. The fluid distributor includes a plurality of fluid inlets each coupled to a respective one of the mass flow controllers and configured to receive the buffer fluid from the mass flow controller and one or more outlets configured to supply the buffer fluid into the extreme ultraviolet radiation generation chamber. 
     In one embodiment, a method includes performing a photolithography process by generating extreme ultraviolet radiation in an extreme ultraviolet radiation generation chamber and flowing a buffer fluid into the extreme ultraviolet radiation generation chamber. The method includes generating sensor signals indicative of contamination of a collector mirror in the extreme ultraviolet radiation generation chamber. The method includes adjusting flow of the buffer fluid into the extreme ultraviolet radiation generation chamber based on analysis of the sensor signals by an analysis model trained with a machine learning process. 
     In one embodiment, a method includes outputting a stream of droplets from a droplet generator and generating, in an extreme ultraviolet radiation generation chamber, a plasma by irradiating the droplets with a laser. The method includes reflecting the extreme ultraviolet radiation with a collector mirror, receiving a buffer fluid into a fluid distributor via a plurality of mass flow controllers each coupled to a respective inlet of the fluid distributor, and flowing the buffer fluid into the extreme ultraviolet radiation generation chamber from the fluid distributor. 
     Embodiments of the present disclosure provide many benefits to extreme ultraviolet radiation photolithography systems. Embodiments of the present disclosure reduce contamination of components of the extreme ultraviolet photolithography systems. Embodiments of the present disclosure dynamically adjust the flow of a buffer fluid into an extreme ultraviolet radiation generation chamber. The buffer fluid helps to prevent and remove contamination of sensitive components of the extreme ultraviolet radiation generation chamber. This helps to ensure that photolithography processes have adequate extreme ultraviolet radiation. Furthermore, because contamination is removed and prevented, sensitive components of the extreme ultraviolet radiation systems do not have to be replaced as frequently. The sensitive components can be extremely expensive. Accordingly embodiments of the present disclosure not only enhance the effectiveness of photolithography processes, but they also reduce the cost of operating the photolithography system because components need to be replaced less frequently. 
     The various embodiments described above can be combined to provide further embodiments. 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.