System and method for monitoring and controlling extreme ultraviolet photolithography processes

Abstract

Photolithography system, comprising: a plasma generation chamber (101); a droplet generator (108) configured to deliver a stream of droplets (142) into the plasma generation chamber; a laser (102) configured to generate a plasma from the droplets by irradiating the droplets in the plasma generation chamber; one or more first charged particle detectors configured to detect the speed, intensity, and / or energy of the charged particles ejected from the plasma and to output first sensor signals indicative of the charged particles; and a control system (114) configured to receive the first sensor signals, analyze the first sensor signals, and adjust plasma generation parameters based at least in part on the first sensor signals.

Description

BACKGROUNDTechnical field

[0001] The present disclosure relates to the field of photolithography. In particular, the present disclosure relates to extreme ultraviolet photolithography. Description of the state of the art

[0002] There is a continuous need for increasing computing power in electronic devices, such as smartphones, tablets, desktop computers, laptop computers, and many other types of electronic devices. The computing power for these electronic devices is provided by integrated circuits. One way to increase computing power in integrated circuits is to increase the number of transistors and other integrated circuit elements that can be incorporated into a given area of ​​the semiconductor substrate.

[0003] The elements on an IC die are partially fabricated using photolithography. Conventional photolithography techniques involve creating a mask that outlines the structure of the elements to be formed on an IC die. The photolithography light source illuminates the IC die through the mask. The size of the elements that can be fabricated via photolithography of the IC die is limited, at the lower end, in part by the wavelength of the light generated by the photolithography light source. Smaller feature sizes can be produced using shorter light wavelengths.

[0004] Extreme ultraviolet light is used to manufacture particularly small elements because of its relatively short wavelengths. For example, extreme ultraviolet light is typically generated 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 propagates toward a collector with an elliptical or parabolic surface. The collector reflects the extreme ultraviolet light onto a scanner. The scanner illuminates the target with the extreme ultraviolet light via a mask.

[0005] An extreme ultraviolet light source with an ion detector is known from US 2008 / 0 087 840 A1, US 2019 / 0 239 329 A1, and US 2012 / 0 267 553 A1. US 2017 / 0 280 545 A1 discloses an application of the Thompson effect in an extreme ultraviolet light source. DE 10 2017 207 458 A1 discloses a particle trap in an extreme ultraviolet projection exposure system, in which charged particles can be deflected into a collecting device. US 2020 / 0 057 382 A1 discloses a method for generating extreme ultraviolet light, in which a machine learning model is used as a feedback control system. Further prior art is known from US 2010 / 0 327 192 A1, US 2020 / 0 344 868 A1 and US 2009 / 0 072 167 A1. BRIEF DESCRIPTION OF THE DIFFERENT VIEWS OF THE DRAWINGS Fig. 1 is a block diagram of a photolithography system according to one embodiment. Fig. 2A to 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 flowchart of a method of 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 flowchart of a method of operating a photolithography system according to one embodiment. Fig. 9 is a flowchart of a method of operating a photolithography system according to one embodiment. DETAILED DESCRIPTION

[0006] In the following description, many thicknesses and materials are described for various layers and structures within an IC die. Specific dimensions and materials are provided for various embodiments by way of example. Those skilled in the art will recognize, in light of the present disclosure, that other dimensions and materials may be used in many cases without departing from the scope of the present disclosure.

[0007] The following disclosure provides many different embodiments or examples for implementing various elements of the described invention. Specific examples of components and arrangements are described below to simplify the present description. For example, in the following description, forming a first element over or on top of a second element may include embodiments in which the first and second elements are formed in direct contact, and may also include embodiments in which additional elements may be formed between the first and second elements such that the first and second elements need not be in direct contact. Furthermore, reference numerals and / or letters may be repeated throughout the various examples in the present disclosure.This repetition is for convenience and clarity of illustration and does not in itself determine the relationship between the various embodiments and / or configurations described.

[0008] Furthermore, for ease of description, terms of spatial relationship such as "beneath," "under," "lower," "above," "upper," and the like may be used herein to describe the relationship of one element or feature to another element(s) or feature(s) as illustrated in the figures. The terms of spatial relationship are intended to encompass other orientations of the device in use or operation, in addition to the orientation depicted in the figures. The devices may be oriented differently (rotated 90 degrees or have other orientations), and the spatial relationship descriptors used herein may be interpreted accordingly.

[0009] In the following description, certain specific details are set forth in order to provide a thorough understanding of various embodiments of the disclosure. However, those 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 manufacturing techniques have not been described in detail to avoid unnecessarily obscuring the descriptions of the embodiments of the present disclosure.

[0010] Unless the context requires otherwise, the word "comprise" and variations thereof, e.g., "comprises" and "comprising," throughout the specification and the following claims, are to be construed in an open, inclusive sense, that is, as "including but not limited to."

[0011] The use of ordinal numbers, such as first, second, and third, does not necessarily imply a ranking, but is instead intended to distinguish between multiple instances of an action or structure.

[0012] Reference in 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, while the phrase "in one embodiment" appears in various places throughout this specification, it is not necessarily referring to the same embodiment each time. Furthermore, the particular features, structures, or characteristics may be combined in one or more embodiments in any suitable manner.

[0013] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include the plural forms unless the context clearly indicates otherwise. It should also be noted that the term "or" is used generically in its sense, which includes "and / or," unless the context clearly indicates otherwise.

[0014] Embodiments of the present disclosure provide many advantages for photolithography systems using extreme ultraviolet radiation. In embodiments of the present disclosure, plasma generation characteristics are dynamically adjusted based on various sensors and machine learning techniques. Furthermore, in embodiments of the present disclosure, charged particles are deflected to avoid damaging delicate components of the photolithography system. Accordingly, in embodiments of the present disclosure, damage to expensive photolithography components, including photolithography masks, optical systems, and semiconductor wafers, is reduced. Furthermore, embodiments of the present disclosure improve the efficiency of extreme ultraviolet light generation by dynamically adjusting parameters of the photolithography system in response to the sensor signals.

[0015] Fig. 1 is a block diagram of a photolithography system 100 according to 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 guided 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.

[0016] In one embodiment, photolithography system 100 is a laser-produced plasma (LPP) extreme ultraviolet radiation photolithography system. Photolithography system 100 includes a laser 102, a collector 106, a droplet generator 108, and a droplet receiver 110. Laser 102, collector 106, and droplet generator 108 cooperate to generate extreme ultraviolet radiation within plasma generation chamber 101.

[0017] The droplet generator 108 generates and dispenses a droplet stream. The droplets may comprise liquid (molten) tin in one example. Other droplet materials may also be used without departing from the scope of the present disclosure. The droplets move at high speed toward the droplet receiver 110. The photolithography system 100 uses the droplets to generate extreme ultraviolet light for photolithography processes. Extreme ultraviolet light typically corresponds to light with wavelengths from 1 nm to 125 nm.

[0018] The laser 102 emits a laser beam. The laser beam is focused to a point through which the droplets pass on their way from the droplet generator 108 to the droplet receiver 110. Specifically, the laser 102 emits 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 emits extreme ultraviolet radiation.

[0019] In one embodiment where 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 with wavelengths other than 13.5 nm may also be used without departing from the scope of the present disclosure.

[0020] In one embodiment, the radiation emitted by the plasma is randomly scattered in many directions. Photolithography system 100 uses collector 106 to collect the scattered extreme ultraviolet radiation from the plasma droplets and reflects the extreme ultraviolet radiation toward scanner 103. Scanner 103 directs the extreme ultraviolet radiation toward photolithography target 104.

[0021] In one embodiment, collector 106 has an aperture. The laser pulses from laser 102 pass through the aperture in the direction of the droplet stream. This allows collector 106 to be positioned between laser 102 and photolithography target 104.

[0022] After the droplets have been irradiated by the laser 102, the droplets continue their trajectory toward the droplet receiver 110. The droplet receiver 110 receives the droplets in a droplet reservoir. The droplets can be drained from the droplet reservoir and reused or discarded.

[0023] Extreme ultraviolet radiation photolithography systems face many problems. For example, after the droplets are irradiated with the laser 102 and converted into a plasma, many charged particles are scattered around the plasma generation chamber 101. The charged particles may include ions and free electrons. This is because when the droplets are converted into a plasma, the atoms in the droplets are ionized, and many free electrons are generated. Accordingly, the droplets converted into a plasma comprise a fluid (a plasma) of charged particles containing ions and free electrons.

[0024] Some of the charged particles released from the plasma may travel toward the scanner 103. The charged particles can damage components within the scanner 103. The scanner 103 may include highly sensitive precision optics, such as lenses and mirrors. The scanner 103 also includes the photolithography mask, which defines the pattern to be printed on the photolithography target 104. Typically, the photolithography target 104 is a semiconductor wafer. The charged particles can damage all of these components. Damage to the mask or any of the other optical components can result in non-functional semiconductor wafers that must be discarded at great expense. If the mask is damaged by the 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 scanner 103. As used herein, the term "charged particles" includes, but is not limited to, electrons, protons, and ions.

[0025] Another challenge facing extreme ultraviolet photolithography systems is that it can be extremely difficult to fine-tune the plasma generation parameters to generate sufficient extreme ultraviolet radiation. Fine-tuning parameters can include droplet velocity, 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 currently at a satisfactory level of effectiveness and efficiency, it can be very difficult to determine which parameters need to be adjusted.

[0026] 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 protect sensitive components of the scanner 103. The control system 114 adjusts parameters of the plasma generation process in response to the side-scatter detection system 116 and the charged particle detection system 118.

[0027] In one embodiment, the side-scatter detection system 116 detects a current intensity level of the extreme ultraviolet light generated in the plasma generation chamber 101. Specifically, the side-scatter detection system 116 detects extreme ultraviolet light emitted with a substantially lateral trajectory. The side-scatter detection system 116 can detect refracted light, reflected light, diffracted light, and scattered light.

[0028] The total intensity of the extreme ultraviolet light emitted by the plasma can be estimated or calculated based on the amount of light received by the side-scatter detection system 116. On average, the plasma emits extreme ultraviolet light in all directions at the same rate or with known relationships between different scattering directions. Accordingly, the total intensity of the extreme ultraviolet light can be estimated or calculated based on the light received by the side-scatter detection system 116.

[0029] In one embodiment, the extreme ultraviolet side scatter detection system 116 provides sensor signals to the control system 114. The sensor signals indicate the light intensity at the light sensors. The control system 114 receives the sensor signals and can adjust parameters of the photolithography system 100 in response to the sensor signals.

[0030] In one embodiment, control system 114 adjusts parameters of photolithography system 100 to more effectively generate extreme ultraviolet radiation. Control system 114 may adjust one or more of the droplet velocity, droplet size, laser pulse power, laser pulse timing, laser pulse profile, initial droplet temperature, pressure within the plasma generation chamber, or other parameters.

[0031] In one embodiment, the photolithography system 100 uses the charged particle detection system 118 to detect charged particles ejected from the plasma. As previously described, 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 the droplets or otherwise move away from them. The properties of the charged particles ejected from the plasma are indicative of properties of the plasma itself. The properties of the charged particles may 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 properties. Accordingly, the charged particle detection system 118 detects the charged particles and generates sensor signals indicative of charged particle parameters.The charged particle detection system 118 transmits the sensor signals to the control system 114.

[0032] In one embodiment, the charged particle detection system 118 includes an array of charged particle detectors positioned within the plasma generation chamber 101. In other words, the charged particle detectors may be arranged at various locations within the plasma generation chamber 101. Each of the charged particle detectors detects the impact of charged particles on the charged particle detectors. The charged particle detectors transmit sensor signals indicating properties of the charged particles to the control system 114.

[0033] In one embodiment, the control system 114 may adjust parameters of the photolithography system 100 in response to the sensor signals from the charged particle detectors. The control system 114 may adjust the same types of parameters of the photolithography system 100 as those previously described with respect to the side scatter detection system 116. The control system 114 may adjust the parameters of the photolithography system to more effectively generate the extreme ultraviolet radiation for performing photolithography.

[0034] In one embodiment, the control system 114 adjusts parameters of the photolithography system 100 in response to sensor signals from the side scatter detection system 116 and the charged particle detection system 118.

[0035] In one embodiment, the charged particle deflection system 119 is disposed within the scanner 103. The charged particle deflection system is configured to protect sensitive devices 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 from the plasma generation chamber 101 may enter the scanner 103 through the intermediate focus aperture. If the charged particles impact the mask or other sensitive components within the scanner 103, severe damage may occur to the photolithography system or process. 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.

[0036] In one embodiment, the charged particle deflection system 119 includes one or more deflection elements that generate a magnetic field in an area between the intermediate focus aperture 120 and sensitive devices of the scanner 103. As the charged particles move through the magnetic field generated by the deflection element, the trajectory of the charged particles is adjusted due to the forces acting on charged particles moving through a magnetic field. The direction of the magnetic field is selected to cause charged particles initially traveling toward sensitive components within the scanner 103 to pivot to a safe trajectory. The charged particles can then be collected or trapped, preventing damage to sensitive components within the scanner 103.Alternatively, the charged particle deflection system 119 can use electric fields or a combination of electric and magnetic fields to deflect charged particles.

[0037] In one embodiment, the photolithography system 100 can capture plasma information and stray light by implementing monitoring and control systems. Furthermore, by recording detailed plasma information, the photolithography system can reconstruct a 3D image through machine learning or computation by an artificial intelligence system, resulting in the ability to provide sophisticated control of light energy management and provide more plasma information for analysis. The photolithography system 100 helps explore issues such as ionization rate, conversion efficiency, dynamic time-resolved plasma density distribution, tin debris migration, tin-to-scanner migration, collector lifetime control, and may also provide a potential path for diagnosing a tin-to-scanner aperture mechanism.

[0038] Fig. 2A to 2C are illustrations of a photolithography system 200 according to one embodiment. The photolithography system 200 is an extreme ultraviolet photolithography system that generates extreme ultraviolet radiation through laser-plasma interaction. The plasma may be generated in a substantially similar manner as described with respect to Fig. 1 described. Fig. Figure 2A shows the photolithography system before generating the plasma and extreme ultraviolet radiation. Fig. 2B and Fig. 2C show the photolithography system 200 during the generation of the plasma in extreme ultraviolet radiation. Fig. Figure 2B shows the extreme ultraviolet radiation emitted by the droplets converted to plasma. Fig. Figure 2C shows charged particles emitted by the droplets being converted. In practice, the extreme ultraviolet radiation and the charged particles are present simultaneously. However, for illustrative purposes, the extreme ultraviolet radiation is only Fig. 2B, while the charged particles are only in Fig. 2C are shown.

[0039] Referring to Fig. 2A, the photolithography system 200 includes a plasma generation chamber 101, a 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 to generate extreme ultraviolet radiation and to perform photolithography processes using the extreme ultraviolet radiation.

[0040] The droplet generator 108 generates and discharges a droplet stream 142. The droplets comprise tin, as previously described, although droplets of other materials may also be used without departing from the scope of the present disclosure. The droplets 142 move at high speed toward the droplet receiver 110.

[0041] In one embodiment, the droplet generator 108 generates 40,000 to 100,000 droplets per second. The droplets 142 have an initial velocity of 60 m / s to 200 m / s. The droplets have a diameter of 10 µm to 200 µm. The droplet generator 108 may generate different numbers of droplets per second than described above without departing from the scope of the present disclosure. The droplet generator 108 may also generate droplets having different initial velocities than those described above without departing from the scope of the present disclosure.

[0042] The laser 102 is arranged behind the collector 106. During operation, the laser 102 emits laser light pulses 144 (see Fig. 2B). The laser light pulses 144 are focused to a point through which the droplets pass on their way from the droplet generator 108 to the droplet receiver 110. Each laser light pulse 144 is received by a droplet 142. When the droplet 142 receives the laser light pulse 144, the energy from the laser light pulse 144 generates a high-energy plasma from the droplet 142. The high-energy plasma emits extreme ultraviolet radiation.

[0043] In one embodiment, laser 102 is a carbon dioxide (CO2) laser. The CO2 laser emits radiation or laser light 144 with a wavelength centered around 9.4 µm or 10.6 µm. Laser 102 may include lasers other than carbon dioxide lasers and may emit radiation at wavelengths other than those described above without departing from the scope of the present disclosure.

[0044] 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 stronger 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. However, the laser 102 may also irradiate the droplets 142 in a manner different from that described above without departing from the scope of the present disclosure. For example, the primary laser here not only causes the droplets to take on a disc-like shape, but also atomizes them or converts them into a gaseous state.

[0045] In one embodiment, the droplets are tin. When the tin droplets 142 are converted into a plasma, the tin droplets 142 emit extreme ultraviolet radiation 146 having a wavelength centered between 10 nm and 15 nm. More specifically, in one embodiment, the tin plasma emits extreme ultraviolet radiation having a central wavelength of 13.5 nm. These wavelengths correspond to extreme ultraviolet radiation. Materials other than tin may also be used for the droplets 142 without departing from the scope of the present disclosure. Such other materials may produce extreme ultraviolet radiation having wavelengths other than those described above without departing from the scope of the present disclosure.

[0046] In one embodiment, the radiation emitted by the droplets is randomly scattered in multiple directions. Photolithography system 100 uses collector 106 to collect the scattered extreme ultraviolet radiation 146 from the plasma and emit the extreme ultraviolet radiation toward a photolithography target 104.

[0047] In one embodiment, the collector 106 is a parabolic or elliptical mirror. The scattered radiation 146 is collected by the parabolic or elliptical mirror and reflected with a trajectory toward the scanner 103. The scanner uses a series of optical conditioning devices, e.g., mirrors and lenses, to guide the extreme ultraviolet radiation to the photolithography mask. The extreme ultraviolet radiation 146 is reflected off 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 the purposes of this disclosure, no details of the mask are shown in the various configurations of optical devices in the scanner 103.

[0048] In one embodiment, the collector 106 has a central aperture 125. The laser light pulses 144 pass from the laser 102 through the central aperture 125 toward the droplet stream 142. This allows the collector 106 to be positioned between the laser 102 and the photolithography target 104.

[0049] In one embodiment, the photolithography system 200 includes a plurality of light sensors 126. The light sensors 126 are arranged to detect side scattering of extreme ultraviolet radiation from the plasma-converted droplets 142. The light sensors 126 may be part of a side scatter detection system 116, as described with respect to Fig. 1. The light sensors 126 can also be combined here, e.g., into a grating, to extend the application or function into spectroscopy.

[0050] In one embodiment, the light sensors 126 collectively detect a current intensity level of the extreme ultraviolet radiation generated in the plasma generation chamber 101. Specifically, the light sensors 126 detect light from extreme ultraviolet radiation emitted with a largely lateral trajectory.

[0051] In one embodiment, the light sensors 126 are used to detect Thomson scattering of extreme ultraviolet radiation from the droplets converted to plasma. The phenomenon of Thomson scattering occurs due to elastic scattering of electromagnetic radiation by a single free charged particle. This can be used as a high-temperature plasma diagnostic technique. In particular, Thomson scattering measurements can be used to determine the ionization rate in the droplets. The intensity of the scattered light is based in part on the extent of interactions between the laser and the plasma. Accordingly, the ionization rate can be determined from the intensity of the scattered light. The intensity of Thomson scattering is independent of the wavelength of the incident light. Thus, Thomson scattering can be useful for analyzing the relationship between the electric field of the incident light and the electron density.The light sensors 126 generate signals indicating the intensity of the laterally scattered light and transmit the signals to the control system 114.

[0052] The total intensity of the extreme ultraviolet light emitted by the plasma can be estimated or calculated based on the amount of light received by the light sensors 126. On average, the plasma emits extreme ultraviolet light in all directions at the same rate or with known relationships between different scattering directions. Accordingly, the total intensity of the extreme ultraviolet light can be estimated or calculated based on the light received by the light sensors 126.

[0053] In one embodiment, the light sensors 126 may be arranged substantially in the same lateral plane as the droplet generator 108 and the droplet receiver 110. Although Fig. While Figures 2A to 2C depict 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 the 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 the droplet generator 108 and the droplet receiver 110. Alternatively, the light sensors 126 may be positioned at different heights equal to or above the droplet generator 108 and the droplet receiver 110.

[0054] In one embodiment, the photolithography system 200 includes a plurality of lenses 128. Each lens 128 is arranged to focus light scattered by the plasma-converted droplets 142 onto or into the light sensors 126. Although the lenses 128 are illustrated as being located outside the plasma generation chamber 101, in practice, the lenses 128 may be arranged at different locations or in different orientations than shown in Fig. 2A to 2C.

[0055] In one embodiment, the one or more lenses 128 are connected to an edge of the collector 106. The lenses can be arranged in the same lateral plane as the droplet generator 108 and the droplet receiver 110. Laterally scattered light from the plasma droplets passes through the lenses and is focused onto the light sensors 126.

[0056] In one embodiment, the one or more lenses 128 may correspond to windows in the wall of the plasma generation chamber 101. Accordingly, a wall of the plasma generation chamber 101 may have windows or apertures. The lenses 128 or lens materials may be disposed in the windows or apertures. When light is scattered by the plasma, the light passes through the windows and onto the light sensors. The lenses 128 disposed in the windows or apertures may focus the light onto the light sensors 126.

[0057] In one embodiment, the light sensors 126 provide sensor signals to the control system 114. The sensor signals indicate the intensity of the light received by the light sensors. The control system 114 receives the sensor signals and can adjust parameters of the photolithography system 200 in response to the sensor signals.

[0058] In one embodiment, control system 114 adjusts parameters of photolithography system 200 to more effectively generate extreme ultraviolet radiation. Control system 114 may adjust one or more of the droplet velocity, droplet size, laser pulse power, laser pulse timing, laser pulse sequence profile, initial droplet temperature, pressure within the plasma generation chamber, or other parameters.

[0059] In one embodiment, adjusting aspects of the laser pulses may include adjusting the flattening pulse with which the droplets 142 are initially flattened. As previously described, 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 disk or pancake partially determines how effectively the plasma can be generated from the droplets 142 by the subsequent plasma generation pulse. Accordingly, the parameters of the flattening pulse partially determine 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 may adjust aspects of the flattening laser pulse in response to the sensor signals.

[0060] In one embodiment, adjusting aspects of the laser pulses may include adjusting the plasma generation pulse used to generate plasma from the flattened droplet 142. The plasma generation pulse is used to generate a plasma from the flattened droplet. The timing, pulse shape, and power of the plasma generation pulse may be adjusted by the control system 114 in response to the sensor signals from the light sensors 126.

[0061] In one embodiment, the photolithography system includes charged particle detectors 130. The charged particle detectors 130 may be part of a charged particle detection system, e.g., the charged particle detection system 118 of the Fig. 1. The charged particle detectors 130 are configured to detect charged particles ejected from the plasma.

[0062] As previously described, the process of generating a plasma results in the generation of charged particles within the droplets. Some of the charged particles may be ejected from the droplets or otherwise move away from them. The properties of the charged particles ejected from the plasma indicate properties of the plasma itself. The properties of the charged particles may 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 properties.

[0063] Fig. Figure 2C illustrates charged particles 148 scattered by a droplet 142 converted to plasma. For clarity, Fig. 2C does not depict the extreme ultraviolet radiation 146, which is also emitted from the plasma-converted droplet 142. The charged particle detectors 130 are configured to detect the charged particles 148 emitted from the plasma-converted droplet 142.

[0064] In one embodiment, charged particle detectors 130 are connected to control system 114. Charged particle detectors 130 are configured to generate sensor signals indicative of charged particle parameters. Charged particle detectors 130 transmit the sensor signals to control system 114.

[0065] In one embodiment, an array of charged particle detectors 130 is disposed within the plasma generation chamber 101. The array of charged particle detectors 130 can be arranged to detect a variety of charged particle trajectories within the plasma generation chamber 101. In other words, the charged particle detectors 130 can be arranged at different locations within the plasma generation chamber 101. Each of the charged particle detectors 130 detects the impact of charged particles on the charged particle detectors 130. The charged particle detectors 130 transmit sensor signals indicative of properties of the charged particles to the control system 114.

[0066] Fig. 2A through 2C illustrate the charged particle detectors 130 disposed on an exterior wall of the plasma generation chamber 101. However, the charged particle detectors 130 may also be disposed within the plasma generation chamber 101. For example, the charged particle detectors 130 may be disposed on an interior wall of the plasma generation chamber 101. Alternatively, the charged particle detectors 130 may be disposed, supported, or configured in other ways inside or outside the plasma generation chamber 101. In one embodiment, the plasma generation chamber 101 may include apertures that allow charged particles 148 to pass from inside the plasma generation chamber 101 to the charged particle detectors 130.

[0067] In one embodiment, the charged particle detectors 130 comprise charge-coupled devices configured to detect the impact of the charged particles 148. The charge-coupled devices generate signals each time a charged particle impacts the charge-coupled devices. The charge-coupled devices then transmit sensor signals to the control system 114.

[0068] In one embodiment, the charge-coupled devices for detecting charged particles comprise 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 may include a fluorescent film or sheet disposed in front of a sensing region of the charge-coupled device. When charged particles 148 strike the fluorescent film, the fluorescent film emits light. The light is detected by the charge-coupled device, and the charge-coupled device counts the impact of the charged particle.

[0069] In one embodiment, the charged particle detectors may include Faraday cups. A Faraday cup is a conductive receptacle configured to detect or trap charged particles 148 in a vacuum, e.g., a vacuum within the plasma generation chamber 101. The Faraday cup generates a current based on the charged particles 148 trapped by the Faraday cup. This current can be used to determine the number of charged particles impacting the cup. The Faraday cups may provide sensor signals to the control system 114 indicating the number of charged particles 148 collected or trapped by the Faraday cup.

[0070] In one embodiment, the photolithography system 200 includes an electron capture box 139 and an ion capture box 140 connected to the scanner 103 or a portion thereof. As previously described, 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 the first and second deflection elements 134a and 134b, are configured to capture electrons and ions, respectively. The function of the deflection elements 134a, 134b will be described in more detail below.

[0071] The charged particle detector 136 is arranged in the electron capture box 139. After the charged particles 148 pass through the intermediate focus aperture into the scanner 103, the deflection elements 134a, 134b deflect electrons into the electron capture box 139. The deflection elements 134a, 134b deflect ions into the ion capture box 140. The charged particle detector 136 is configured to detect electrons entering the scanner 103.

[0072] In one embodiment, the charged particle detector 136 is an electron multiplication charge-coupled device. As previously described, the electron multiplication charge-coupled device may be a frame transfer charge-coupled device that includes an output register. The electron multiplication charge-coupled device may include a fluorescent film or sheet disposed in front of a sensor region of the electron multiplication charge-coupled device. When electrons strike the fluorescent film, the fluorescent film emits light. The light is detected by the electron multiplication charge-coupled device, and the charge-coupled device counts the impact of the charged particle.

[0073] The charged particle detector 136 may be connected to the control system 114. The charged particle detector 136 may be part of a charged particle detection system. For example, the charged particle detector 136 may be part of the charged particle detection system 118 of the Fig. 1.

[0074] An electromagnetic lens 138 is disposed within the scanner 103. The electromagnetic lens 138 is configured to focus electrons onto the charged particle detector 136. The electromagnetic lens 138 uses electromagnetic forces to act as a lens for electrons entering the scanner 103. The electromagnetic lens 138 can help ensure that a high percentage of the electrons entering the scanner 103 are detected at the charged particle detector 136.

[0075] Although in Fig. 2A to 2C, a single charged particle detector 136 is shown, in practice, the photolithography system 200 may include multiple charged particle detectors 136. In particular, the electron capture box 139 may include multiple charged particle detectors 136. Although in Fig. 2A to 2C, the charged particle capture box 140 may also include one or more charged particle detectors configured to detect ions entering the scanner 103.

[0076] In one embodiment, the charged particle detector 136 disposed within the scanner 103 may function as a z-axis charged particle detector, in an example where the z-axis corresponds to an axis extending between the collector 106 and the intermediate focus aperture 120. As described in more detail below, the charged particle detector 136 may function as a z-axis charged particle detector, while the charged particle detectors 130 may function as detectors for other axes or angles.

[0077] In one embodiment, the collector 106 includes charged particle detectors. The charged particle detectors on the collector 106 can be used to assist in determining a z-axis distribution of charged particles from the plasma-converted droplets 142. The charged particle detectors can be arranged at various locations on the collector 106. In one embodiment, the charged particle detectors can be arranged in or adjacent to the apertures in the collector 106.

[0078] In one embodiment, the control system 114 may adjust parameters of the photolithography system 100 in response to sensor signals from the charged particle detectors 130 and / or 136. The control system 114 may adjust the same types of parameters of the photolithography system 200 as those previously described with respect to the light sensors 126. The control system 114 may adjust the parameters of the photolithography system 200 to more effectively generate the extreme ultraviolet radiation for performing photolithography.

[0079] In one embodiment, the control system 114 may generate a 3D model of the droplets 142 after the flattening pulse and / or the plasma generation pulse. Since the charged particle detectors 130, 136 are arranged at different locations in the plasma generation chamber 101 and / or the scanner 103, the sensor signals from the various charged particle detectors may be used to generate a 3D model of the droplets before the charged particles are injected. The 3D model may indicate a shape of the flattened droplets after the flattening pulse and before the plasma generation pulse. Alternatively or additionally, the 3D model may indicate a shape of the flattened droplets after the plasma generation pulse.The control system 114 can analyze the 3D model to determine whether the flattening pulse, the plasma generation pulse, the droplet velocity, the droplet size, the initial droplet temperature, or other parameters should be adjusted to generate a plasma from the droplets having a selected shape. Accordingly, the control system 114 can adjust parameters of the photolithography system 100 in response to sensor signals from the charged particle detectors.

[0080] In one embodiment, the control system 114 adjusts parameters of the photolithography system 100 in response 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 plasma-converted droplets 142, or 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 detectors 130, 136.

[0081] In one embodiment, the deflection elements 134a, 134b are arranged within the scanner 103. The deflection elements 134a, 134b may be part of a charged particle deflection system. For example, the deflection elements 134a, 134b may be part of the charged particle deflection system 119 of the Fig. 1. The charged particle deflection elements 134a, 134b are configured to protect sensitive devices 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 of the plasma generation chamber 101 may enter the scanner 103 through the intermediate focus aperture 120. If the charged particles 148 strike the mask or other sensitive components within the scanner 103, severe damage may be caused to the photolithography system or process. Accordingly, the deflection elements 134a, 134b protect the sensitive components of the scanner 103 by deflecting the charged particles 148 away from the sensitive components of the scanner 103.In particular, the deflection elements 134a, 134b are configured to deflect electrons into the electron capture box 139 and to deflect ions into the ion capture box 140.

[0082] In one embodiment, the deflection elements 134a, 134b generate a magnetic field in a vicinity between the intermediate focus aperture 120 and sensitive devices of the scanner 103. As the charged particles 148 move through the magnetic field generated by the deflection elements 134a, 134b, the trajectory of the charged particles 148 is adjusted due to the forces acting on charged particles 148 moving through the magnetic field. The direction of the magnetic field is selected to cause charged particles that initially have a trajectory toward sensitive components within the scanner 103 to pivot to a safe trajectory. The charged particles 148 can then be collected or trapped, preventing damage to sensitive components within the scanner 103.

[0083] In one embodiment, the first deflection element 134a can 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 deflection element 134a, the electron trajectories are adjusted by the Lorentz force. The Lorentz force F acts on a charged particle with charge q and velocity v moving through a magnetic field B 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 deflection element 134a is configured to deflect the negatively charged electrons into the electron capture box 139. The electromagnetic lens 138 focuses the electrons toward the charged particle detector 136.

[0084] In one embodiment, the first deflection element 134a deflects positively charged ions toward the ion capture box 140. The positive charge of the ions and the negative charge of the electrons cause them to be deflected in different directions by the deflection element 134a.

[0085] In one embodiment, the photolithography system 200 uses a second deflection element 134b to more effectively deflect positively charged ions into the ion capture box 140. The positively charged ions typically have a much higher mass than the electrons. In one example where the positively charged ions are tin ions, the mass of the positively charged ions is several orders of magnitude greater than the mass of the electrons. Accordingly, a single deflection element 134a 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 deflection element 134b to further deflect the ions into the ion capture box 140. The second deflection element 134b may be largely similar to the first deflection element 134b in that the second deflection element 134b generates a magnetic field.The second deflection element may generate a magnetic field that is much stronger than the magnetic field of the first deflection element 134a. However, other numbers and arrangements of deflection elements may be used without departing from the scope of the present disclosure.

[0086] In one embodiment, the deflection elements 134a, 134b may comprise electromagnets. The electromagnets may be disposed within the scanner 103 and may generate magnetic fields according to well-known electromagnetic principles. The electromagnets may comprise one or more conductors that conduct an electric current, thereby generating a magnetic field. Alternatively, the deflection elements 134a, 134b may comprise 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 deflection elements 134a, 134b may be disposed outside the scanner 103, yet still generate magnetic fields within the scanner 103 to deflect the charged particles 148.

[0087] In one embodiment, the control system 114 may include one or more controllers or processors. The control system 114 may 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 used to help adjust or control parameters of the photolithography system 200 in response to the sensor signals received from the light sensors 126 and / or the charged particle detectors 130, 136.

[0088] In one embodiment, the control system 114 applies machine learning to precisely adjust the parameters of the photolithography system 200. Accordingly, the control system 114 may include a machine learning model that can be trained to adjust one or more parameters of the laser pulses or the droplets 142 in response to sensor signals received from the light sensors 126 and / or the charged particle detectors 130, 136. Details of a machine learning method are described with respect to Fig. 7 described.

[0089] In one embodiment, the machine learning model comprises a neural network. The machine learning model may comprise one or more supervised machine learning models based on neural networks. The machine learning model may comprise one or more unsupervised machine learning models. Other types of machine learning models may be used to control the velocity of the droplets without departing from the scope of the present disclosure. For example, machine learning models other than machine learning models based on neural networks may be used by the control system 114. Further details of an analysis model are described with respect to Fig. 7 presented.

[0090] The image generated by electron multiplication-type charge-coupled devices may require post-processing due to different electron energies with different deflection directions. The image may contain energy (distribution across the image) and counting (intensity across the image) information. Therefore, corrections can be made to reconstruct an XY-plane image to resolve the original distribution. Given a known optics specification, the position in a volume with a specific geometry can be estimated.

[0091] In one embodiment, by recording information from the Thomson scattering phenomenon and the electron distribution in space in multiple dimensions, the original electron density distribution from the plasma could be calculated in multiple dimensions. Using optical imaging theory and optical specifications, the control system 114 can correct plasma deformation for deviations. Furthermore, the relationship between the intensity of the incident light and the electron density distribution can be derived from Thomson scattering theory. By analyzing the relationship of the electron distribution in space and combining the results in three dimensions, the control system 114 can assemble a 3D plasma model.

[0092] Fig. 3 is a top view of a portion of a photolithography system 300 according to one embodiment. The photolithography system 300 is largely similar to the photolithography system 200 described in relation to Fig. 2A to 2C, except that in Fig. 3 shows a specific distribution of the light sensors 126 and the charged particle detectors 130. The photolithography system includes a collector 106. The collector 106 has a central aperture 125 through which laser pulses can pass to flatten the droplets 142 and convert them into plasma.

[0093] The photolithography system 300 includes a plurality of light sensors 126 arranged radially around and above the collector 106. The light sensors 126 may be largely similar to the light sensors 126 described in Fig. 2A to 2C. The light sensors 126 may be configured to detect side-scattered extreme ultraviolet light from droplets 142 converted to plasma. The light sensors 126 may 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 may be largely similar to the lenses 128 described in Fig. 2A to 2C are described.

[0094] The photolithography system 300 includes a plurality of charged particle detectors 130 arranged radially around and above the collector 106. The charged particle detectors 130 may be largely similar to the charged particle detectors 130 described in Fig. 2A to 2C. The charged particle detectors 130 may be configured to provide sensor signals to the control system 114.

[0095] Fig. 4 is an illustration of a photolithography system 400 according to one embodiment. The photolithography system 400 is largely similar to the photolithography system 200 of Fig. 2A to 2C, except for the arrangement of the lenses 128. The view of the Fig. 4 shows 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 is connected to an edge of the collector 106. The wall of the plasma generation chamber 101 is connected to the lens 128. The lens 128 is configured to direct side-scattered radiation from droplets 142 converted to plasma onto the light sensor 126. In practice, the photolithography system 400 may include a plurality of lenses 128 connected to the edge of the collector 106 and arranged radially, each configured to direct side-scattered radiation onto a light sensor 126. The lenses 128 and the light sensors 126 may perform substantially similar functions to the lenses 128 and the light sensors 126 described with respect to Fig. 2A to 2C are described.

[0096] Fig. 5 is a top view of a photolithography system 500 according to one embodiment. The photolithography system 500 is largely similar to the photolithography system 200 of Fig. 2A to 2C, except that the collector 106 has a grid structure 150 that helps capture ions and charged particles emitted from the plasma-converted droplets 142. For simplicity, Fig. 5, the lenses 128, the light sensors 126, the charged particle detectors 130, and other components shown in the photolithography system 200 are not shown, although these components may also be present in the photolithography system 500. The collector 106 of the Fig. 5 is a grid collector. The grid collector has a periodic grid structure. The periodic grid structure has periodic bands 150 of a material sensitive to charged particle ions. The periodic bands 150 generate electrical signals upon contact with a charged particle. Each of the bands 150 is connected to the control system 114. The bands provide sensor signals indicating charged particles 148 that come into contact with the bands 150. The control system 114 can analyze the sensor signals and adjust parameters of the photolithography system 500 in response to the sensor signals.

[0097] In one embodiment, the surface of the collector 106 has a grating structure. The grating structure is arranged in a coaxial band pattern. The purpose is to filter out band wavelengths. Charged particle detectors can be arranged between the grating bands and used to capture ions and electrons located further downstream. Furthermore, by placing some charged particle detectors in the vicinity of the conical chamber, the resolution of the plasma information can be more detailed, allowing for improved analysis. The system error and the detector positions relative to the plasma source can be corrected by a machine learning system or an artificial intelligence system. This can be combined with the side scatter information and the deflection system.By knowing the angle, position, speed, time of flight, and energy of the moving ions / electrons, the plasma distribution can be calculated and a 3D model created. Furthermore, the moving ions and electrons can be deflected by a strong magnetic field to prevent damage to the masks and sensitive components in the scanner.

[0098] Fig. 6 is a flowchart of a method 600 for operating a photolithography system according to one embodiment. In the method 600, the structures and methods described with respect to Fig. 1 to 5 to generate plasma and collect information as described below. At 602, in method 600, a plasma is generated in a plasma generation chamber. The plasma may be generated by irradiating a droplet with a flattening laser pulse and then irradiating the droplets with a plasma conversion laser pulse, which generates a plasma from the flattened droplet. Components and methods already described with respect to Fig. 1 to 5 were described.

[0099] From 602, the method 600 proceeds to steps 604, 610, and 616. At 604, the method 600 detects side-scattered light from the droplets converted to plasma. In one example, the side-scattered light may be detected by the light sensors 126 of the Fig. 2A to 4. At 606, the method 600 records the information about the side scattered light. In one example, the control system 114 of the Fig. 2A to 2C show the sensor signals with the information about the side scattered light received by the light sensors 126.

[0100] At 608, in method 600, the information about the side-scattered light is corrected or adjusted. The side-scattered light is received by the light sensors 126 after it has passed through the lenses 128. This means that the raw sensor data generated by the light sensors does not inherently represent the accurate distribution of the side-scattered light. Accordingly, before the side-scatter data can be used to explain the state of the droplets converted to plasma, 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 conversions may be performed to adjust the raw side-scatter data. In one example, the control system 114 of the Fig. 2A to 2C Correct errors in the side-scattered light data.

[0101] At 610, the method 600 detects parameters of charged particles emitted or ejected from the plasma. In one example, the charged particle parameters may be detected by the charged particle detectors 130 of the Fig. 2A to 2C. The charged particle detectors 130 may detect the speed, intensity, energy, numbers, or other parameters of the charged particles emitted from the plasma. At 612, the method 600 records the charged particle information. In one example, the control system 114 of the Fig. 2A to 2C record the information about the charged particles based on sensor signals received by the charged particle detectors 130.

[0102] At 614, in the method 600, the information about the charged particles is corrected or adjusted. The correction or adjustment is based on the same principles as described above at 608 with respect to the side-scattered light. In particular, some calculations or conversions may be required to be performed on the raw sensor data generated by the charged particle detectors 130 to correct known distortions introduced by the charged particle detectors 130 or other components that may focus or steer the charged particles. In one example, the control system 114 of the Fig. 2A to 2C correct or adjust the charged particle data in preparation for analyzing the charged particle information.

[0103] At 616, the method 600 detects deflected charged particles. In one example, the charged particle detector 136 of the Fig. 2A to 2C, charged particles deflected by one or both of the deflection elements 134a, 134b within the scanner 103. The charged particles detected by the charged particle detector 136 may indicate z-axis scattering of the charged particles. At 618, in the method 600, the charged particle information is recorded. In one example, the control system 114 of the Fig. 2A to 2C Sensor signals from the charged particle detector 136 of the Fig. 2A to 2C and records the data on the charged particles based on the sensor signals.

[0104] At 620, in method 600, the charged particle data is corrected or adjusted. The correction or adjustment is based on the same principles as described above at 608 with respect to the side-scattered light. The charged particles detected by the charged particle detector 130 may first be reflected by the reflector 106 and may then be deflected by the deflection element 134a and subsequently focused by the electromagnetic lens 138. Accordingly, the raw sensor data provided by the charged particle detector 130 will contain distortions based on the effects of these components.Accordingly, before the sensor data from the charged particle detector 136 can be used, some calculations and conversions may be required to the raw sensor data to correct for known distortions introduced by the above-mentioned components. In one example, the control system 114 of the . Fig. 2A to 2C correct or adjust the charged particle data in preparation for analyzing the charged particle information.

[0105] From 608, 614, and 620, the method 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 the Fig. 2A to 2C different data from the adjusted recorded data and then combines the different data.

[0106] At 624, method 600 compares energy-related data associated with the charged particles and / or the side-scattered light. In one example, control system 114 compares the energy-related data associated with the charged particles and / or the side-scattered light. The energy-related data may include the energy of the detected light and the charged particles, as well as the intensity or count of the light and the charged particles. The amount of EUV radiation that can be generated is related to the energy of the plasma, which in turn is related to the intensity or count of the light and the charged particles. Accordingly, comparing the energy-related data corresponds to determining the maximum amount of EUV light that could be generated with the current plasma-converted droplets.

[0107] At 626, a 3D model of the plasma is generated in the method 600. In one example, the control system 114 of the Fig. 2A to 2C illustrate the 3D model based on the charged particle data, the side-scattered light data, the deflected charged particle data, and the comparison of the energy-related data. Specifically, based on the side-scattered light information, the XY distribution of the plasma can be calculated. The Z distribution of the plasma can be calculated based on the charged particle data acquired by the charged particle detector 136. The 3D model may correspond to the calculation of the XY and Z distributions of the plasma based on these parameters.

[0108] At 628, in method 600, adjustments to be made to the plasma generation process are determined. 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 the analysis of the 3D model. Examples of adjustments may include adjusting the timing, position, power, duration, or profile of the flattening laser pulse. Examples of adjustments may include adjusting the timing, position, power, duration, or profile of the plasma generation laser pulse. Examples of adjustments may include adjusting a droplet velocity, droplet size, droplet material, droplet temperature, droplet trajectory, or droplet shape.

[0109] In one embodiment, the adjustments to be made can be determined by an analysis model that is trained using a machine learning method. The analysis model and the machine learning method are compared with respect to Fig. 7 described in more detail.

[0110] At 630, in the method 600, the plasma generation parameters are adjusted according to the analysis of the 3D model. In one example, the control system 114 of the Fig. 2A to 2C, the plasma generation parameters are set. From 630, the method returns to 602, where plasma is generated in method 600 with the adjusted plasma generation parameters. Method 600 may be repeated throughout the extreme ultraviolet photolithography process to continuously update and adjust the plasma generation parameters to improve the extreme ultraviolet photolithography processes.

[0111] In one embodiment, method 600 or other embodiments of a photolithography system or method may include post-processing of sensor data. For example, an electron distribution image may be generated using one or more charge-coupled devices with electron multiplication. The image may be corrected by calculating the original position, and the real distribution in the XY plane may be generated. The control system may correct for system error deformation and may calculate an electron density on the target. This may be used together with the Thomson scattering data to generate a 3D plasma model. The model, or the conditions represented by the model, may be analyzed by machine learning or artificial intelligence systems of the control system, and appropriate adjustments to be made to the plasma generation parameters may be determined.

[0112] Fig. 7 is a block diagram of an analysis model 152 according to one embodiment. The analysis model 152 may be part of the control system 114 of the Fig. 1 to 2C and may, according to one embodiment, be operated in conjunction with the systems and methods described with respect to Fig. 1 to 6. The analysis model 152 may perform functions corresponding to block 628 of the Fig. 6. The analysis model 152 includes a neural encoder network 160 and a neural decoder network 162. The analysis model 152 is trained via a machine learning process to identify recommended changes to plasma generation parameters based on sensed plasma properties, e.g., those sensed by the light sensors 126, the charged particle detectors 130, and the charged particle detector 138. The analysis model of the Fig. 7 is only one example of an analysis model. Many other types of analysis models and training methods may be applied without departing from the scope of the present disclosure.

[0113] The training process uses a training set. The training set includes historical plasma generation conditions. Each set of historical plasma generation conditions includes the flattening laser pulse parameters, the plasma conversion laser pulse parameters, and the droplet parameters for a specific EUV generation process. The training set includes historical plasma data resulting from the historical plasma generation conditions for each set of historical plasma generation conditions.

[0114] Each previously performed EUV generation process has taken place under specific plasma generation conditions and resulted in specific plasma properties. The plasma generation conditions for each plasma data value are formatted into a corresponding plasma generation condition vector 164. The plasma generation condition vector 164 has several data fields 166. Each data field 166 corresponds to a specific process condition.

[0115] The example of Fig. Figure 7 illustrates a single plasma generation condition vector 164 which is passed to the encoder 160 of the analysis model 152 during the training process. In the example of Fig. 7, the plasma generation condition vector 164 includes three data fields 166. A first data field 166 corresponds to the pre-pulse laser settings. In practice, there may be multiple data fields 166 for the pre-pulse laser settings, one each for the pulse power, pulse duration, pulse timing, etc. A second data field 166 corresponds to the plasma conversion pulse settings. In practice, there may be multiple data fields 166 for each of a plurality of settings, including the 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 the droplet velocity, droplet size, droplet temperatures, etc. Each plasma generation condition vector 164 may include other types of plasma generation conditions without departing from the scope of the present disclosure.The special plasma generation conditions used in . Fig. 7 are provided merely as examples. Each process condition is represented by a numerical value in the corresponding data field 166.

[0116] The encoder 160 has a plurality of neural layers 168a through 168c. Each neural layer has a plurality of nodes 170. Each node 170 may also be referred to as a neuron.

[0117] Each node 170 from the first neural layer 168a receives the data values ​​for each data field from the plasma generation condition vector 164. Accordingly, in the example, the Fig. 7 each node 170 from the first neural layer 168a has three data values, since the plasma generation condition vector 164 has three data fields, although in practice, as mentioned above, the plasma generation condition vector 164 may have many more data fields than 3. Each neuron 170 has a corresponding inner mathematical function, which in Fig. 7 as F(x). Each node 170 of the first neural layer 168a generates a scalar value by applying the intrinsic mathematical function F(x) to the data values ​​from the data fields 166 of the plasma generation condition vector 164. Further details regarding the intrinsic mathematical functions F(x) are provided below.

[0118] In the example of Fig. 7, each neural layer 168a to 168e in both the encoder 160 and the decoder 162 is a fully connected layer. This means that each neural layer has the same number of nodes as the subsequent neural layer. In the example of Fig. 7, each neural layer 168a to 168e has five nodes. However, the neural layers of the encoder 160 and the decoder 162 may have different numbers of layers than in Fig. 7 without departing from the scope of the present disclosure.

[0119] Each node 170 of the second neural layer 168b receives the scalar values ​​generated by each node 170 of the first neural layer 168a. Accordingly, in the example, the Fig. 7 Each node of the second neural layer 168b has five scalar values, since there are five nodes 170 in the first neural layer 168a. Each node 170 of the second neural layer 168b generates a scalar value by applying the corresponding inner mathematical function F(x) to the scalar values ​​from the first neural layer 168a.

[0120] There may be one or more additional neural layers between neural layer 168a and neural layer 168c. The last neural layer 168c of encoder 160 receives the five scalar values ​​from the five nodes of the previous neural layer (not shown). The output of the last neural layer 168 is the predicted plasma data. In practice, the predicted plasma data is a vector comprising many data fields. Each data field corresponds to a specific aspect of the detected plasma properties, e.g., XY 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 130.

[0121] During the machine learning process, the analysis model compares the predicted plasma data 172 with the actual plasma data. The analysis model 172 generates an error value indicating the error, or difference, between the predicted plasma data from the data value 172 (in practice, a vector comprising many data values ​​representing values ​​associated with a 3D plasma model) and the actual plasma data. The error value is used to train the encoder 160.

[0122] The training of encoder 160 can be more fully understood by discussing the inner mathematical functions F(x). Although all nodes 170 are labeled with an inner mathematical function, each node's mathematical function F(x) is unique. In one example, each inner mathematical function has the following form: F(x)=x1*w1+x2*w2+…xn*w1+b. In the above equation, each value x1 to x n a data value received from a node 170 in the previous neural layer, or in the case of the first neural layer 168a, each value x1 to x n a corresponding data value from the data fields 166 of the plasma generation condition vector 164. Accordingly, n for a given node is equal to the number of nodes in the previous neural layer. The values ​​w1 to w n are scalar weight values ​​that belong to a corresponding node in the previous layer. The analysis model 152 selects the values ​​of the weight values ​​w1 to w n The constant b is a scalar displacement value and can also be multiplied by a weight value. The value generated by a node 170 is based on the weight values ​​w1 to w n . Accordingly, each node 170 has n weight values ​​w1 to wn Although not shown above, every function F(x) can also have an activation function. The sum given in the above equation is multiplied by the activation function. Examples of activation functions can be rectifier functions (ReLU functions, Rectified Linear Unit), sigmoid functions, hyperbolic voltage functions, or other types of activation functions. Every function F(x) can also have a transfer function.

[0123] After the error value has been calculated, the analysis model 152 adjusts the weight values ​​w1 to w n for the different nodes 170 of the different neural layers 168a to 168c. After the analysis model 152 has determined the weight values ​​w1 to w nAfter the analysis model 152 has adjusted the plasma generation condition vector 164, it again provides the plasma generation condition vector 164 to the neural input layer 168a. Since the weight values ​​for the various nodes 170 of the analysis model 152 are different, the predicted plasma data 172 is different from the previous iteration. The analysis model 152 again generates an error value by comparing the actual removal efficiency with the predicted plasma data 172.

[0124] The analysis model 152 again adjusts the weight values ​​w1 to w n associated with the various nodes 170. The analysis model 152 reprocesses the plasma generation condition vector 164 and generates predicted plasma data 172 and an associated error value. The training procedure includes adjusting the weight values ​​w1 to w n in iterations until the error is minimized.

[0125] Fig. 7 shows a single plasma generation condition vector 164 being passed to the encoder 160. In practice, the training method includes passing a large number of plasma generation condition vectors 164 through the analysis model 152, generating predicted plasma data 172 for each plasma generation condition vector 164, and generating an associated error value for all predicted plasma data. The training method may also include generating a summed error value indicating the average error for all predicted plasma data for a set of plasma generation condition vectors 164. The analysis model 152 adjusts the weight values ​​w1 through w nafter processing each set of plasma generation condition vectors 164. The training process continues until the average error across all plasma generation condition vectors 164 is less than a selected tolerance threshold. When the average error is less than the selected tolerance threshold, 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.

[0126] Decoder 162 operates similarly to encoder 160, as described above, and is trained in a similar manner. During the training process of decoder 162, decoder 162 receives plasma property data corresponding to a plasma generation condition vector 164. The plasma property data is received from each node 170 of the first neural layer 168d of decoder 162. Nodes 170 and the first neural layer 168d apply their respective functions F(x) to the values ​​of the plasma property data and feed the resulting scalar values ​​to nodes 170 of the next neural layer 168e. After the last neural layer 168f processes the scalar values ​​it received from the previous neural layer (not shown), the last neural layer 168f outputs a predicted plasma generation condition vector 174.The predicted plasma generation condition vector 174 has the same shape as the plasma generation condition vector 164. The data fields 175 in the predicted plasma generation condition vector 174 represent the same parameters or conditions as the data fields 166 of the plasma generation condition vector 164.

[0127] In the training process, the predicted plasma generation condition vector 174 is compared with the plasma generation condition vector 164, and an error value is determined. The weighting parameters of the functions F(x) of the nodes 170 of the decoder 162 are adjusted, and the plasma property data is again provided to the decoder 162. The decoder 162 again generates a predicted plasma generation condition vector 174, and an error value is determined. This process is repeated for all of the plasma generation condition vectors in the historical plasma generation condition data and for all of the historical plasma property data from the historical plasma data until the decoder 162 can generate a predicted plasma generation condition vector 174 for each historical plasma data value that matches the corresponding plasma generation condition vector 164.The training procedure is completed when a predicted cumulative error value is lower than the error threshold.

[0128] After the encoder 160 and the decoder 162 have been trained as described above, the analysis model 152 is ready to generate a recommended plasma generation to improve the plasma quality and thus the resulting EUV quality produced by the EUV photolithography systems that are related to Fig. 1 to 6. During operation, the analysis model receives a current plasma generation condition vector, which represents current conditions or parameters of the EUV photolithography systems related to Fig. 1 to 6. Encoder 160 processes the current plasma generation condition vector and generates predicted future plasma data based on the current plasma generation condition vector. If the predicted future plasma data is lower than desired, decoder 162 is used to generate a set of recommended plasma generation conditions that result in higher plasma quality. Specifically, decoder 162 receives increased plasma quality values. Decoder 162 then generates a predicted plasma generation condition vector based on the higher removal efficiency value.

[0129] The predicted plasma generation condition vector includes recommended values ​​for the plasma generation conditions for certain plasma generation condition types. For example, the predicted plasma generation condition vector may include recommended values ​​for the various pre-pulse laser conditions, the plasma conversion laser pulse conditions, and the droplet conditions.

[0130] Many other types of analysis models, training methods, and data forms may be used without departing from the scope of the present disclosure.

[0131] Fig. 8 is a method 800 for dynamically adjusting plasma generation parameters in an extreme ultraviolet radiation photolithography system according to one embodiment. At 802, the method includes dispensing a stream of droplets from a droplet generator. An example of a droplet generator is droplet generator 108 of Fig. 1. An example of droplets are the droplets 142 of the Fig. 2A to 2C. At 804, the method 800 includes generating a plasma in a plasma generation chamber by irradiating the droplets with a laser. An example of a plasma generation chamber is the plasma generation chamber 101 of Fig. 2A to 2C. An example of a laser is the laser 102 of the Fig. 2A to 2C. At 806, the method 800 includes detecting extreme ultraviolet radiation emitted from the plasma with one or more light sensors. An example of a light sensor is the light sensor 126 of the Fig. 2A to 2C. At 808, the method includes adjusting one or more plasma generation parameters with the control system based at least in part on characteristics of the extreme ultraviolet radiation detected by the one or more light sensors. An example of the control system is control system 114 of the Fig. 2A to 2C.

[0132] Fig. 9 is a method 900 for reducing charged particle damage in an extreme ultraviolet radiation photolithography system according to one embodiment. At 902, the method 900 includes dispensing a stream of droplets from a droplet generator. An example of a droplet generator is the droplet generator 108 of Fig. 1. An example of droplets are the droplets 142 of the Fig. 2A to 2C. At 904, the method 900 includes generating a plasma in a plasma generation chamber by irradiating the droplets with a laser. An example of a plasma generation chamber is the plasma generation chamber 101 of Fig. 2A to 2C. An example of a laser is the laser 102 of the Fig. 2A to 2C. At 906, the method 900 includes directing extreme ultraviolet light emitted by the plasma onto a scanner. An example of a scanner is scanner 103 of Fig. 2A to 2C. At 908, the method 900 includes performing a photolithography process as the extreme ultraviolet radiation enters the scanner. At 910, the method 900 includes deflecting charged particles entering the scanner with a magnetic deflector into a charged particle box. An example of a charged particle capture box is the electron capture box 139 of the Fig. 2A to 2C. An example of a magnetic deflection element is the deflection element 134a of Fig. 2A to 2C.

[0133] The invention is defined by the main claim and the subordinate claims. Further embodiments of the invention are recited in the dependent claims.

[0134] Embodiments of the present disclosure provide many advantages for extreme ultraviolet radiation photolithography systems. In embodiments of the present disclosure, plasma generation characteristics are dynamically adjusted based on various sensors and machine learning techniques. Furthermore, in embodiments of the present disclosure, charged particles are deflected so that they do not damage sensitive components of the photolithography system. Accordingly, in embodiments of the present disclosure, damage to expensive photolithography components, such as photolithography masks, optical systems, and semiconductor wafers, is reduced. Furthermore, in embodiments of the present disclosure, the efficiency of extreme ultraviolet light generation is improved by dynamically adjusting parameters of the photolithography system in response to the sensor signals.

Claims

[1] Photolithography system, comprising: a plasma generation chamber (101); a droplet generator (108) configured to deliver a stream of droplets (142) into the plasma generation chamber; a laser (102) configured to generate a plasma from the droplets by irradiating the droplets in the plasma generation chamber; one or more first charged particle detectors configured to detect the speed, intensity, and / or energy of the charged particles ejected from the plasma and to output first sensor signals indicative of the charged particles; and a control system (114) configured to receive the first sensor signals, analyze the first sensor signals, and adjust plasma generation parameters based at least in part on the first sensor signals. [2] The photolithography system of claim 1, further comprising one or more light sensors (126) configured to detect extreme ultraviolet radiation emitted from the plasma and output second sensor signals indicative of the extreme ultraviolet radiation. [3] The photolithography system of claim 2, wherein the one or more first charged particle detectors comprise a charge-coupled device. [4] The photolithography system of claim 2, wherein the one or more first charged particle detectors comprise a Faraday cup. [5] The photolithography system of claim 2, 3 or 4, wherein the one or more first charged particle detectors are disposed within the plasma generation chamber. [6] The photolithography system of any one of claims 2 to 5, wherein the control system is configured to analyze the second sensor signals and adjust the plasma generation parameters based at least in part on the first sensor signals and the second sensor signals. [7] Photolithography system according to one of the preceding claims, further comprising: a collector (106) configured to receive extreme ultraviolet radiation from the plasma and to reflect the extreme ultraviolet radiation; a scanner configured to receive the extreme ultraviolet radiation reflected by the collector; and one or more second charged particle detectors disposed within the scanner and configured to detect charged particles within the scanner and generate third sensor signals indicative of the charged particles detected within the scanner. [8] The photolithography system of claim 7, wherein the control system is configured to analyze the third sensor signals and adjust the plasma generation parameters based at least in part on the first sensor signals, the second sensor signals, and the third sensor signals. [9] Photolithography system according to claim 7 or 8, wherein the control system is arranged to generate a model of the plasma based on the first sensor signals, the second sensor signals and the third sensor signals, to analyze the model and to adjust the plasma generation parameters based on the model. [10] The photolithography system of any one of claims 7 to 9, wherein the one or more second charged particle detectors comprise an electron multiplication charge-coupled device. [11] Photolithography system according to one of claims 7 to 10, further comprising: one or more charged particle capture boxes that are part of the scanner or are connected to the scanner; and one or more deflection elements configured to deflect charged particles in the scanner into the one or more charged particle capture boxes, wherein the one or more second charged particle detectors are arranged in the one or more charged particle capture boxes and are configured to detect charged particles deflected by the deflection elements. [12] The photolithography system of claim 11, further comprising an electromagnetic lens disposed in the scanner and configured to guide charged particles deflected by the one or more deflection elements toward the one or more second charged particle detectors. [13] The photolithography system of any preceding claim, further comprising one or more lenses configured to direct extreme ultraviolet radiation toward the one or more light sensors. [14] The photolithography system of claim 13, wherein the one or more light sensors are located outside the plasma generation chamber. [15] A photolithography system according to any preceding claim, wherein the one or more light sensors are charge-coupled devices. [16] A photolithography system according to any preceding claim, wherein the one or more light sensors are arranged to detect laterally scattered radiation from the plasma. [17] The photolithography system of any preceding claim, wherein the control system is configured to analyze the plasma based on Thomson scattering associated with the extreme ultraviolet radiation detected by the one or more light sensors. [18] Photolithography system according to one of the preceding claims, wherein the control system comprises one or more machine learning models. [19] A photolithography system according to any one of the preceding claims, wherein the plasma generation parameters comprise one or more of the following: a timing of the laser; a pulse power of the laser; a pulse profile of the laser; a position of the laser; a speed of the droplets; a size of the droplets; an initial temperature of the droplets; and a trajectory of the droplets. [20] Method comprising: Emitting a stream of droplets (142) from a droplet generator (108); Generating a plasma in a plasma generation chamber (101) by irradiating the droplets with a laser (102); Detecting the velocity, intensity and / or energy of charged particles emitted by the plasma with one or more charged particle detectors; and Adjusting one or more plasma generation parameters with the control system based at least in part on the velocity, intensity, and / or energy of the charged particles detected by the one or more charged particle detectors. [21] The method of claim 20, further comprising: detecting extreme ultraviolet radiation emitted by the plasma with one or more light sensors (126); Adjusting one or more plasma generation parameters with a control system (114) based at least in part on characteristics of the extreme ultraviolet radiation detected by the one or more light sensors. [22] The method of claim 20 or 21, further comprising: Reflecting extreme ultraviolet light from the plasma toward a scanner with a collector in the plasma generation chamber; Performing a photolithography process with the extreme ultraviolet radiation entering the scanner; and Deflecting charged particles entering the scanner from the plasma generation chamber using a magnetic field into a charged particle capture box.

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