Patent Publication Number: US-2022229371-A1

Title: System and method for monitoring and controlling 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. 1  is a block diagram of a photolithography system, according to one embodiment. 
         FIGS. 2A-2C  are illustrations of a photolithography system, according to one embodiment. 
         FIG. 3  is a top view of a portion of a photolithography system, according to one embodiment. 
         FIG. 4  is a side view of a portion of a photolithography system, according to one embodiment. 
         FIG. 5  is a top view of a portion of a photolithography system, according to one embodiment. 
         FIG. 6  is a flow diagram of a method for operating a photolithography system, according to one embodiment. 
         FIG. 7  is a block diagram of an analysis model, 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. 
     
    
    
     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 dynamically adjust plasma generation properties based on various sensors and machine learning processes. Embodiments of the present disclosure also deflect charged particles from damaging sensitive components of the photolithography system. Accordingly, embodiments of the present disclosure reduce damage to expensive photolithography components including photolithography masks, optical systems, and semiconductor wafers. Additionally, embodiments of the present disclosure improve the efficiency of generation of extreme ultraviolet light by dynamically adjusting parameters of the photolithography system responsive to the sensor signals. 
       FIG. 1  is a block diagram of a photolithography system  100  in accordance with one embodiment. The photolithography system  100  includes a plasma generation chamber  101  and a scanner  103 . Extreme ultraviolet light is generated in the plasma generation chamber  101 . The extreme ultraviolet light is passed from the plasma generation chamber  101  to the scanner  103 . The extreme ultraviolet light irradiates a photolithography target  104  in the scanner  103  via a mask. 
     In one embodiment, the photolithography system  100  is a laser produced plasma (LPP) extreme ultraviolet radiation photolithography system. The photolithography system  100  includes a laser  102 , a collector  106 , a droplet generator  108 , and a droplet receiver  110 . The laser  102 , the collector  106 , and the droplet generator  108  cooperate to generate extreme ultraviolet radiation within the plasma generation chamber  101 . 
     The droplet generator  108  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  110 . The photolithography system  100  utilizes the droplets to degenerate extreme ultraviolet light for photolithography processes. Extreme ultraviolet light typically corresponds to light with wavelengths between 1 nm and 125 nm. 
     The laser  102  outputs a laser beam. The laser beam is focused on a point through which the droplets pass on their way from the droplet generator  108  to the droplet receiver  110 . In particular, the laser  102  outputs laser pulses. Each laser pulse is timed to irradiate a droplet. When the droplet receives the laser pulse, the energy from the laser pulse generates a high-energy plasma from the droplet. The high-energy plasma outputs extreme ultraviolet radiation. 
     In an embodiment in which the droplets are tin droplets, the extreme ultraviolet radiation has a central wavelength of about 13.5 nm. This is because tin atoms in the plasma state release electromagnetic radiation with a characteristic wavelength of about 13.5 nm. Extreme ultraviolet radiation having wavelengths other than 13.5 nm can be utilized without departing from the scope of the present disclosure. 
     In one embodiment, the radiation output by the plasma scatters randomly in many directions. The photolithography system  100  utilizes the collector  106  to collect the scattered extreme ultraviolet radiation from the plasma droplets and reflects the extreme ultraviolet radiation toward the scanner  103 . The scanner  103  directs the extreme ultraviolet radiation toward the photolithography target  104 . 
     In one embodiment, the collector  106  includes an aperture. The laser pulses from the laser  102  pass through the aperture toward the stream of droplets. This enables the collector  106  to be positioned between the laser  102  and the photolithography target  104 . 
     After the droplets have been irradiated by the laser  102 , the droplets continue with a trajectory toward the droplet receiver  110 . The droplet receiver  110  receives the droplets in a droplet pool. The droplets can be drained from the droplet pool and reused or disposed of. 
     Extreme ultraviolet radiation photolithography systems face many challenges. For example, after the droplets have been irradiated with the laser  102  and changed into a plasma, many charged particles scatter about the plasma generation chamber  101 . The charged particles can include ions and free electrons. This is because when the droplets are converted to a plasma, the atoms in the droplets become ionized and many free electrons are generated. Accordingly, the plasmatized droplets include a sort of fluid (a plasma) of charged particles including ions and free electrons. 
     Some of the charged particles released from the plasma may travel toward the scanner  103 . The charged particles can damage components in the scanner  103 . The scanner  103  can include highly sensitive precision optics such as lenses and mirrors. The scanner  103  also includes the photolithography mask defining the pattern to be imprinted on the photolithography target  104 . Typically, the photolithography target  104  is a semiconductor wafer. The charged particles can damage any of these components. Damage to the mask or any of the other optics can result in nonfunctioning semiconductor wafers that must be scrapped, at great expense. Additionally, if the mask is damaged by charged particles, it can cost millions of dollars to repair or replace the mask. Accordingly, it is desirable to ensure that charged particles from the plasma do not damage components within the scanner  103 . As used herein, the term “charged particles” includes, but is not limited to, electrons, protons, and ions. 
     Another challenge faced by extreme ultraviolet photolithography systems is that it can be extremely difficult to fine-tune the parameters of plasma generation in order to generate sufficient extreme ultraviolet radiation. Parameters to fine-tune can include droplet speed, droplet size, laser pulse timing, laser pulse power, droplet preconditioning, and other parameters that contribute to the generation of extreme ultraviolet radiation. It can be very difficult to determine whether plasma generation is currently at a satisfactory level of effectiveness and efficiency. If plasma generation is not at a currently satisfactory level of effectiveness and efficiency, it can be very difficult to determine what parameters to adjust. 
     In one embodiment, the extreme ultraviolet light photolithography system  100  includes a control system  114  and one or more of a side scatter detection system  116 , a charged particle detection system  118 , and a charged particle deflection system  119 . The side scatter detection system  116  and the charged particle detection system  118  assist in monitoring the current effectiveness of the plasma generation process. The charged particle detection system  118  detects parameters of charged particles emitted from the plasma. The charged particle deflection system  119  helps to protect sensitive components of the scanner  103 . The control system  114  adjusts parameters of the plasma generation process responsive to the side scatter detection system  116  and the charged particle detection system  118 . 
     In one embodiment, the side scatter detection system  116  detects a current level of intensity of extreme ultraviolet light being generated in the plasma generation chamber  101 . In particular, the side scatter detection system  116  detects extreme ultraviolet light that is emitted with a substantially lateral trajectory. The side scatter detection system  116  can detect refracted light, reflected light, diffracted light, and scattered light. 
     The total intensity of extreme ultraviolet light emitted by plasma can be estimated or calculated based on the amount of light received by the side scatter detection system  116 . On average, the plasma will emit extreme ultraviolet light at the same rate in all directions, or with known relationships between various scattering directions. Accordingly, the total intensity of extreme ultraviolet light can be estimated or calculated based on the light received by the side scatter detection system  116 . 
     In one embodiment, the extreme ultraviolet side scatter detection system  116  provides sensor signals to the control system  114 . The sensor signals indicative of intensity of light on the light sensors. The control system  114  receives the sensor signals and can adjust parameters of the photolithography system  100  responsive to the sensor signals. 
     In one embodiment, the control system  114  adjusts parameters of the photolithography system  100  in order to more effectively generate extreme ultraviolet radiation. The control system  114  can adjust one or more of droplet speed, droplet size, laser pulse power, laser pulse timing, laser pulse profile, initial droplet temperature, pressure within the plasma generation chamber, or other parameters. 
     In one embodiment, the photolithography system  100  utilizes the charged particle detection system  118  to detect charged particles ejected from the plasma. As described previously, the process of generating the plasma results in the generation of charged particles in the droplets. 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 the 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. Accordingly, the charged particle detection system  118  detects the charged particles and generates sensor signals indicative of parameters of the charged particles. The charged particle detection system  118  passes the sensor signals to the control system  114 . 
     In one embodiment, the charged particle detection system  118  includes an array of charged particle detectors positioned within the plasma generation chamber  101 . The charged particle detectors can be positioned to detect a variety of charged particle trajectories within the plasma generation chamber  101 . In other words, the charged particle detectors can be positioned in various locations throughout the plasma generation chamber  101 . Each of the charged particle detectors detects impacts of charged particles against the charged particle detectors. The charged particle detectors pass sensor signals indicative of characteristics of the charged particles to the control system  114 . 
     In one embodiment, the control system  114  can adjust parameters of the photolithography system  100  responsive to the sensor signals from the charged particle detectors. The control system  114  can adjust the same sorts of parameters of the photolithography system  100  as those described previously in relation to the side scatter detection system  116 . The control system  114  can adjust the parameters of the photolithography system in order to more effectively generate extreme ultraviolet radiation for performing photolithography. 
     In one embodiment, the control system  114  adjusts parameters of the photolithography system  100  responsive to sensor signals from the side scatter detection system  116  and charged particle detection system  118 . 
     In one embodiment, the charged particle deflection system  119  is positioned within the scanner  103 . The charged particle deflection system is configured to protect sensitive equipment within the scanner  103  from being damaged by charged particles entering the scanner  103  from the plasma generation chamber  101 . In particular, some charged particles from the plasma may pass from the plasma generation chamber  101  through the intermediate focus aperture into the scanner  103 . If the charged particles impact the mask or other sensitive components within the scanner  103 , then the photolithography system or process may be heavily damaged. Accordingly, the charged particle deflection system  119  protects the sensitive components of the scanner  103  by deflecting charged particles away from the sensitive components of the scanner  103 . 
     In one embodiment, the charged particle deflection system  119  includes one or more deflectors that generate a magnetic field in a vicinity between the intermediate focus aperture  120  and sensitive equipment of the scanner  103 . As the charged particles travel through the magnetic field generated by the deflector, the trajectory of the charged particles is adjusted due to the forces that act upon charged particles traveling through magnetic field. The direction of the magnetic field is selected to cause charged particles that have an initial trajectory toward sensitive components within the scanner  103  to divert to a harmless trajectory. The charged particles can then be collected or captured, thereby preventing damage to sensitive components within the scanner  103 . Alternatively, the charged particle deflection system  119  can utilize electric fields, or a combination of electric and magnetic fields to deflect charged particles. 
     In one embodiment, the photolithography system  100  can collect plasma information and scattering light by introducing monitoring systems and control systems. By recording plasma detail information, the photolithography system can also rebuild a 3D image through machine learning or artificial intelligence system calculation, which gives the ability to provide advanced control in light energy management and provide more plasma information for analysis. The photolithography system  100  help to research issues like ionization rate, conversion efficiency, dynamic time-resolved plasma density distribution, tin debris mitigation, tin to scanner mitigation, collector lifetime control, and can also provide a possible way to diagnose a tin to scanner orifice mechanism. 
       FIGS. 2A-2C  are illustrations of a photolithography system  200 , according to an embodiment. The photolithography system  200  is an extreme ultraviolet photolithography system that generates extreme ultraviolet radiation by laser plasma interaction. The plasma can be generated in a substantially similar manner as described in relation to  FIG. 1 .  FIG. 2A  illustrates the photolithography system prior to generating plasma and extreme ultraviolet radiation.  FIGS. 2B and 2C  illustrate the photolithography system  200  during generation of plasma in the extreme ultraviolet radiation.  FIG. 2B  illustrates extreme ultraviolet radiation emitted from plasmatized droplets.  FIG. 2C  illustrates charged particles emitted from the plasmatized droplets. In practice, the extreme ultraviolet radiation and the charged particles are present simultaneously. However, for illustrative purposes, the extreme ultraviolet radiation is shown only in  FIG. 2B , while the charged particles are shown only in  FIG. 2C . 
     With reference to  FIG. 2A , the photolithography system  200  includes a plasma generation chamber  101 , laser  102 , a scanner  103 , a collector  106 , a droplet generator  108 , and a droplet receiver  110 . These components of the photolithography system  200  cooperate together to generate extreme ultraviolet radiation and to perform photolithography processes with the extreme ultraviolet radiation. 
     The droplet generator  108  generates and outputs a stream of droplets  142 . The droplets can include, as described previously, tin, the droplets of other material can be utilized without departing from the scope of the present disclosure. The droplets  142  move at a high rate of speed toward the droplet receiver  110 . 
     In one embodiment, the droplet generator  108  generates between 40,000 and 100,000 droplets per second. The droplets  142  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  108  can generate different numbers of droplets per second than described above without departing from the scope of the present disclosure. The droplet generator  108  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  102  is positioned behind the collector  106 . During operation, the laser  102  outputs pulses of laser light  144  (see  FIG. 2B ). The pulses of laser light  144  are focused on a point through which the droplets pass on their way from the droplet generator  108  to the droplet receiver  110 . Each pulse of laser light  144  is received by a droplet  142 . When the droplet  142  receives the pulse of laser light  144 , the energy from the laser pulse generates a high-energy plasma from the droplet  142 . The high-energy plasma outputs extreme ultraviolet radiation. 
     In one embodiment, the laser  102  is a carbon dioxide (CO 2 ) laser. The CO 2  laser emits radiation or laser light  144  with a wavelength centered around 9.4 μm or 10.6 μm. The laser  102  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  102  irradiates each droplet  142  with two pulses. A first pulse causes the droplet  142  to flatten into a disk like shape. The second pulse causes the droplet  142  to form a high temperature plasma. The second pulse is significantly more powerful than the first pulse. The laser  102  and the droplet generator  108  are calibrated so that the laser  102  emits pairs of pulses such that each droplet  142  is irradiated with a pair of pulses. For example, if the droplet generator  108  outputs 50,000 droplets per second, the laser  102  will output 50,000 pairs of pulses per second. The laser  102  can irradiate droplets  142  in a manner other than described above without departing from the scope of the present disclosure. For example, the laser  102  may irradiate each droplet  142  with a single pulse or with more pulses than two. Moreover, the primary laser here can not only cause droplet into disk-like shape but also can be mist or vapor state. 
     In one embodiment, the droplets  142  are tin. When the tin droplets  142  are converted to a plasma, the tin droplets  142  output extreme ultraviolet radiation  146  with a wavelength centered between 10 nm and 15 nm. More particularly, in one embodiment, the tin plasma emits extreme ultraviolet radiation with a central wavelength of 13.5 nm. These wavelengths correspond to extreme ultraviolet radiation. Materials other than tin can be used for the droplets  142  without departing from the scope of the present disclosure. Such other materials may generate extreme ultraviolet 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  106  to collect the scattered extreme ultraviolet radiation  146  from the plasma and output the extreme ultraviolet radiation toward a photolithography target  104 . 
     In one embodiment, the collector  106  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 extreme ultraviolet radiation to the photolithography mask. The extreme ultraviolet radiation  146  reflects off of the mask onto a photolithography target. The extreme ultraviolet 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 in the various configurations of optical equipment in the scanner  103  are not shown. 
     In one embodiment, the collector  106  includes a central aperture  125 . The pulses of laser light  144  pass from the laser  102  through the central aperture  125  toward the stream of droplets  142 . This enables the collector  106  to be positioned between the laser  102  and the photolithography target  104 . 
     In one embodiment, the photolithography system  200  includes a plurality of light sensors  126 . The light sensors  126  are positioned to detect side scattering of extreme ultraviolet radiation from the plasmatized droplets  142 . The light sensors  126  can be part of a side scatter detection system  116  as described in relation to  FIG. 1 . The light sensor  126  here can also combine such as grating to extend the application or function into spectroscopy. 
     In one embodiment, the light sensors  126  collectively detect a current level of intensity of extreme ultraviolet light being generated in the plasma generation chamber  101 . In particular, the light sensors  126  detect light from extreme ultraviolet radiation that is emitted with a substantially lateral trajectory. 
     In one embodiment, the light sensors  126  are utilized to detect Thomson scattering of extreme ultraviolet 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  126  generate signals indicative of the intensity of side scattered light and passes the signals to the control system  114 . 
     The total intensity of extreme ultraviolet light emitted by plasma can be estimated or calculated based on the amount of light received by the light sensors  126 . On average, the plasma will emit extreme ultraviolet light at the same rate in all directions, or with known relationships between various scattering directions. Accordingly, the total intensity of extreme ultraviolet light can be estimated or calculated based on the light received by the light sensors  126 . 
     In one embodiment, the light sensors  126  can be positioned substantially in a same lateral plane as the droplet generator  108  and the droplet receiver  110 . Though  FIGS. 2A-2C  illustrate the light sensors  126  as being positioned slightly above the droplet generator  108  and the droplet receiver  110 , in practice, the plurality of light sensors  126  may be positioned in a same XY plane as the droplet generator  108  and the droplet receiver  110 . Alternatively, the light sensors  126  may be positioned in an XY plane above droplet generator  108  and the droplet receiver  110 . Alternatively, the light sensors  126  may be positioned at varying heights equal to or above the droplet generator  108  and the droplet receiver  110 . 
     In one embodiment, the photolithography system  200  includes a plurality of lenses  128 . Each lens  128  is positioned to focus light scattered from the plasmatized droplets  142  onto or into the light sensors  126 . Though the lenses  128  are shown as being positioned external to the plasma generation chamber  101 , in practice, the lenses  128  may be positioned in other locations or orientations than those shown in  FIGS. 2A-2C . 
     In one embodiment, the one or more lenses  128  are coupled to a rim of the collector  106 . The lenses can be positioned in a same lateral plane as the droplet generator  108  and droplet receiver  110 . Side scattered light from the plasma droplets passes through the lenses and is focused onto the light sensors  126 . 
     In one embodiment, the one or more lenses  128  can correspond to windows in the wall of the plasma generation chamber  101 . Accordingly, a wall of the plasma generation chamber  101  can include windows or apertures. Lenses  128 , 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  128  positioned in the windows or apertures can focus the light onto the light sensors  126 . 
     In one embodiment, the light sensors  126  provide sensor signals to the control system  114 . The sensor signals are indicative of the intensity of light received by the light sensors  126 . The control system  114  receives the sensor signals and can adjust parameters of the photolithography system  200  responsive to the sensor signals. 
     In one embodiment, the control system  114  adjusts parameters of the photolithography system  200  in order to more effectively generate extreme ultraviolet radiation. The control system  114  can adjust one or more of droplet speed, droplet size, laser pulse power, laser pulse timing, laser pulse train profile, initial droplet temperature, pressure within the plasma generation chamber, or other parameters. 
     In one embodiment, adjusting aspects of the laser pulses can include adjusting the flattening pulse that initially flattens the droplets  142 . As described previously, before generating a plasma from a droplet, the laser  102  irradiates the droplet  142  with a flattening pulse that flattens the droplet. The flattening pulse flattens the droplet  142  substantially into the shape of a thin disk. The overall shape of the disc or pancake determines, in part, how effectively the plasma can be generated from the droplet  142  by the subsequent plasma generation pulse. Accordingly, the parameters of the flattening pulse determine, in part, how effectively the plasma can be generated from the droplets. This in turn affects how efficiently extreme ultraviolet radiation can be generated from the droplets. The control system  114  can adjust aspects of the flattening laser pulses responsive to the sensor signals. 
     In one embodiment, adjusting aspects of the laser pulses can include adjusting the plasma generation pulse that generates the plasma from the flattened droplet  142 . 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  114  responsive to the sensor signals from the light sensors  126 . 
     In one embodiment, the photolithography system includes charged particle detectors  130 . The charged particle detectors  130  can be part of a charged particle detection system, such as the charged particle detection system  118  of  FIG. 1 . The charged particle detectors  130  are configured to detect charged particles ejected from the plasma. 
     As described previously, the process of generating a plasma results in the generation of charged particles in the droplets. 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. 
       FIG. 2C  illustrates charged particles  148  scattering from a plasmatized droplet  142 . For clarity,  FIG. 2C  does not illustrate the extreme ultraviolet radiation  146  that is also emitted from the plasmatized droplet  142 . The charged particle detectors  130  are configured to detect the charged particles  148  emitted from the plasmatized droplet  142 . 
     In one embodiment, the charged particle detectors  130  are coupled to the control system  114 . The charged particle detectors  130  are configured to generate sensor signals indicative of parameters of the charged particles. The charged particle detectors  130  pass the sensor signals to the control system  114 . 
     In one embodiment, an array of charged particle detectors  130  is positioned within the plasma generation chamber  101 . The array of charged particle detectors  130  can be positioned to detect a variety of charged particle trajectories within the plasma generation chamber  101 . In other words, the charged particle detectors  130  can be positioned in various locations throughout the plasma generation chamber  101 . Each of the charged particle detectors  130  detects impacts of charged particles on the charged particle detectors  130 . The charged particle detectors  130  pass sensor signals indicative of characteristics of the charged particles to the control system  114 . 
       FIGS. 2A-2C  illustrate the charged particle detectors  130  as being positioned on an exterior wall of the plasma generation chamber  101 . However, the charged particle detectors  130  can be positioned within the plasma generation chamber  101 . For example, the charged particle detectors  130  can be positioned on an interior wall of the plasma generation chamber  101 . Alternatively, the charged particle detectors  130  can be positioned, supported, or arranged in other ways in an interior or an exterior of the plasma generation chamber  101 . In one embodiment, the plasma generation chamber  101  can include apertures that permit charged particles  148  to pass from an interior of the plasma generation chamber  101  to the charged particle detectors  130 . 
     In one embodiment, the charged particle detectors  130  include charge coupled devices configured to detect impacts from charged particles  148 . 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  114 . 
     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  148  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 receptacle that is configured to detect or capture charged particles  148  in a vacuum, such as a vacuum in the plasma generation chamber  101 . The Faraday cup generates a current based on the charged particles  148  captured by the Faraday cup. This current can be utilized to determine the number of charged particles  148  that impact the cup. The Faraday cups can provide sensor signals to the control system  114  indicative of the number of charged particles  148  collected or captured by the Faraday cups. 
     In one embodiment, the photolithography system  200  includes an electron capture box  139  and an ion capture box  140  coupled to or part of the scanner  103 . As described previously, some of the charged particles  148  are electrons and some of the charged particles  148  are ions. The electron capture box  139  and the ion capture box  140 , in conjunction with first and second deflectors  134   a  and  134   b , are respectively configured to capture electrons and ions. The function of the deflectors  134   a ,  134   b  will be described in more detail below. 
     The charged particle detector  136  is positioned in the electron capture box  139 . After charged particles  148  pass through the intermediate focus aperture  120  into the scanner  103 , the deflectors  134   a ,  134   b  deflect electrons into the electron capture box  139 . The deflectors  134   a ,  134   b  deflect ions into the ion capture box  140 . The charged particle detector  136  is configured to detect electrons that enter the scanner  103 . 
     In one embodiment, charged particle detector  136  is an electron multiplying charge coupled device. As described previously, the electron multiplying charge coupled device can be a frame transfer charge coupled device that includes 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 electron multiplying charge coupled device. When electrons impact the florescent film, the florescent film emits light. The light is sensed by the electron multiplying charge coupled device and the electron multiplying charge coupled device counts the impact of the charged particle. 
     The charged particle detector  136  can be coupled to the control system  114 . The charged particle detector  136  can be part of a charged particle detection system. For example, the charged particle detector  136  can be part of the charged particle detection system  118  of  FIG. 1 . 
     An electromagnetic lens  138  is positioned in the scanner  103 . The electromagnetic lens  138  is configured to focus electrons toward the charged particle detector  136 . The electromagnetic lens  138  utilizes electromagnetic forces to act as a lens for electrons that enter the scanner  103 . The electromagnetic lens  130  can help to assure that a high percentage of the electrons that enter the scanner  130  are detected at the charged particle detector  136 . 
     Though a single charged particle detector  136  is shown in  FIGS. 2A-2C , in practice the photolithography system  200  can include multiple charged particle detectors  136 . In particular, the electron capture box  139  can include multiple charged particle detectors  136 . Though not shown in  FIGS. 2A-2C , the charged particle capture box  140  can also include one or more charged particle detectors configured to detect ions that enter the scanner  103 . 
     In one embodiment, the charged particle detector  136  positioned within the scanner  103  can act as a z-axis charged particle detector in an example in which the z-axis corresponds to an axis extending between the collector  106  and the intermediate focus aperture  120 . As will be described in more detail below, the charged particle detector  136  can act as a z-axis charged particle detector while the charged particle detectors  130  can act as detectors for other axes or angles. 
     In one embodiment, the collector  106  includes charged particle detectors. The charged particle detectors on the collector  106  can be utilized to assist in determining a z-axis distribution of charged particles from the plasmatized droplets  142 . The charged particle detectors can be positioned at various locations on the collector  106 . In one embodiment, the charged particle detectors can be positioned in or adjacent to the apertures in the collector  106 . 
     In one embodiment, the control system  114  can adjust parameters of the photolithography system  100  responsive to the sensor signals from the charged particle detectors  130  and/or  136 . The control system  114  can adjust the same sorts of parameters of the photolithography system  200  as those described previously in relation to the light sensors  126 . The control system  114  can adjust the parameters of the photolithography system  200  in order to more effectively generate extreme ultraviolet radiation for performing photolithography. 
     In one embodiment, the control system  114  can generate a 3D model of the droplets  142  after the flattening pulse and/or the plasma generation pulse. Because the charged particle detectors  130 ,  136  are positioned in various locations throughout the plasma 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  114  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. Accordingly, the control system  114  can adjust parameters of the photolithography system  100  responsive to sensor signals from the charged particle detectors. 
     In one embodiment, the control system  114  adjust parameters of the photolithography system  100  responsive to sensor signals from the light sensors  126  and the charged particle detectors  130 ,  136 . The control system  114  can generate a model of the flattened droplets  142 , the plasmatized droplets  142 , or of other aspects of the plasma or droplets  142  based on the combination of sensor signals from both the light sensors  126  and the charged particle sensors  130 ,  136 . 
     In one embodiment, the deflectors  134   a ,  134   b  are positioned within the scanner  103 . The deflectors  134   a ,  134   b  can be part of a charged particle deflection system. For example, the deflectors  134   a ,  134   b  can be part of the charged particle deflection system  119  of  FIG. 1 . The charged particle deflectors  134   a ,  134   b  are configured to protect sensitive equipment within the scanner  103  from being damaged by charged particles  148  entering the scanner  103  from the plasma generation chamber  101 . In particular, some charged particles  148  from the plasma may pass from the plasma generation chamber  101  through the intermediate focus aperture  120  into the scanner  103 . If the charged particles  148  impact the mask or other sensitive components within the scanner  103 , then the photolithography system or process may be heavily damaged. Accordingly, the deflectors  134   a ,  134   b  protect the sensitive components of the scanner  103  by deflecting charged particles  148  away from the sensitive components of the scanner  103 . In particular, the deflectors  134   a ,  134   b  are configured to deflect electrons into the electron capture box  139  and to deflect ions into the ion capture box  140 . 
     In one embodiment, the deflectors  134   a ,  134   b  generate a magnetic field in a vicinity between the intermediate focus aperture  120  and sensitive equipment of the scanner  103 . As the charged particles  148  travel through the magnetic field generated by the deflectors  134   a ,  134   b , the trajectory of the charged particles  148  is adjusted due to the forces that act upon charged particles  148  traveling through the magnetic field. The direction of the magnetic field is selected to cause charged particles that have an initial trajectory toward sensitive components within the scanner  103  to divert to a harmless trajectory. The charged particles  148  can then be collected or captured, thereby preventing damage to sensitive components within the scanner  103 . 
     In one embodiment, the first deflector  134   a  may generate a magnetic field sufficient to deflect electrons that have comparatively low masses. The electrons initially have a trajectory generally in the Z direction. As the electrons pass through the magnetic field generated by the deflector  134   a , the trajectories of the electrons are adjusted by the Lorentz force. The Lorentz force F acts on a charged particle with charge q and velocity v that passes through a magnetic field B in accordance with the following formula: 
     
       
      
       F=q*vXB,  
      
     
     where F, v, and B are vectors, and X represents the cross product operator. The magnetic field generated by the deflector  134   a  is configured to cause the negatively charged electrons to deflect into the electron capture box  139 . The electromagnetic lens  138  focuses the electrons toward the charged particle detector  136 . 
     In one embodiment, the first deflector  134   a  deflects positively charged ions toward the ion capture box  140 . The positive charge of the ions and the negative charge of the electrons result in their being diverted in different directions by the deflector  134   a.    
     In one embodiment, the photolithography system  200  utilizes a second deflector  134   b  to more effectively deflect positively charged ions into the ion capture box  140 . The positively charged ions typically are far more massive than the electrons. In an example in which the positively charged ions are tin ions, the mass of the positively charged ions is several orders of magnitude larger than the mass of the electrons. Accordingly, a single deflector  134   a  may not sufficiently deflect the ions away from sensitive components of the scanner  103  and into the ion capture box  140 . For this reason, the photolithography system  200  may include the second deflector  134   b  to further deflect the ions into the ion capture box  140 . The second deflector  134   b  may be substantially similar to the first deflector  134   a  in that the second deflector  134   b  generates a magnetic field. The second deflector may generate a magnetic field that is much stronger than the magnetic field of the first deflector  134   a . Other numbers and arrangements of deflectors may be utilized without departing from the scope of the present disclosure. 
     In one embodiment, the deflectors  134   a ,  134   b  can include electromagnets. The electromagnets can be positioned within the scanner  103  and can generate magnetic fields in accordance with well understood electromagnetic principles. The electromagnets can include one or more conductors that pass an electric current, thereby generating a magnetic field. Alternatively, the deflectors  134   a ,  134   b  can include other types of magnets or other types of components that generate magnetic fields without departing from the scope of the present disclosure. In some cases, the deflectors  134   a ,  134   b  may be positioned external to the scanner  103  but may still generate magnetic fields within the scanner  103  in order to deflect the charged particles  148 . 
     In one embodiment, the control system  114  can include one or more controllers or processors. The control system  114  can include one or more computer memories that can store instructions and data. The controllers or processors can execute the instructions and process the data. For example, the processors and instructions can be utilized to assist in adjusting or controlling parameters of the photolithography system  200  responsive to the sensor signals received from the light sensors  126  and/or the charged particle detectors  130 ,  136 . 
     In one embodiment, the control system  114  utilizes machine learning to accurately adjust the parameters of the photolithography system  200 . Accordingly, the control system  114  can include a machine learning model that can be trained to adjust one or more parameters of the laser pulses or droplets  142  responsive to sensor signals received from the light sensors  126  and/or the charged particle detectors  130 ,  136 . Details of a machine learning process are described in relation to  FIG. 7 . 
     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  114 . Further details of an analysis model are provided in relation to  FIG. 7 . 
     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. With optical imaging theory and optics specifications, the plasma deformation from aberrations can be corrected by the control system  114 . 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  114  can compose a 3D plasma model. 
       FIG. 3  is a top view of a portion of a photolithography system  300 , according to one embodiment. The photolithography system  300  is substantially similar to the photolithography system  200  described in relation to  FIGS. 2A-2C , except that a particular distribution of light sensors  126  and charged particle detectors  130  is shown in  FIG. 3 . The photolithography system includes a collector  106 . The collector  106  includes a central aperture  125  through which laser pulses can pass in order to flatten and plasmatize the droplets  142 . 
     The photolithography system  300  includes a plurality of light sensors  126  positioned radially around and above the collector  106 . The light sensors  126  can be substantially similar to the light sensors  126  described in relation to  FIGS. 2A-2C . The light sensors  126  can be configured to sense side scattered extreme ultraviolet light from plasmatized droplets  142 . The light sensors  126  can be configured to provide sensor signals to the control system  114 . The photolithography system  300  includes lenses  128  configured to direct light onto the light sensors  126 . The lenses  128  can be substantially similar to the lenses  128  described in relation to  FIGS. 2A-2C . 
     The photolithography system  300  includes a plurality of charged particle detectors  130  positioned radially around and above the collector  106 . The charged particle detectors  130  can be substantially similar to the charged particle detectors  130  described in relation to  FIGS. 2A-2C . The charged particle detectors  130  can be configured to provide sensor signals to the control system  114 . 
       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  200  of  FIGS. 2A-2C , except for the placement of the lenses  128 . The view of  FIG. 4  illustrates a portion of the collector  106  and the plasma generation chamber  101 . The photolithography system  400  differs from the photolithography system  200  in that a lens  128  coupled to a rim of the collector  106 . The wall of the plasma generation chamber  101  is coupled to the lens  128 . The lens  128  is configured to direct side scattered radiation from plasmatized droplets  142  onto the light sensor  126 . In practice, the photolithography system  400  can include a plurality of lenses  128  coupled to the rim of the collector  106  and arranged radially and each configured to direct side scattered radiation to a light sensor  126 . Lenses  128  and light sensors  126  can perform substantially similar functions to the lenses  128  and light sensors  126  described in relation to  FIGS. 2A-2C . 
       FIG. 5  is a top view of a photolithography system  500 , according to one embodiment. The photolithography system  500  is substantially similar to the photolithography system  200  of  FIGS. 2A-2C , except the collector  106  includes a grating structure  150  that assists in detecting ions and charge particles emitted from plasmatized droplets  142 . For simplicity,  FIG. 5  does not illustrate lenses  128 , light sensors  126 , charged particle detectors  130 , and other components illustrated in the photolithography system  200 , though such components may also be present in the photolithography system  500 . The collector  106  of  FIG. 5  is a grated collector. The grated collector includes a periodic grating structure. The periodic grating structure includes periodic bands  150  of material sensitive to ions of charged particles. The periodic bands  150  generate electrical signals upon contact with a charged particle. Each of the bands  150  is coupled to the control system  114 . The bands provide sensor signals indicative of charged particles  148  that contact the bands  150 . The control system  114  can analyze the sensor signals and adjust parameters of the photolithography system  500 , responsive to the sensor signals. 
     In one embodiment, the collector  106  surface has a grating structure. The grating structure is arranged as a coaxial band shape. The purpose is to filter out of band wavelengths. Charged particle detectors can be placed between grating bands and can be used to catch downward ions and electrons. Moreover, by arranging some charged particle detectors on the cone chamber environment, the resolved plasma information can be more detailed for improved analysis. The system error and detector positions to plasma source can be corrected by a machine learning or artificial intelligence system. This can be combined with the side scattering information and the deflection system. By known information of moving ions/electrons angle, position, velocity, flying timing and energy, the plasma distribution can be calculated and a 3D model can be built. Moreover, the moving ions and electrons could be deflected by a strong magnetic field to avoid damage to the mask and sensitive parts in the scanner. 
       FIG. 6  is a flow diagram of a method  600  for operating a photolithography system, according to one embodiment. The method  600  can utilize the structures and processes described in relation to  FIGS. 1-5  in order to generate plasma and collect information as described below. At  602 , the method  600  generates a plasma in a plasma generation chamber. The plasma can be generated by irradiating a droplet with a flattening laser pulse and then irradiating the droplets with a plasmatizing laser pulse that generates a plasma from the flattened droplet. The generation of plasma can utilize components and processes described previously in relation to  FIGS. 1-5 . 
     From  602 , the method  600  proceeds to steps  604 ,  610 , and  616 . At  604 , the method  600  senses side scattered light from the plasmatized droplets. In one example, the side scattered light can be sensed with light sensors  126  of  FIGS. 2A-4 . At  606 , the method  600  records the side scattered light information. In one example, the control system  114  of  FIGS. 2A-2C  records the side scattered light information sensor signals received from the light sensors  126 . 
     At  608 , the method  600  corrects or adjusts the side scattered light information. The side scattered light is received by the light sensors  126  after passing through lenses  128 . This means that the raw sensor data generated by the light sensors does not by itself represent the accurate distribution of side scattered light. Accordingly, before the side scattering data can be used to understand the state of the plasmatized droplets, the raw sensor data should be adjusted to account for the effect that the lenses  128  have on the side scattered light data. In other words, some mathematical transformations may be performed to adjust the raw side scattering data. In one example, the control system  114  of  FIGS. 2A-2C  can correct errors in the side scattered light data. 
     At  610 , the method  600  senses parameters of charged particles emitted or ejected from the plasma. In one example, the parameters of the charged particles can be sensed by charged particle sensors  130  of  FIGS. 2A-2C . The charged particle sensors  130  can sense velocity, intensity, energy, numbers, or other parameters of charged particles emitted from the plasma. At  612 , the method  600  records the charged particle information. In one example, the control system  114  of  FIGS. 2A-2C  can record charged particle information based on sensor signals received from the charged particle sensors  130 . 
     At  614 , the method  600  corrects or adjusts the charged particle information. The correction or adjustment is based on the same principles as described above in relation to the side scattered light at  608 . In particular, some calculations or transformations may need to be applied to the raw sensor data produced by the charged particle sensors  130  in order to correct for known distortions introduced by the charged particle sensors  130  or other components that may focus or direct the charged particles. In one example, the control system  114  of  FIGS. 2A-2C  can correct or adjust the charged particle data in preparation for analysis of the charged particle information. 
     At  616 , the method  600  senses deflected charged particles. In one example, the charged particle detector  136  of  FIGS. 2A-2C  senses charged particles deflected by one or both of the deflectors  134   a ,  134   b  within the scanner  103 . The charged particles sensed by the charged particle detector  136  can be indicative of a z-axis scattering of charged particles. At  618 , the method  600  records charged particle information. In one example, the control system  114  of  FIGS. 2A-2C  receives sensor signals from the charged particle detector  136  of  FIGS. 2A-2C  and records charged particle data based on the sensor signals. 
     At  620 , the method  600  corrects or adjusts the charged particle data. The correction or adjustment is based on the same principles as described above in relation to the side scattered light at  608 . The charged particles that are sensed by the charged particle detector  136  may first be reflected by the reflector  106  and may then be deflected by the deflector  134   a , and then focused by the electromagnetic lens  138 . Accordingly, the raw sensor data provided by the charged particle detector  136  will have distortions based on the effects of these components. Accordingly, before the sensor data from the charged particle detector  136  can be used, some calculations or transformations may need to be applied to the raw sensor data in order to correct for known distortions introduced by the components mentioned above. In one example, the control system  114  of  FIGS. 2A-2C  can correct or adjust the charged particle data in preparation for analysis of the charged particle information. 
     From  608 ,  614 , and  620 , the method  600  proceeds to  622 . At  622 , the method  600  calculates and combines the side scattered light information, the charged particle information, and the deflected charged particle information. In one example, the control system  114  of  FIGS. 2A-2C  calculates various data from the adjusted recorded data and then combines the various data. 
     At  624 , the method  600  compares energy-related data associated with the charged particles and/or the side scattered light. In one example, the control system  114  compares energy-related data associated with the charged particles and/or the side scattered light. The energy related data can include the energy of the sensed light and charged particles, as well as the intensity or count of light and charged particles. The amount of EUV radiation that can be generated is related to the energy of the plasma, which is in turn related to the intensity or count of light and charged particles. Accordingly, comparing the energy related data corresponds to determining the maximum amount of EUV light that could be generated with the current plasmatized droplets. 
     At  626 , the method  600  builds a 3D model of the plasma. In one example, the control system  114  of  FIGS. 2A-2C  builds the 3D model based on the charged particle data, the side scattered light data, the deflected charged particle data, and the comparison of energy-related data. In particular, the X-Y distribution of the plasma can be calculated based on the side-scattered light information. The Z distribution of the plasma can be calculated based on the charged particle data sensed by the charged particle sensor  136 . The 3D model may correspond to calculating the X-Y and Z distribution of the plasma, based on these parameters. 
     At  628 , the method  600  determines adjustments to be made to the plasma generation process. In one example, the control system  114  analyzes the 3D plasma model and determines adjustments to be made to the plasma generation process based on analysis of the 3D model. Examples of adjustments can include adjusting the timing, position, power, duration, or profile of the flattening laser pulse. Examples of adjustments can include adjusting the timing, position, power, duration, or profile of the plasma generation laser pulse. Examples of adjustments can include adjusting a velocity of droplets, the size of droplets, the material of droplets, the temperature of droplets, the trajectory of droplets, or the shape of droplets. 
     In one embodiment, determining the adjustments to be made can be performed by an analysis model trained with a machine learning process. The analysis model and the machine learning process are described in further detail in relation to  FIG. 7 . 
     At  630 , the method  600  adjusts the plasma generation parameters in accordance with the analysis of the 3D model. In one example, the control system  114  of  FIGS. 2A-2C  adjusts the plasma generation parameters. From  630 , the process returns to  602 , at which point the method  600  generates plasma with the adjusted plasma generation parameters. The method  600  can repeat itself throughout the extreme ultraviolet photolithography process in order to continuously update and adjust or correct plasma generation parameters in order to improve extreme ultraviolet photolithography processes. 
     In one embodiment, the method  600 , or other embodiments of a photolithography system or method can include post processing of sensor data. For example an electron distribution image can be generated with one or more electron multiplying charge coupled devices. The image can be corrected by computing the original position and the real distribution in the XY plane can be generated. The control system can correct system error deformation and can compute electron density on target. This, together with Thomson scattering data, can be used to generate a 3D plasma model. Machine learning or artificial intelligence systems of the control system can analyze the model, or the conditions represented by the model, and determine appropriate adjustments to be made to the plasma generation parameters. 
       FIG. 7  is a block diagram of an analysis model  152 , according to one embodiment. The analysis model  152  can be part of the control system  114  of  FIGS. 1-2A-2C  and can operate in conjunction with the systems and processes described in relation to  FIGS. 1-6 , according to one embodiment. The analysis model  152  can perform functions associated with block  628  of  FIG. 6 . The analysis model  152  includes an encoder neural network  160  and a decoder neural network  162 . The analysis model  152  is trained with a machine learning process to identify recommended changes to plasma generation parameters based on sensed plasma qualities, such as those sensed by the light sensors  126 , the charged particle detectors  130 , and the charged particle detector  138 . The analysis model of  FIG. 7  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 plasma 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 plasma qualities. The plasma generation conditions for each plasma data value are formatted into a respective plasma generation conditions vector  164 . The plasma generation conditions vector  164  includes a plurality of data fields  166 . Each data field  166  corresponds to a particular process condition. 
     The example of  FIG. 7  illustrates a single plasma generation conditions vector  164  that will be passed to the encoder  160  of the analysis model  152  during the training process. In the example of  FIG. 7 , the plasma generation conditions vector  164  includes three data fields  166 . A first data field  166  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  166  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  166  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  164  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. 7  are given only by way of example. Each process condition is represented by a numerical value in the corresponding data field  166 . 
     The encoder  160  includes a plurality of neural layers  168   a - c . Each neural layer includes a plurality of nodes  170 . Each node  170  can also be called a neuron. Each node  170  from the first neural layer  168   a  receives the data values for each data field from the plasma generation conditions vector  164 . Accordingly, in the example of  FIG. 7 , each node  170  from the first neural layer  168   a  receives three data values because the plasma generation conditions vector  164  has three data fields, though as mentioned above, in practice, the plasma generation conditions vector  164  may include many more data fields than 3. Each neuron  170  includes a respective internal mathematical function labeled F(x) in  FIG. 7 . Each node  170  of the first neural layer  168   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  164 . Further details regarding the internal mathematical functions F(x) are provided below. 
     In the example of  FIG. 7 , each neural layer  168   a - 168   e  in both the encoder  160  and the decoder  162  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. 7 , each neural layer  168   a - 168   e  includes five nodes. However, the neural layers of the encoder  160  and the decoder  162  can include different numbers of layers than are shown in  FIG. 7  without departing from the scope of the present disclosure. 
     Each node  170  of the second neural layer  168   b  receives the scalar values generated by each node  170  of the first neural layer  168   a . Accordingly, in the example of  FIG. 7  each node of the second neural layer  168   b  receives five scalar values because there are five nodes  170  in the first neural layer  168   a . Each node  170  of the second neural layer  168   b  generates a scalar value by applying the respective internal mathematical function F(x) to the scalar values from the first neural layer  168   a.    
     There may be one or more additional neural layers between the neural layer  168   b  and the neural layer  168   c . The final neural layer  168   c  of the encoder  160  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 plasma data. In practice, the predicted plasma data will be a vector including many data fields. Each data field corresponds to a particular aspect of the sensed plasma qualities such as X-Y plasma distribution data, Z plasma distribution data, and other parameters generated from the sensor data provided by the light sensors  126 , the charged particle sensors  130 , and the charged particle detector  136 . 
     During the machine learning process, the analysis model compares the predicted plasma data  172  to the actual plasma data. The analysis model  152  generates an error value indicating the error or difference between the predicted plasma data from the data value  172  (in practice a vector having many data values representing values associated with a 3D plasma model) and the actual plasma data. The error value is utilized to train the encoder  160 . 
     The training of the encoder  160  can be more fully understood by discussing the internal mathematical functions F(x). While all of the nodes  170  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  170  in the previous neural layer, or, in the case of the first neural layer  168   a , each value x 1 -x n  corresponds to a respective data value from the data fields  166  of the plasma generation conditions vector  164 . 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  152  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  170  is based on the weighting values w 1 -w n . Accordingly, each node  170  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  152  adjusts the weighting values w 1 -w n  for the various nodes  170  of the various neural layers  168   a - 168   c . After the analysis model  152  adjusts the weighting values w 1 -w n , the analysis model  152  again provides the plasma generation conditions vector  164  to the input neural layer  168   a . Because the weighting values are different for the various nodes  170  of the analysis model  152 , the predicted plasma data  172  will be different than in the previous iteration. The analysis model  152  again generates an error value by comparing the actual removal efficiency to the predicted plasma data  172 . 
     The analysis model  152  again adjusts the weighting values w 1 -w n  associated with the various nodes  170 . The analysis model  152  again processes the plasma generation conditions vector  164  and generates a predicted plasma data  172  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. 7  illustrates a single plasma generation conditions vector  164  being passed to the encoder  160 . In practice, the training process includes passing a large number of plasma generation conditions vectors  164  through the analysis model  152 , generating a predicted plasma data  172  for each plasma generation conditions vector  164 , and generating an associated error value for each predicted plasma data. The training process can also include generating an aggregated error value indicating the average error for all the predicted plasma data for a batch of plasma generation conditions vectors  164 . The analysis model  152  adjusts the weighting values w 1 -w n  after processing each batch of plasma generation conditions vectors  164 . The training process continues until the average error across all plasma generation conditions vectors  164  is less than a selected threshold tolerance. When the average error is less than the selected threshold tolerance, the training of the encoder  160  is complete and the analysis model is trained to accurately predict the plasma data based on the plasma generation conditions. 
     The decoder  162  operates and is trained in a similar manner as the encoder  160  as described above. During the training process of the decoder  162 , the decoder receives plasma quality data associated with a plasma generation conditions vector  164 . The plasma quality data is received by each node  170  of the first neural layer  168   d  of the decoder  162 . The nodes  170  and the first neural layer  168   d  apply their respective functions F(x) to the plasma quality data values and pass the resulting scalar values to the nodes  170  of the next neural layer  168   e . After the final neural layer  168   f  processes the scalar values received from the previous neural layer (not shown), the final neural layer  168   f  outputs a predicted plasma generation conditions vector  174 . The predicted plasma generation conditions vector  174  has the same form as the plasma generation conditions vector  164 . The data fields  175  of the predicted plasma generation conditions vector  174  represent the same parameters or conditions as the data fields  166  of the plasma generation conditions vector  164 . 
     The training process compares the predicted plasma generation conditions vector  174  to the plasma generation conditions vector  164  and determines an error value. The weighting parameters of the functions F(x) of the nodes  170  of the decoder  162  are adjusted and the plasma quality data is again provided to the decoder  162 . The decoder  162  again generates a predicted plasma generation conditions vector  174  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 plasma quality data from the historical plasma data until the decoder  162  can generate, for each historical plasma data value, a predicted plasma generation conditions vector  172  that matches the corresponding plasma generation conditions vector  164 . The training process is complete when a prediction cumulative error value is lower than the threshold error value. 
     After the encoder  160  and the decoder  162  have been trained as described above, the analysis model  152  is ready to generate recommended plasma generation to improve the plasma quality, and hence, the resulting EUV quality produced by the EUV photolithography systems described in relation to  FIGS. 1-6 . 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. 1-6 . The encoder  160  processes the current plasma generation conditions vector and generates a predicted future plasma data based on the current plasma generation conditions vector. If the predicted future plasma data is less than desired, then the decoder  162  is utilized to generate a set of recommended plasma generation conditions that will result in a higher plasma quality. In particular, the decoder  162  receives increased plasma quality values. The decoder  162  then generates a predicted plasma generation conditions vector based on the higher removal efficiency data value. 
     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. 
     Many other kinds of analysis models, training processes, and data forms can be utilized without departing from the scope of the present disclosure. 
       FIG. 8  is a method  800  for dynamically adjusting plasma generation parameters in an extreme ultraviolet radiation photolithography system, according to an embodiment. At  802 , the method  800  includes outputting a stream of droplets from a droplet generator. One example of a droplet generator is the droplet generator  108  of  FIG. 1 . One example of droplets is the droplets  142  of  FIGS. 2A-2C . At  804 , the method  800  includes generating, in a plasma generation chamber, a plasma by irradiating the droplets with a laser. One example of a plasma generation chamber is the plasma generation chamber  101  of  FIGS. 2A-2C . One example of a laser is the laser  102  of  FIGS. 2A-2C . At  806 , the method  800  includes sensing, with one or more light sensors, extreme ultraviolet radiation emitted from the plasma. One example of a light sensor is the light sensor  126  of  FIGS. 2A-2C . At  808 , the method  800  includes adjusting, with the control system, one or more plasma generation parameters based, at least in part, on characteristics of the extreme ultraviolet radiation sensed by the one or more light sensors. One example of the control system is the control system  114  of  FIGS. 2A-2C . 
       FIG. 9  is a method  900  for reducing damage from charged particles in an extreme ultraviolet radiation photolithography system, according to an embodiment. At  902 , the method  900  includes outputting a stream of droplets from a droplet generator. One example of a droplet generator is the droplet generator  108  of  FIGS. 2A-2C . One example of droplets is the droplets  142  of  FIGS. 2A-2C . At  904 , the method  900  includes generating, in a plasma generation chamber, a plasma by irradiating the droplets with a laser. One example of a plasma generation chamber is the plasma generation chamber  101  of  FIGS. 2A-2C . One example of a laser is the laser  102  of  FIGS. 2A-2C . At  906 , the method  900  includes directing extreme ultraviolet light emitted by the plasma to a scanner. One example of a scanner is the scanner  103  of  FIGS. 2A-2C . At  908 , the method  900  includes performing a photolithography process when the extreme ultraviolet radiation enters the scanner. At  910 , the method  900  includes deflecting, into a charged particle box and with a magnetic deflector, charged particles that enter the scanner. One example of a charged particle capture box is the electron capture box  139  of  FIGS. 2A-2C . One example of a magnetic deflector is the deflector  134   a  of  FIGS. 2A-2C . 
     In one embodiment, a photolithography system includes a plasma generation chamber and a droplet generator configured to output a stream of droplets into the plasma generation chamber. The system includes a laser configured to generate a plasma from the droplets by irradiating the droplets in the plasma generation chamber. The system includes one or more light sensors configured to detect extreme ultraviolet radiation emitted from the plasma and to output first sensor signals indicative of the extreme ultraviolet radiation. The system includes a control system configured to receive the first sensor signals, to analyze the first sensor signals, and to adjust plasma generation parameters based, at least in part, on the first sensor signals. 
     In one embodiment, a method includes outputting a stream of droplets from a droplet generator, generating, in a plasma generation chamber, a plasma by irradiating the droplets with a laser. The method includes sensing, with one or more light sensors, extreme ultraviolet radiation emitted from the plasma and adjusting, with a control system, one or more plasma generation parameters based, at least in part, on characteristics of the extreme ultraviolet radiation sensed by the one or more light sensors. 
     In one embodiment, a method includes outputting a stream of droplets from a droplet generator and generating, in a plasma generation chamber, a plasma by irradiating the droplets with a laser and directing extreme ultraviolet light emitted by the plasma to a scanner. The method includes performing a photolithography process with the extreme ultraviolet radiation that enters the scanner. The method includes deflecting, into a charged particle capture box and with a magnetic deflector, charged particles that enter the scanner. 
     Embodiments of the present disclosure provide many benefits to extreme ultraviolet radiation photolithography systems. Embodiments of the present disclosure dynamically adjust plasma generation properties based on various sensors and machine learning processes. Embodiments of the present disclosure also deflect charged particles from damaging sensitive components of the photolithography system. Accordingly, embodiments of the present disclosure reduce damage to expensive photolithography components including photolithography masks, optical systems, and semiconductor wafers. Additionally, embodiments of the present disclosure improve the efficiency of generation of extreme ultraviolet light by dynamically adjusting parameters of the photolithography system responsive to the sensor signals. 
     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.