Magnetic field leakage cancelation in a charged-particle beam system

Abstract

The disclosed embodiments include a power system that includes a plurality of DC power supplies configured to receive AC power and to provide DC power to components of the charged particle system, a plurality of AC power supplies configured to provide AC power to the plurality of DC power supplies, and a controller comprising circuitry configured to perform operations that includes receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply, determining, based on the determined load characteristic, a value associated with an AC power supply corresponding to said DC power supply, wherein the determined value facilitates reducing a combined magnetic field leakage from the plurality of DC power supplies, and generating a control signal to cause the AC power supply to drive an output with the determined value.

Description

MAGNETIC FIELD LEAKAGE CANCELATION IN A CHARGED-PARTICLE BEAM SYSTEMCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority of US application 63 / 686,103 which was filed on August 22, 2024 and which is incorporated herein in its entirety by reference.TECHNICAL FIELD

[0002] The description herein generally relates to systems and methods to cancel, or reduce the effect of, magnetic field interference in a charged-particle beam apparatus.BACKGROUND

[0003] In manufacturing processes of integrated circuits (ICs), unfinished or finished circuit components are inspected or measured to ensure that features are fabricated according to design and are free of defects. Inspection or metrology systems utilizing charged particle (e.g., electron) beam microscopes, such as a scanning electron microscope (SEM) can be employed. As feature sizes of IC components continue to shrink, determination of the resolution of the particle beam (e.g., electron beam) becomes important, for example, for the accurate determination of feature sizes. Increased resolution makes even the smallest disturbances to the beam path detrimental to performance of charged particle beam instruments, such as SEM.SUMMARY

[0004] Some embodiments of the present disclosure provide a method of reducing a combined magnetic field leakage from a plurality of DC power supplies of a charged particle system. The method may include receiving sensor information, and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a phase for said DC power supply, where the determined phase facilitates reducing the combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause an AC power supply corresponding to said DC power supply to drive an output with the determined phase.

[0005] Some embodiments of the present disclosure provide a method of reducing a combined magnetic field leakage from a plurality of DC power supplies of a charged particle system. The method may include receiving sensor information, and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a value associated with an AC power supply corresponding to said DC power supply, where the determined value facilitates reducing the combined magnetic field leakage from the plurality of DC power supplies; andgenerating a control signal to cause the AC power supply to drive an output with the determined value.

[0006] Some embodiments of the present disclosure provide a power system that may include a plurality of DC power supplies configured to receive AC power and to provide DC power to components of the charged particle system. Power system may also include a plurality of AC power supplies configured to provide AC power to the plurality of DC power supplies. System may furthermore include a controller having circuitry configured to perform: receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a phase for said DC power supply, where the determined phase facilitates reducing a combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause an AC power supply among the plurality of AC power supplies corresponding to said DC power supply to drive an output with the determined phase.

[0007] Some embodiments of the present disclosure provide a power system that may include a plurality of DC power supplies configured to receive AC power and to provide DC power to components of the charged particle system. Power system may also include a plurality of AC power supplies configured to provide AC power to the plurality of DC power supplies. System may furthermore include a controller having circuitry configured to perform: receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a value associated with an AC power supply corresponding to said DC power supply, where the determined value facilitates reducing a combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause the AC power supply to drive an output with the determined value. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0008] Some embodiments of the present disclosure provide a non-transitory computer readable medium that stores a set of instructions that is executable by at least on processor of a computing device to cause the computing device to perform operations of reducing a combined magnetic field leakage from a plurality of DC power supplies of a charged particle system. The operations may include receiving sensor information, and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a phase for said DC power supply, where the determined phase facilitates reducing the combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause an AC power supply corresponding to said DC power supply to drive an output with the determined phase.

[0009] Some embodiments of the present disclosure provide a non-transitory computer readable medium that stores a set of instructions that is executable by at least on processor of a computing device to cause the computing device to perform operations of reducing a combined magnetic field leakage from a plurality of DC power supplies of a charged particle system. The operations may include receiving sensor information, and receiving sensor information. Non - transitory computer readable medium may also include for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a value associated with an AC power supply corresponding to said DC power supply, where the determined value facilitates reducing the combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause the AC power supply to drive an output with the determined value. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0010] Some embodiments of the present disclosure provide a charged particle beam apparatus that may include a charged-particle beam source configured to generate a charged-particle beam for sample scanning. Charged particle beam apparatus may also include a projection optical system configured to project the charged-particle beam towards the sample. Apparatus may furthermore include a wafer stage holding the sample. Apparatus may in addition include a power system having: a plurality of DC power supplies configured to receive AC power and to provide DC power to components of the charged particle system; a plurality of AC power supplies configured to provide AC power to the plurality of DC power supplies; and a controller having circuitry configured to perform: receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a phase for said DC power supply, where the determined phase facilitates reducing a combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause an AC power supply among the plurality of AC power supplies corresponding to said DC power supply to drive an output with the determined phase.

[0011] Some embodiments of the present disclosure provide a charged particle beam apparatus that may include a charged-particle beam source configured to generate a charged-particle beam for sample scanning. Charged particle beam apparatus may also include a projection optical system configured to project the charged-particle beam towards the sample. Apparatus may furthermore include a wafer stage holding the sample. Apparatus may in addition include a power system having: a plurality of DC power supplies configured to receive AC power and to provide DC power to components of the charged particle system; a plurality of AC power supplies configured to provide AC power to the plurality of DC power supplies; and a controller having circuitry configured toperform: receiving sensor information; and for each De power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a value associated with an AC power supply corresponding to said DC power supply, where the determined value facilitates reducing a combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause the AC power supply to drive an output with the determined value.

[0012] Other advantages of the embodiments of the present disclosure will become apparent from the following description taken in conjunction with the accompanying drawings wherein are set forth, by way of illustration and example, certain embodiments of the present invention.BRIEF DESCRIPTION OF FIGURES

[0013] The above and other aspects of the present disclosure will become more apparent from the description of exemplary embodiments, taken in conjunction with the accompanying drawings.

[0014] Fig. 1 is a schematic illustration of an example charged-particle beam system, consistent with some embodiments of the present disclosure.

[0015] Fig. 2A is a schematic illustration of an example multi-beam tool that may be part of the charged-particle beam system of Fig. 1.

[0016] Fig. 2B is a schematic illustration of an example single-beam tool that may be part of the charged-particle beam system of Fig. 1.

[0017] Fig. 3 is a block diagram of a conventional power system.

[0018] Fig. 4 is a block diagram of an example power system for a charged-particle beam tool, consistent with some embodiments of the present disclosure.

[0019] Fig. 5 A is a block diagram of an example inverter of a power system of Fig. 4, consistent with some embodiments of the present disclosure.

[0020] Fig. 5B is a graph showing a pulse width modulated waveform and a corresponding sinusoidal waveform, consistent with some embodiments of the present disclosure.

[0021] Fig. 6A is a block diagram of an example DC power supply of a power system of Fig. 4, consistent with some embodiments of the present disclosure.

[0022] Fig. 6B illustrates an example signal waveform at each step of a DC power supply in Fig. 6A, consistent with some embodiments of the present disclosure.

[0023] Fig. 7 illustrates an example AC power signal and measured current signal of a DC power supply of Fig. 4, consistent with some embodiments of the present disclosure.

[0024] Fig. 8A illustrates two example current signals of two DC power supplies in Fig. 4.

[0025] Fig. 8B illustrates that two example current signals in Fig. 8A are constructive with each other.

[0026] Fig. 8C illustrates that two example current signals in Fig. 8A are destructive with each other with phase shift, consistent with some embodiments of the present disclosure.

[0027] Fig. 9A illustrates two example current signals of Fig. 8A with frequency adjustment, consistent with some embodiments of the present disclosure.

[0028] Fig. 9B illustrates that two example current signals of Fig. 9A are destructive with each other with frequency adjustment and phase shift, consistent with some embodiments of the present disclosure.

[0029] Fig. 10 is a block diagram of an example training system, consistent with some embodiments of the present disclosure.

[0030] Fig. 11 is a flow chart of an example method for reducing magnetic field leakage from DC power supplies, consistent with some embodiments of the present disclosure.DETAILED DESCRIPTION

[0031] Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise represented. The implementations set forth in the following description of exemplary embodiments do not represent all implementations. Instead, they are merely examples of apparatuses and methods consistent with aspects related to the disclosed embodiments as recited in the appended claims. For example, although some embodiments are described in the context of utilizing electron beams, the disclosure is not so limited. Other types of charged-particle beams (e.g., including protons, ions, muons, or any other particle carrying electric charges) may be similarly applied. Furthermore, other imaging systems may be used, such as optical imaging, photon detection, x-ray detection, ion detection, etc.

[0032] Electronic devices are constructed of circuits formed on a piece of semiconductor material called a substrate. The semiconductor material may include, for example, silicon, silicon nitride, gallium arsenide, indium phosphide, silicon germanium, diamond, or the like. Many circuits may be formed together on the same piece of semiconductor material and are called integrated circuits or ICs. The size of these circuits has decreased dramatically so that many more of them can be fit on the substrate. For example, an IC chip in a smartphone can be as small as a thumbnail and yet may include over 2 billion transistors, the size of each transistor being less than 1 / 1000th the size of a human hair.

[0033] Making ICs with extremely small structures or components is a complex, time-consuming, and expensive process, often involving hundreds of individual steps. Errors in even one step have the potential to result in defects in the finished IC, rendering it useless. Thus, the manufacturing process highly relies on the defect inspection and metrology (critical dimensions (CD), edge placement error (EPE), pattern pitch, etc.) so that the fabrication process can be optimized to maximize the number of functional ICs made in the process; that is, to improve the overall yield of the process.

[0034] Defect inspection and metrology of semiconductor circuits can be conducted using a scanning charged particle microscope (“SCPM”). For example, an SCPM may be a scanning electron microscope (SEM). A SCPM can be used to image these extremely small structures, in effect, taking a “picture” of the structures of the wafer. The image can be used to determine if the structure was formed properly in the proper location. If the structure is defective, then the process can be adjusted, so the defect is less likely to recur.

[0035] As the physical sizes of IC components continue to shrink, accuracy and yield in defect detection becomes increasingly important. In the context of a SEM used for inspection in semiconductor fabrication, resolution refers to the microscope’s ability to distinguish fine details in the sample being observed. Resolution is typically described in terms of spatial resolution, which is the smallest distance between two points on a sample's surface that can be distinguished as separate entities in the SEM image. In the context of SEM imaging, fidelity refers to the microscope’s ability to reproduce and display fine details of the sample being observed, with the displayed image being an accurate representation of the sample being observed. The resolution and fidelity of a SEM are critical performance parameters for the semiconductor industry where extremely small features need to be accurately characterized.

[0036] SEMs (and other charged-particle beam systems) are sensitive to magnetic fields since they interact with the electron beam and affect image quality. When there is undesired magnetic field leakage in the system, it adversely affects image quality by shifting the charged-particle beam in both time and space. For example, magnetic field leakage affects charged-particle beams by exerting forces that cause deflection or distortion of their trajectories and thereby impact the performance of the system. Undesired magnetic fields generated by sources located within or near the particle beam instrument may adversely affect image quality by causing unintended shift of the particle beam in space and time. In charged-particle beam systems, such as SEMs, one type of undesired magnetic field sources includes power transformers in the power delivery network. A conventional power delivery network for charged-particle beam systems includes a plurality of linear DC power supplies that provide sub-modules in the system with required DC power. Such linear DC power supplies generally include large transformers that are configured to convert input AC power to the DC power required by loads, e.g., sub-modules. Because the DC power supplies share a common AC power source, magnetic fields leaked from transformers in the DC power supplies are approximately in phase and, resultantly, the magnetic flux of the leaked magnetic fields combines together, causing issues in the system. This magnetic field leakage interacts with electron beams, resulting in degraded SEM image quality. While multiple approaches to prevent magnetic leakage have been considered and subject to experimentation, such as a shielding scheme using high-Mu metal, transformer core shape change, etc., they were not sufficient to reduce magnetic field leakage from DC power supplies to a neglectable level. There is still a need for improvement.

[0037] Embodiments of the present disclosure provide systems and techniques to reduce or minimize the combined magnetic field leakage from DC power supplies of a power delivery system for SEMs and other charged-particle beam tools and systems. In some embodiments, a power delivery system provides an individual AC power supply for each DC power supply. According to some embodiments of the present disclosure, each DC power supply is fed from its own AC power supply with the AC power having a unique phase or frequency. According to some embodiments of the present disclosure, each AC power supply can be controlled to generate AC power with a certain phase or frequency that is determined based on sensor feedback that provides information regarding its corresponding DC power supply, such as the total load driven by the DC supply. In some embodiments, the phase or frequency of AC power to be supplied to each DC power supply is determined to reduce or minimize the combined magnetic field leakage of a plurality of DC power supplies. In some embodiments, the phase or frequency of AC power to be supplied to each DC power supply is determined such that the magnetic field leakage from each of a plurality of DC power supplies combined to cancel out each other. In some embodiments, a guided neural network technology, e.g., edge Al (artificial intelligence) is utilized in analyzing the sensor feedback, in determining phase or frequency of AC power to be supplied to each DC power supply, or in generating control signals for AC power supplies.

[0038] Relative dimensions of components in drawings may be exaggerated for clarity. Within the following description of drawings, the same or like reference numbers refer to the same or like components or entities, and only the differences with respect to the individual embodiments are described. Other objects and advantages of the disclosure may be realized by the elements and combinations as set forth in the embodiments discussed herein. However, embodiments of the present disclosure are not necessarily required to achieve such exemplary objects or advantages, and some embodiments may not achieve any of the stated objects or advantages.

[0039] As used herein, unless specifically stated otherwise, the term “or” encompasses all possible combinations, except where infeasible. For example, if it is stated that a component includes A or B, then, unless specifically stated otherwise or infeasible, the component may include A, or B, or A and B. As a second example, if it is stated that a component includes A, B, or C, then, unless specifically stated otherwise or infeasible, the component may include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C. Expressions such as “at least one of’ do not necessarily modify an entirety of a following list and do not necessarily modify each member of the list, such that “at least one of A, B, and C” should be understood as including only one of A, only one of B, only one of C, or any combination of A, B, and C. The phrase “one of A and B” or “any one of A and B” shall be interpreted in the broadest sense to include one of A, or one of B.

[0040] All relative terms such as “about,” “substantially,” “approximately,” etc., indicate a possible variation of ±10% (unless noted otherwise or another variation is specified). For example, a feature disclosed as being about “t” units long (wide, thick, etc.) may vary in length from (t-O.lt) to (t+0. It)units. Similarly, a width within a range of about 100-150 mm can be any width between (100 - 10%) and (150 + 10%). Further, a range described as varying from, or between, 5 to 10 (5-10), includes the endpoints (i.e., 5 and 10). In some cases, the specification also provides context to some of the relative terms used. Similarly, term “generally” implies a degree of approximation or similarity rather than an exact replication. For example, a structure described as being substantially circular or generally circular, it means that the structure shares similarities with the geometric characteristics of a circle but may not precisely match its form. For example, its shape may deviate slightly (e.g., 10% variation in diameter or curvature at different locations, etc.) from being perfectly circular.

[0041] Fig. 1 illustrates an example electron beam (EB) system 100 consistent with embodiments of the present disclosure. EB system 100 may be used for imaging in any application (metrology, defect detection, etc.). As shown in Fig. 1, EB system 100 includes a main chamber 101, a load / lock chamber 102, a beam tool 104, and an equipment front end module (EFEM) 106. Beam tool 104 is located within main chamber 101. EFEM 106 includes a first loading port 106a and a second loading port 106b. EFEM 106 may include additional loading port(s). First loading port 106a and second loading port 106b receive wafer front opening unified pods (FOUPs) that contain wafers (e.g., semiconductor wafers or wafers made of other material(s)) or samples to be inspected (wafers and samples may be used interchangeably). A “lot” is a plurality of wafers that may be loaded for processing as a batch.

[0042] One or more robotic arms (not shown) in EFEM 106 may transport the wafers to load / lock chamber 102. Load / lock chamber 102 is connected to a load / lock vacuum pump system (not shown) which removes gas molecules in load / lock chamber 102 to reach a first pressure below the atmospheric pressure. After reaching the first pressure, one or more robotic arms (not shown) may transport the wafer from load / lock chamber 102 to main chamber 101. Main chamber 101 is connected to a main chamber vacuum pump system (not shown) which removes gas molecules in main chamber 101 to reach a second pressure below the first pressure. After reaching the second pressure, the wafer is subject to inspection by beam tool 104. Beam tool 104 may be a single-beam system or a multi-beam system.

[0043] A controller 109 is electronically connected to beam tool 104. Controller 109 may be a computer configured to execute various controls of EB system 100. While controller 109 is shown in Fig. 1 as being outside of the structure that includes main chamber 101, load / lock chamber 102, and EFEM 106, it is appreciated that controller 109 may be a part of the structure. In some embodiments, controller 109 may include one or more processors (not shown). A processor may be a generic or specific electronic device capable of manipulating or processing information. For example, the processor may include any combination of any number of a central processing unit (or “CPU”), a graphics processing unit (or “GPU”), an optical processor, a programmable logic controllers, a microcontroller, a microprocessor, a digital signal processor, an intellectual property (IP) core, a Programmable Logic Array (PLA), a Programmable Array Logic (PAL), a Generic Array Logic(GAL), a Complex Programmable Logic Device (CPLD), a Field-Programmable Gate Array (FPGA), a System On Chip (SoC), an Application-Specific Integrated Circuit (ASIC), a neural processing unit (NPU), and any other type of circuit capable of data processing. The processor may also be a virtual processor that includes one or more processors distributed across multiple machines or devices coupled via a network.

[0044] In some embodiments, controller 109 may further include one or more memories (not shown). A memory may be a generic or specific electronic device capable of storing codes and data accessible by the processor (e.g., via a bus). For example, the memory may include any combination of any number of a random-access memory (RAM), a read-only memory (ROM), an optical disc, a magnetic disk, a hard drive, a solid-state drive, a flash drive, a security digital (SD) card, a memory stick, a compact flash (CF) card, or any type of storage device. The codes and data may include an operating system (OS) and one or more application programs (or “apps”) for specific tasks. The memory may also be a virtual memory that includes one or more memories distributed across multiple machines or devices coupled via a network.

[0045] Fig. 2A illustrates a schematic diagram of an example multi-beam beam tool 104A (also referred to herein as apparatus 104A) and an image processing system 290 that may be configured for use in EB system 100 (Fig. 1), consistent with embodiments of the present disclosure. Beam tool 104A comprises a charged particle source 202, a gun aperture 204, a condenser lens 206, a primary charged-particle beam 210 emitted from charged particle source 202, a source conversion unit 212, a plurality of beamlets 214, 216, and 218 of primary charged-particle beam 210, a primary projection optical system 220, a motorized wafer stage 280, a wafer holder 282, multiple secondary charged- particle beams 236, 238, and 240, a secondary optical system 242, and a charged particle detection device 244. Primary projection optical system 220 can comprise a beam separator 222, a deflection scanning unit 226, and an objective lens 228. Charged particle detection device 244 can comprise detection sub-regions 246, 248, and 250.

[0046] Charged particle source 202, gun aperture 204, condenser lens 206, source conversion unit 212, beam separator 222, deflection scanning unit 226, and objective lens 228 can be aligned with a primary optical axis 260 of apparatus 104A. Secondary optical system 242 and charged particle detection device 244 can be aligned with a secondary optical axis 252 of apparatus 104A.

[0047] Charged particle source 202 can emit one or more charged particles, such as electrons, protons, ions, muons, or any other particle carrying electric charges. In some embodiments, charged particle source 202 may be an electron source. For example, charged particle source 202 may include a cathode, an extractor, or an anode, wherein primary electrons can be emitted from the cathode and extracted or accelerated to form primary charged-particle beam 210 (in this case, a primary electron beam) with a crossover (virtual or real) 208. For ease of explanation without causing ambiguity, electrons are used as examples in some of the descriptions herein. However, it should be noted that any charged particle may be used in any embodiment of this disclosure, not limited to electrons.Primary charged-particle beam 210 can be visualized as being emitted from crossover 208. Gun aperture 204 can block off peripheral charged particles of primary charged-particle beam 210 to reduce Coulomb effect. The Coulomb effect may cause an increase in size of probe spots.

[0048] Source conversion unit 212 can comprise an array of image-forming elements and an array of beam-limit apertures. The array of image-forming elements can comprise an array of micro-deflectors or micro-lenses. The array of image-forming elements can form a plurality of parallel images (virtual or real) of crossover 208 with a plurality of beamlets 214, 216, and 218 of primary charged-particle beam 210. The array of beam-limit apertures can limit the plurality of beamlets 214, 216, and 218. While three beamlets 214, 216, and 218 are shown in Fig. 2 A, embodiments of the present disclosure are not so limited. For example, in some embodiments, the apparatus 104 A may be configured to generate a first number of beamlets. In some embodiments, the first number of beamlets may be in a range from 1 to 1000. In some embodiments, the first number of beamlets may be in a range from 200-500.

[0049] Condenser lens 206 can focus primary charged-particle beam 210. The electric currents of beamlets 214, 216, and 218 downstream of source conversion unit 212 can be varied by adjusting the focusing power of condenser lens 206 or by changing the radial sizes of the corresponding beam-limit apertures within the array of beam-limit apertures. Objective lens 228 can focus beamlets 214, 216, and 218 onto a wafer 230 for imaging, and can form a plurality of probe spots 270, 272, and 274 on a surface of wafer 230.

[0050] Beam separator 222 can be a beam separator of Wien filter type generating an electrostatic dipole field and a magnetic dipole field. In some embodiments, if they are applied, the force exerted by the electrostatic dipole field on a charged particle (e.g., an electron) of beamlets 214, 216, and 218 can be substantially equal in magnitude and opposite in a direction to the force exerted on the charged particle by magnetic dipole field. Beamlets 214, 216, and 218 can, therefore, pass straight through beam separator 222 with zero deflection angle. However, the total dispersion of beamlets 214, 216, and 218 generated by beam separator 222 can also be non-zero. Beam separator 222 can separate secondary charged-particle beams 236, 238, and 240 from beamlets 214, 216, and 218 and direct secondary charged-particle beams 236, 238, and 240 towards secondary optical system 242.

[0051] Deflection scanning unit 226 can deflect beamlets 214, 216, and 218 to scan probe spots 270, 272, and 274 over a surface area of wafer 230. In response to the incidence of beamlets 214, 216, and 218 at probe spots 270, 272, and 274, secondary charged-particle beams 236, 238, and 240 may be emitted from wafer 230. Secondary charged-particle beams 236, 238, and 240 may comprise charged particles (e.g., electrons) with a distribution of energies. For example, secondary charged-particle beams 236, 238, and 240 may be secondary electron beams including secondary electrons (energies < 50 eV) and backscattered electrons (energies between 50 eV and landing energies of beamlets 214, 216, and 218). Secondary optical system 242 can focus secondary charged-particle beams 236, 238, and 240 onto detection sub-regions 246, 248, and 250 of charged particle detection device 244.Detection sub-regions 246, 248, and 250 may be configmed to detect corresponding secondary charged-particle beams 236, 238, and 240 and generate corresponding signals (e.g., voltage, current, or the like) used to reconstruct an inspection image of structures on or underneath the surface area of wafer 230.

[0052] The generated signals may represent intensities of secondary charged-particle beams 236, 238, and 240 and may be provided to image processing system 290 that is in communication with charged particle detection device 244, primary projection optical system 220, and motorized wafer stage 280. The movement speed of motorized wafer stage 280 may be synchronized and coordinated with the beam deflections controlled by deflection scanning unit 226, such that the movement of the scan probe spots (e.g., scan probe spots 270, 272, and 274) may orderly cover regions of interests on the wafer 230. The parameters of such synchronization and coordination may be adjusted to adapt to different materials of wafer 230. For example, different materials of wafer 230 may have different resistance-capacitance characteristics that may cause different signal sensitivities to the movement of the scan probe spots.

[0053] The intensity of secondary charged-particle beams 236, 238, and 240 may vary according to the external or internal structure of wafer 230, and thus may indicate whether wafer 230 includes defects. Moreover, as discussed above, beamlets 214, 216, and 218 may be projected onto different locations of the top surface of wafer 230, or different sides of local structures of wafer 230, to generate secondary charged-particle beams 236, 238, and 240 that may have different intensities. Therefore, by mapping the intensity of secondary charged-particle beams 236, 238, and 240 with the areas of wafer 230, image processing system 290 may reconstruct an image that reflects the characteristics of internal or external structures of wafer 230.

[0054] In some embodiments, image processing system 290 may include an image acquirer 292, a storage 294, and a controller 296. Image acquirer 292 may comprise one or more processors. For example, image acquirer 292 may comprise a computer, server, mainframe host, terminals, personal computer, any kind of mobile computing devices, or the like, or a combination thereof. Image acquirer 292 may be communicatively coupled to charged particle detection device 244 of beam tool 104A through a medium such as an electric conductor, optical fiber cable, portable storage media, IR, Bluetooth, internet, wireless network, wireless radio, or a combination thereof. In some embodiments, image acquirer 292 may receive a signal from charged particle detection device 244 and may construct an image. Image acquirer 292 may thus acquire inspection images of wafer 230. Image acquirer 292 may also perform various post-processing functions, such as generating contours, superimposing indicators on an acquired image, or the like. Image acquirer 292 may be configured to perform adjustments of brightness and contrast of acquired images. In some embodiments, storage 294 may be a storage medium such as a hard disk, flash drive, cloud storage, random access memory (RAM), other types of computer-readable memory, or the like. Storage 294 may be coupled with image acquirer 292 and may be used for saving scanned raw image data as original images, and post-processed images. Image acquirer 292 and storage 294 may be connected to controller 296. In some embodiments, image acquirer 292, storage 294, and controller 296 may be integrated together as one control unit.

[0055] In some embodiments, image acquirer 292 may acquire one or more inspection images of a wafer based on an imaging signal received from charged particle detection device 244. An imaging signal may correspond to a scanning operation for conducting charged particle imaging. An acquired image may be a single image comprising a plurality of imaging areas. The single image may be stored in storage 294. The single image may be an original image that may be divided into a plurality of regions. Each of the regions may comprise one imaging area containing a feature of wafer 230. The acquired images may comprise multiple images of a single imaging area of wafer 230 sampled multiple times over a time sequence. The multiple images may be stored in storage 294. In some embodiments, image processing system 290 may be configured to perform image processing steps with the multiple images of the same location of wafer 230.

[0056] When electrons of primary charged-particle beam 210 are projected onto a surface of wafer 230 (e.g., probe spots 270, 272, and 274), the electrons of primary charged-particle beam 210 may penetrate the surface of wafer 230 for a certain depth, interacting with particles of wafer 230. Some electrons of primary charged-particle beam 210 may elastically interact with (e.g., in the form of elastic scattering or collision) the materials of wafer 230 and may be reflected or recoiled out of the surface of wafer 230. An elastic interaction conserves the total kinetic energies of the bodies (e.g., electrons of primary charged-particle beam 210) of the interaction, in which the kinetic energy of the interacting bodies does not convert to other forms of energy (e.g., heat, electromagnetic energy, or the like). Such reflected electrons generated from elastic interaction may be referred to as backscattered electrons (BSEs). Some electrons of primary charged-particle beam 210 may inelastically interact with (e.g., in the form of inelastic scattering or collision) the materials of wafer 230. An inelastic interaction does not conserve the total kinetic energies of the bodies of the interaction, in which some or all of the kinetic energy of the interacting bodies convert to other forms of energy. For example, through the inelastic interaction, the kinetic energy of some electrons of primary charged-particle beam 210 may cause electron excitation and transition of atoms of the materials. Such inelastic interaction may also generate electrons exiting the surface of wafer 230, which may be referred to as secondary electrons (SEs). Yield or emission rates of BSEs and SEs depend on, e.g., the material under inspection and the landing energy of the electrons of primary charged-particle beam 210 landing on the surface of the material, among others. The energy of the electrons of primary charged- particle beam 210 may be imparted in part by its acceleration voltage (e.g., the acceleration voltage between the anode and cathode of charged particle source 202 in Fig. 2A). The quantity of BSEs and SEs may be more or fewer (or even the same) than the injected electrons of primary charged-particle beam 210.

[0057] In some embodiments, image processing system 290 may include measurement circuits (e.g., analog-to-digital converters) to obtain a distribution of the detected secondary charged particles (e.g., secondary electrons). The charged particle distribution data collected during a detection time window, in combination with corresponding scan path data of beamlets 214, 216, and 218 incident on the wafer surface, can be used to reconstruct images of the wafer structures under inspection. The reconstructed images can be used to reveal various features of the internal or external structures of wafer 230, and thereby can be used to reveal any defects that may exist in the wafer.

[0058] Another example of a charged-particle beam apparatus will now be discussed with reference to Fig. 2B. Beam tool 104B (also referred to herein as apparatus 104B) may be an example of beam tool 104 and may be similar to beam tool 104 A shown in Fig. 2A. However, different from apparatus 104A, apparatus 104B may be a single-beam tool that uses only one primary electron beam to scan one location on the wafer at a time. As shown in Fig. 2B, apparatus 104B includes a wafer holder 136 supported by motorized stage 134 to hold a wafer 150 to be inspected. Beam tool 104B includes an electron emitter, which may comprise a cathode 103, an anode 121, and a gun aperture 122. Beam tool 104B further includes a beam limit aperture 125, a condenser lens 126, a column aperture 135, an objective lens assembly 132, and a detector 144. Objective lens assembly 132, in some embodiments, may be a modified SORIL lens, which includes a pole piece 132a, a control electrode 132b, a deflector unit 132c, and an exciting coil 132d. In a detection or imaging process, an electron beam 161 emanating from the tip of cathode 103 may be accelerated by anode 121 voltage, pass through gun aperture 122, beam limit aperture 125, condenser lens 126, and be focused into a probe spot 170 by the modified SORIL lens and impinge onto the surface of wafer 150. Probe spot 170 may be scanned across the surface of wafer 150 by a deflector, such as deflector unit 132c or other deflectors in the SORIL lens. Secondary or scattered particles, such as secondary electrons or scattered primary electrons emanated from the wafer surface may be collected by detector 144 to determine intensity of the beam and so that an image of an area of interest on wafer 150 may be reconstructed.

[0059] There may also be provided an image processing system 199 that includes an image acquirer 120, a storage 130, and controller 109. Image acquirer 120 may comprise one or more processors. For example, image acquirer 120 may comprise a computer, server, mainframe host, terminals, personal computer, any kind of mobile computing devices, and the like, or a combination thereof. Image acquirer 120 may connect with detector 144 of beam tool 104B through a medium such as an electrical conductor, optical fiber cable, portable storage media, IR, Bluetooth, internet, wireless network, wireless radio, or a combination thereof. Image acquirer 120 may receive a signal from detector 144 and may construct an image. Image acquirer 120 may thus acquire images of wafer 150. Image acquirer 120 may also perform various post-processing functions, such as image averaging, generating contours, superimposing indicators on an acquired image, and the like. Image acquirer 120 may be configured to perform adjustments of brightness and contrast, etc. of acquired images. Storage 130 may be a storage medium such as a hard disk, random access memory (RAM), cloud storage,other types of computer readable memory, and the like. Storage 130 may be coupled with image acquirer 120 and may be used for saving scanned raw image data as original images, and postprocessed images. Image acquirer 120 and storage 130 may be connected to controller 109. In some embodiments, image acquirer 120, storage 130, and controller 109 may be integrated together as one electronic control unit.

[0060] In some embodiments, image acquirer 120 may acquire one or more images of a sample based on an imaging signal received from detector 144. An imaging signal may correspond to a scanning operation for conducting charged particle imaging. An acquired image may be a single image comprising a plurality of imaging areas that may contain various features of wafer 150. The single image may be stored in storage 130. Imaging may be performed on the basis of imaging frames.

[0061] The condenser and illumination optics of the electron beam tool may comprise or be supplemented by electromagnetic quadrupole electron lenses. For example, as shown in Fig. 2B, electron beam tool 104B may comprise a first quadrupole lens 148 and a second quadrupole lens 158. In some embodiments, the quadrupole lenses may be used for controlling the electron beam. For example, first quadrupole lens 148 may be controlled to adjust the beam current and second quadrupole lens 158 may be controlled to adjust the beam spot size and beam shape.

[0062] Fig. 2B illustrates a charged-particle beam apparatus that may use a single primary beam configured to generate secondary electrons by interacting with wafer 150. Detector 144 may be placed along optical axis 105, as in the example shown in Fig. 2B. The primary electron beam may be configured to travel along optical axis 105. Accordingly, detector 144 may include a hole at its center so that the primary electron beam may pass through to reach wafer 150. Fig. 2B shows an example of detector 144 having an opening at its center. However, some embodiments may use a detector placed off axis relative to the optical axis along which the primary electron beam travels. For example, as in the example shown in Fig. 2A, discussed above, a beam separator 222 may be provided to direct secondary electron beams toward a detector placed off axis. Beam separator 222 may be configured to divert secondary electron beams toward an electron detection device 244, as shown in Fig. 2A. While Figs. 2A and 2B generally show an electron beam inspection system, the disclosed embodiments can be used by any electron beam system.

[0063] The images generated by SCPM may be used for defect inspection. For example, a generated image capturing a test device region of a wafer may be compared with a reference image capturing the same test device region. The reference image may be predetermined (e.g., by simulation) and include no known defect. If a difference between the generated image and the reference image exceeds a tolerance level, a potential defect may be identified. For another example, the SCPM may scan multiple regions of the wafer, each region including a test device region designed as the same, and generate multiple images capturing those test device regions as manufactured. The multiple imagesmay be compared with each other. If a difference between the multiple images exceeds a tolerance level, a potential defect may be identified.

[0064] In some embodiments, machine learning may be employed in the generation of inspection images, reference images, or other images associated with apparatus 100, 104A, or 104B. For example, in some embodiments, a machine learning system may be operated in association with, e.g., controller 109 or 296, image processing system 199 or 290, image acquirer 120 or 292, or storage unit 130 or 294 of Figs. 1-2B. In some embodiments, machine learning may be employed in analyzing sensor feedbacks, in determining phase or frequency for each DC power supply, or in controlling each AC power supply, e.g., with respect to method 1100 of Fig. 11, in association with, e.g., controller 480 of Fig. 4, which will be described below. In some embodiments, a machine learning system may comprise a discriminative model. In some embodiments, a machine learning system may include a generative model. For example, learning can feature two types of mechanisms: discriminative learning that may be used to create classification and detection algorithms, and generative learning that may be used to actually create models that, in the extreme, can render images. For example, as described further below, a generative model may be configured for generating an image from a design clip that resembles a corresponding location on a wafer in a SEM image. This may be performed by 1) training the generative model with design clips and the associated actual SEM images from those locations on the wafer; and 2) using the model in inference mode to feed the model design clips in locations for which simulated SEM images are desired. Such simulated images can be used as reference images in, e.g., die-to-database inspection.

[0065] If the model(s) include one or more discriminative models, the discriminative model(s) may have any suitable architecture or configuration known in the art. Discriminative models, also called conditional models, are a class of models used in machine learning for modeling the dependence of an unobserved variable “y” on an observed variable “x.” Within a probabilistic framework, this may be done by modeling a conditional probability distribution P(y|x), which can be used for predicting y based on x. Discriminative models, as opposed to generative models, may not allow one to generate samples from the joint distribution of x and y. However, for tasks such as classification and regression that do not require the joint distribution, discriminative models may yield superior performance. On the other hand, generative models are typically more flexible than discriminative models in expressing dependencies in complex learning tasks. In addition, most discriminative models are inherently supervised and cannot easily be extended to unsupervised learning. Application specific details ultimately dictate the suitability of selecting a discriminative versus generative model.

[0066] A generative model can be generally defined as a model that is probabilistic in nature. In other words, a “generative” model is not one that performs forward simulation or rule-based approaches and, as such, it may not be necessary to model the physics of the processes involved in generating an actual image or output (for which a simulated image or output is being generated). Instead, the generative model can be learned (in that its parameters can be learned) based on a suitabletraining set of data. Such generative models may have a number of advantages for the embodiments described herein. In addition, the generative model may be configured to have a deep learning architecture in that the generative model may include multiple layers, which may perform a number of algorithms or transformations. The number of layers included in the generative model may depend on the particular use case. For practical purposes, a suitable range of layers is from 2 layers to a few tens of layers.

[0067] Deep learning is a type of machine learning. Machine learning can be generally defined as a type of artificial intelligence (Al) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data — such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs.

[0068] The machine learning described herein may be further performed as described in “Introduction to Statistical Machine Learning,” by Sugiyama, Morgan Kaufmann, 2016, 534 pages; “Discriminative, Generative, and Imitative Learning,” Jebara, MIT Thesis, 2002, 212 pages; and “Principles of Data Mining (Adaptive Computation and Machine Learning)” Hand et al., MIT Press, 2001, 578 pages; which are incorporated by reference as if fully set forth herein. The embodiments described herein may be further configured as described in these references.

[0069] In some embodiments, a machine learning system may comprise a neural network. For example, a model may be a deep neural network with a set of weights that model the world according to the data that it has been fed to train it. Neural networks can be generally defined as a machine learning approach that is based on a collection of connected artificial neurons, inspired by a biological brain, that learns to solve problems from data. Each neural unit is connected with many others, and links can be enforcing or inhibitory in their effect on the activation state of connected neural units. These systems are self-learning and trained rather than explicitly programmed and excel in areas where the solution or feature detection is difficult to express in a traditional computer program.

[0070] Neural networks typically consist of multiple layers, and the signal path traverses from front to back. The goal of the neural network is to solve problems in the same way that the human brain would, although several neural networks are much more abstract. Modem neural network projects typically work with a few thousand to a few million neural units and millions of connections. The neural network may have any suitable architecture or configuration known in the art.

[0071] In some embodiments, a model may comprise convolutional and deconvolution neural network. For example, the embodiments described herein can take advantage of learning concepts such as a convolution and deconvolution neural network to solve the normally intractable representation conversion problem (e.g., rendering). The model may have any convolution and deconvolution neural network configuration or architecture known in the art.

[0072] Fig. 3 is a block diagram of a conventional power system 300 that can supply power to a complex machine, such as a charged-particle beam tool (e.g., electron beam (EB) system 100). As shown in Fig. 3, power system 300 generally comprises an uninterruptable power supply (UPS) unit 310 and a power distribution unit 320. UPS unit 310 provides backup power, protecting the system from damage in the event of power loss. As shown in Fig. 3, UPS unit 310 may be configured to comprise AC to DC converter 330, battery 340, and inverter 350. Inverter 350 is also called an AC power supply in the present disclosure. Power from the utility or other power grid is fed to UPS unit 310, and UPS unit 310 can store power in battery 340 after converting AC power to DC power via AC to DC converter 330. Power from UPS unit 310 can be distributed to various sub-modules within a charged-particle tool via power distribution unit 320. In Fig. 3, sub-modules are indicated as loads 1 to 4. Before power is delivered from UPS unit 310 to power distribution unit 320, DC power from battery 340 is converted to AC power via inverter 350. AC power converted from inverter 350 is supplied to multiple DC power supplies, such as 361 to 364 (collectively “DC power supplies 360”). In some examples, power system 300 can comprise connecting board 370 thru which power can be routed from inverter 350 to the multiple power supplies 361 to 364. Because a complex machine, such as a charged-particle beam tool, may comprise various sub-modules that have various power requirements, power system 300 generally comprises multiple DC power supplies, such as 361-364. Generally, DC power supplies 360 are configured to convert AC power from inverter 350 to DC power that meets a certain power requirement of a corresponding sub-module, represented as loads 1 to 4. As shown in Fig. 3, power system 300 can also comprise controller 380 that controls the operation of AC to DC converter 330 and inverter 350. Controller 380 can also monitor a status of battery 340, such as its charging level or speed. Controller 380 can take into account monitored results when controlling operations of AC to DC converter 330 and inverter 350.

[0073] Examples of types of DC power supplies (e.g., that can be used for AC to DC 330, 360) include a linear DC power supply and a switching DC power supply. In some systems that are sensitive to power ripple noise, a linear DC power supply can be used. A linear DC power supply can include a transformer to step down the voltage from a first AC power voltage to a lower voltage. The linear DC power supply can then use a series of rectifier circuits and a filtering process to generate a stable DC voltage to be supplied to, for example, a corresponding sub-module (e.g., loads 1-4). In power system 300, DC power supplies 361 to 364 can be linear DC power supplies and may be arranged closely or lined up within the common space, and thus their magnetic leakage can combine and impact the performance of the charged-particle beam tool. For example, magnetic leakage from DC power supplies 361-364 can additively combine and can affect electron beams, resulting in degraded SEM image quality. Because the plurality of DC power supplies 361-364 share a common AC power source (e.g., from inverter 350), magnetic fields leaked from transformers in the plurality of DC power supplies 361-364 are approximately in phase and the magnetic flux of the leaked magnetic fields can be additively combined, causing issues in the system due to the combinedmagnetic field leakages. The magnetic field leakage will interact with electron beams, resulting in degraded SEM image quality. While multiple approaches to prevent or reduce magnetic leakage have been considered and subject to experimentation, such as a shielding scheme using high-Mu metal, transformer core shape change, etc., they were not sufficient to reduce the combined magnetic field leakages from the DC power supplies to a neglectable level. There is still a need for improvement.

[0074] Fig. 4 is a block diagram of an example power system for a charged-particle beam tool, consistent with some embodiments of the present disclosure. As shown in Fig. 4, power system 400 can comprise UPS unit 410 and power distribution unit 420. Power system 400 can comprise inverters 451 to 454 (collectively “inverters 450”) in UPS unit 410, and DC power supplies 461 to 464 (collectively “DC power supplies 460”) in power distribution unit 420. In this disclosure, inverters 450 can be configured to convert DC power inputs to AC power outputs. Similar to conventional power system 300, UPS unit 410 of power system 400 can comprise AC to DC converter 430 and battery 440. AC to DC converter 430 can be configured to receive AC power from the power grid or another external power source and to convert AC power to DC power. Battery 440 is configured to store power from the power grid after AC to DC converter 430 and to supply power to sub-modules, which are indicated as loads 1 to 4 in Fig. 4.

[0075] In some embodiments, inverters 450 can comprise a plurality of inverters 451 to 454 that correspond to a plurality of power supplies 461 to 464. Inverters 450 can be configured to convert DC power to AC power at a desired frequency to be eventually consumed by loads 1-4, respectively. For example, the desired frequency can be 60Hz, 50Hz, etc. In some embodiments, inverters 450 do not change a voltage level when converting DC power to AC power such that the peak voltage of AC output power from inverters 450 is at the same voltage level as the DC input power to inverters 450. As shown in Fig. 4, power system 400 can comprise connecting board 470 that provides power line interconnect from battery 440 to each of inverters 451 to 454. According to some embodiments of the present disclosure, a plurality of inverters 451 to 454 can be individually controlled to have different phase or frequency from each other, which will be discussed in reference to controller 480.

[0076] Fig. 5A is a block diagram of an example inverter (e.g., inverters 450) of power system 400 of Fig. 4, consistent with some embodiments of the present disclosure. As shown in Fig. 5A, inverters 450 can comprise switching device 510 and fdter 520. Switching device 510 can be configured to use a pulse width modulation (PWM) method to switch on and off the DC input signal within a certain period of time. In Fig. 5B, the various widths of each pulse 571 each represent a different voltage level. In Fig. 5B, the X-axis represents time and the Y-axis represents a magnitude of a signal. For example, the Y-axis can represent a voltage level of the AC output power of inverters 450 with respect to waveform 572. In some embodiments, switching device 510 turns on and off the DC input signal with pulses 571 having various widths that, combined, correspond to output waveform 572. In some embodiments, a wider pulse represents higher voltage and a narrower pulse represents lower voltage.

[0077] Referring back to Fig. 5A, filter 520 can be configured to generate a continuous (e.g., sinusoidal) waveform 572 based on pulses 571 by calculating voltage levels corresponding to various widths of pulses 571. In some embodiments, filter 520 can be implemented as an LC-low pass filter. As shown in Fig. 5B, continuous waveform 572 that is generated based on pulses 571 can be AC output power of inverters 450. In some embodiments, inverters 450 can further comprise a power transformer that can step down or up the voltage level of waveform 572 to be used by loads. In some embodiments where DC power inputs to inverters 450 are not stable or constant over time, the power transformer combined with control circuits can step down or up the output voltage to provide a constant output to its loads. In some embodiments, inverters 450 can further comprise one or more additional filters to assure power quality and harmonics reduction. In some embodiments, additional filters can comprise a low pass filter, LC -filter, C-filter, LC -tuned filter, CLC -filter, etc.

[0078] In some embodiments, inverters 450 can be configured to change the output frequency as needed. For example, inverters 450 can adjust a frequency of AC output power by increasing or reducing the time period of one cycle. For example, Fig. 5B shows one cycle of pulse width modulated waveform 571 and sinusoidal waveform 572. When the period becomes longer, the frequency of AC output power will go down, and when the period becomes shorter, the frequency of AC output power will go up. For example, 60Hz AC signal has a period of 16.67ms (1 / 60Hz) and 50Hz AC signal has a period of 20ms (1 / 50Hz). Therefore, by controlling the time period of one cycle for generating pulse width modulated waveform 571, a frequency of AC output power from inverters 450 can be controlled. It will be also noted that a phase of an AC output power from inverters 450 can also be controlled by delaying the start of generation of pulse width modulated waveform 571. According to some embodiments of the present disclosure, each inverter 451 to 454 provides its output AC power to its corresponding DC power supply 461 to 464. According to some embodiments of the present disclosure, phase or frequency of inverters 451 to 454 can be adjusted independently. Phase or frequency of each inverter 451 to 454 can be determined and controlled by controller 480, which will be described later. While PWM methods have been described in controlling inverters 450, it will be appreciated that inverters 450 can be controlled with any other methods that allow individual control of the phase or frequency of each of inverters 450.

[0079] Referring back to Fig. 4, DC power supplies 460 can comprise a plurality of power supplies 461 to 464. As shown in Fig. 4, each power supply 461 to 464 is configured to receive AC power from its corresponding inverter 451 to 454 and to provide DC power to its corresponding load 1 to 4, i.e., sub-module. As shown in Fig. 6A, DC power supplies 460 can comprise transformer 610 and rectifier 620. Transformer 610 is configured to take in an AC signal, which is an output of inverters 450, and to generate a stepped up or stepped down or same voltage AC signal. In some embodiments, power supplies 461 to 464 can be linear DC power supplies.

[0080] Fig. 6B illustrates an example signal waveform at each step of a DC power supply in Fig. 6A, consistent with some embodiments of the present disclosure. Graph 651 represents a waveform of ACinput signal and graph 652 represents a waveform of a transformed signal of AC input signal via transformer 610. As shown by graphs 651 and 652, transformer 610 can step down AC input power to a certain level to drive a corresponding load. In some embodiments, transformer 610 can use a transformer core and a coil wound on the transformer and is configured to convert or transform incoming voltage into a desirable outgoing voltage. In transformer 610, electricity flows through a primary coil creating a magnetic flux. As the magnetic field cuts through the secondary coil, the secondary coil picks up the voltage. The power is stepped-up or down in relation to the amount of turns of each winding. During the transformation, magnetic fields may leak from the core, which will cause interferences in the SEM. Such magnetic field leakage is an undesirable byproduct of a transformer.

[0081] Referring back to Fig. 6A, the transformed signal is converted to a DC signal by rectifier 620. As shown in Fig. 6B, graph 653 shows a waveform of the rectified signal, which is maintained positive, i.e., equal to and above magnitude zero (0) in the graph. This confirms that the rectified signal flows consistently in one direction rather than alternating between two directions over time. Referring back to Fig. 6A, power supplies 460 can further comprise filter 630 and regulator 640. Filter 630 can be configured to smoothen the rectified signal by smoothing out the ups and downs, as shown in graph 654. Regulator 640 can be configured to convert the filtered signal to a desired DC voltage, as shown in graph 655, and to avoid fluctuations that can result from driving the loads.

[0082] Fig. 7 illustrates an example AC power signal and measured current signal of a DC power supply of Fig. 4, consistent with some embodiments of the present disclosure. In Fig. 7, input AC signal 710 to DC power supplies 460 and an output current signal 711 from DC power supplies 460 are compared. Because the loading that is driven by the output power of DC power supplies 460 is not uniform over time, output current signal 711 of DC power supplies 460 may not have a matching waveform to input AC signal 710. While output current signal 711 of DC power supplies 460 has a distorted waveform compared to input AC signal 710, it is noted that output current signal 711 has a frequency and phase similar to those of input AC signal 710. Similarly, it will be further understood that magnetic field leakage from transformer 610 has a frequency and phase similar to those of input AC signal 710 because the magnetic field is proportional to current flowing through transformer 610.

[0083] As the SEM resolution becomes finer and charged-particle beams become smaller, interference caused by magnetic field leakage from transformer 610 cannot be ignored and becomes problematic. In particular, the interference and issues can be aggravated when DC power supplies 460 comprise a plurality of DC power supplies 461 to 464. For example, when a plurality of DC power supplies 461-464 receive AC input signals having the same frequency and phase, magnetic field leakages from DC power supplies 461-464 also have the substantially same frequency and phase with each other. Resultantly, magnetic field leakage from DC power supplies 461-464 can be additively combined and amplified, and the resultant fields will interact with the electron beams of the SEM and will degrade any resultant SEM images.

[0084] Fig. 8A is a graph illustrating two current signals 810 and 820 of two different DC power supplies, e.g., DC power supplies 461 and 462. For example, due to different loadings for the two different DC power supplies, two current signals 810 and 820 can have different waveforms. But the two current signals 810 and 820 can be at a similar frequency and phase with each other. Fig. 8B illustrates a graph showing two current signals 810 and 820 overlapped with each other. In Fig. 8B, the X-axis represents time and the Y-axis represents a current magnitude. As indicated by a vertical dotted line in Fig. 8B, peaks of two current signals 810 and 820 tend to have the same directions, rather than the opposite directions. Thus, two current signals 810 and 820 having a similar frequency and phase are constructive with each other. Similarly, it is understood that magnetic field leakage corresponding to two current signals 810 and 820 are also constructive with each other, and thus the total magnetic field leakage becomes bigger.

[0085] Fig. 8C illustrates a graph showing two current signals 810 and 820 overlapped with each other with second current signal 820 being phase shifted. As shown in Fig. 8C, second current signal 820 is phase shifted by delaying second current signal 820 by time delay Td. As indicated by a vertical dotted line in Fig. 8C, peaks of two current signals 810 and 820, after phase shifting second current signal 820, tend to have the opposite direction rather than the same direction. Thus, two current signals 810 and 820 having a similar frequency but with different phases can be destructive. Similarly, it is understood that magnetic field leakage corresponding to two current signals 810 and 820 can also be destructive with each other and thus most of the magnetic field leakage can be canceled out with a selected phase shift as shown in Fig. 8C. The total magnetic field leakage can be resultantly reduced.

[0086] In some embodiments, a frequency of an AC input signal can be adjusted to cancel out magnetic field leakage from multiple DC power supplies 461 to 464. Fig. 9A is a graph illustrating two current signals 910 and 920, which are the same as two current signals 810 and 820 of Fig. 8A but with different frequencies. For example, first current signal 910 can be obtained by adjusting the frequency of first current signal 810, e.g., from 60Hz to 70Hz. Similarly, second current signal 920 can be obtained by adjusting frequency of second current signal 820, e.g., from 60Hz to 50Hz. According to some embodiments of the present disclosure, a frequency of any one or two of the two current signals 810 and 820 can be adjusted such that two current signals 910 and 920 have waveforms that can cancel out each other when they are combined.

[0087] Fig. 9B illustrates that two example current signals of Fig. 9A are destructive with each other (i.e., the signals cancel each other out e.g. by being subtractively combined) with frequency adjustment and phase shift, consistent with some embodiments of the present disclosure. As indicated by a vertical dotted line in Fig. 9B, the peaks of two current signals 910 and 920, after frequency adjustment, tend to have the opposite direction rather than the same direction. Thus, the two current signals 910 and 920 can cancel each other out more by adjusting their frequency and phase. Similarly, it is understood that the magnetic field leakage corresponding to two current signals 910 and 920 arealso more destructive with each other and thus most of the magnetic field leakage can be canceled out. The total magnetic field leakage can be reduced with second current signal 920 being phase shifted. As shown in Fig. 9B, second current signal 920 is phase shifted by delaying second current signal 920 by time delay Tmd.

[0088] While the two current signals 810 and 820 have the similar frequency and phase in the above example in Fig. 8A, making the two current signals 810 and 820 completely out of phase (e.g., 180 degree) may not effectively cancel the two current signals 810 and 820 due to loading change or difference. Therefore, as described above, adjusting the phase or frequency of any one or two of the two current signals 810 and 820 can effectively cancel out the two signals, and accordingly also cancel out the magnetic field leakage. While magnetic field leakage cancelation for two DC power supplies has been described, it will be appreciated that the same or a similar principle can be utilized to cancel magnetic field leakage from any number (> 2) of DC power supplies. While magnetic field leakage cancelation has been described referring to phase or frequency adjustment of current signals flowing through DC power supplies, it will be understood that phase or frequency of current signals flowing through DC power supplies can be adjusted by adjusting phase or frequency of AC output power of corresponding inverters, which can be achieved by a control signal from controller 480.

[0089] Referring back to Fig. 4, power system 400 can further comprise controller 480 that can generate a control signal to drive inverters 450 such that magnetic field leakage from DC power supplies 460 can be reduced or minimized. According to some embodiments of the present disclosure, controller 480 is configured to determine a phase or frequency of each AC power to be supplied to each DC power supply 461-464 that can reduce or minimize total magnetic field leakage from the plurality of DC power supplies 461-464. In some embodiments of the present disclosure, controller 480 can generate a control signal for each inverter 451-454 to produce AC power having the determined phase or frequency for a corresponding DC power supply 461-464. According to some embodiments of the present disclosure, controller 480 can generate a control signal for each inverter 451-454 such that each inverter 451-454 can be individually controlled to produce AC power having the determined phase or frequency for corresponding DC power supply 461-464. According to some embodiments of the present disclosure, each inverter 451-454 can be individually controlled to produce AC power based on its own phase or frequency to minimize total magnetic field leakage in DC power supplies 460.

[0090] According to some embodiments of the present disclosure, controller 480 can receive feedback signals 481 providing information related to DC power supplies 461-464, such as data related to the load 1-4 on the output of the power supplies 461-464. In some embodiments, feedback signals 481 can be sensor information. According to some embodiments of the present disclosure, power system 400 can further comprise a load sensor 490 in distribution unit 420. In some embodiments, load sensor 490 can collect load value information for each of DC power supply 461- 464. In some embodiments, load sensor 490 can measure a current flowing through DC power supply461-464. Because a current flowing through DC power supply 461-464 varies depending on a load value to be driven by DC power supply 461-464, load sensor 490 can comprise a current sensor that is configured to measure a current value flowing through each DC power supply 461-464. In some embodiments, controller 480 can receive load value information from load sensor 490 for DC power supply 461-464. In some embodiments, controller 480 can determine phase or frequency for each DC power supply 461-464 based on the received load value information such that total magnetic field leakage from a plurality of DC power supplies 461-464 can be reduced or minimized. In some embodiments where four DC power supplies 461-464 are employed, controller 480 can receive load value information from each of DC power supplies 461-464. In some embodiments, controller 480 can take into account the four load value information in determining phase or frequency of each AC power to be supplied to each DC power supply 461-464 such that total combined magnetic field can be minimized or reduced. In some embodiments, feedback signals 481 can include load value information collected by load sensor 490 for each DC power supply 461-464. While some embodiments employing four DC power supplies have been described, it will be appreciated that the present disclosure can also be applicable to embodiments employing any number of DC power supplies more than one.

[0091] According to some embodiments of the present disclosure, power system 400 can further comprise magnetic field sensor 491 in distribution unit 420. In some embodiments, magnetic field sensor 491 is configured to collect magnetic field strength information for each of DC power supplies 461-464. In some embodiments, magnetic field sensor 491 can sense magnetic field strength in the vicinity of each of DC power supplies 461-464. In some embodiments, magnetic field sensor 491 can be a 3 -axis magnetometer that may detect the magnetic field interferences along each axis (X, Y, and Z) in proximity to transformer 610 within each of the DC power supplies 461-464. Based on the magnetic fields measured by magnetic field sensor 491, controller 480 can determine the phase or frequency of each AC power supply of 451-454 to be supplied to the corresponding DC power supply of 461-464 such that the combined magnetic field leakage from a plurality of DC power supplies 461- 464 can be reduced or minimized.

[0092] In some embodiments, magnetic field sensor 491 can comprise N number of magnetic field sensors 491, where N is any integer number. In some embodiments, controller 480 can receive N number of magnetic field strength information for each axis, e.g., X(l, . . ., N), Y(l, . . ., N), and Z (1, . . ., N). In some embodiments, controller 480 can take into account the received magnetic field strength information for each axis along with M number of information related to load value in determining the phase or frequency for each DC power supply of 461-464 such that the combined magnetic field for all axes can be minimized or reduced. In some embodiments, feedback signals 481 can include magnetic field information collected by load sensor 490 for each DC power supply 461- 464. In some embodiments numbers N and M can be the same. It is understood that the magnetic field leakage in each axis will also correlate with a current flowing through the corresponding DCpower supply of 461-464, and thus an analysis to reduce or minimize total magnetic field for each axis can be performed in a similar way to that described referring to Figs. 7, 8A-C, and 9A-B. While Fig. 4 illustrates load sensor 490 and magnetic field sensor 491 positioned outside DC power supplies 461-464 for simplicity, it is noted that load sensor 490 and magnetic field sensor 491 can be positioned within any or all of DC power supplies 461-464.

[0093] According to some embodiments of the present disclosure, controller 480 can consider relative positions of DC power supplies 461-464 in a three-dimensional space when determining phase or frequency for each DC power supply 461-464 such that the combined magnetic field leakage from DC power supplies 461-464 can be reduced or minimized. Because the DC power supplies 461- 464 are not positioned in one location but are positioned in different locations in a three-dimensional space, a spatial distance between DC power supplies 461-464 can also be considered in determining phase or frequency.

[0094] According to some embodiments of the present disclosure, controller 480 can evaluate based on an analysis of one or more inspection images, taken while a selected set of phases or frequencies are supplied to DC power supplies 461-464, whether the combined total magnetic field leakage that results from those selected set of phases or frequencies is reduced or minimized. In some embodiments, the combined magnetic field leakage can be measured based on a noise analysis of an inspection image that is taken under a certain condition, e.g., a selected phase or frequency for each DC power supply of 461-464. In some embodiments, an inspection image can be a SCPM image of a sample or a wafer. In some embodiments, an inspection image can be an inspection image generated by, e.g., EBI system 100 of FIG. 1 or electron beam tool 104A of Fig. 2A or 104B of Fig. 2B. In some embodiments, an inspection image can be an image of structures in the region of interest of the wafer. In some embodiments, an inspection image for measuring total magnetic field leakage can be an image of structures having a test pattern, which facilitates measurement of magnetic field leakage. In some embodiments, an inspection image can be a line scan image in any direction. In this example, magnetic field leakage can be determined based on noise on the line scan image when the noise is caused by magnetic field leakage. For example, it can be determined how much an electron beam for the line scan image has deviated from an intended route along with its frequency, by measuring an offset of the line scan image from a hypothetical straight line along the line. It will be also understood that combined total magnetic field leakage affecting the electron beam can be determined based on the determined deviation of the electron beam.

[0095] According to some embodiments of the present disclosure, controller 480 can consider the total magnetic leakage amount when it generates a control signal to each of inverters 451-454. For example, controller 480 can determine whether the applied phase shift or frequency adjustment of AC power generated by inverters 451-454 has reduced the combined total magnetic field leakage from DC power supplies 461-464. If it has not reduced total magnetic field leakage, controller 480 mayrevert back the phase shift or frequency adjustment or change its phase shift or frequency adjustment accordingly.

[0096] According to some embodiments of the present disclosure, controller 480 of Fig. 4 can be implemented as a machine learning model, which can be trained by a training system before being applied to controller 480. Fig. 10 is a block diagram of a training system 1000 (also referred to as “apparatus 1000”) for an inference model, consistent with embodiments of the present disclosure. In some embodiments, training system 1000 may comprise one or more processors and memories. It is appreciated that in various embodiments training system 1000 may be part of or may be separate from a charged-particle beam inspection system (e.g., EBI system 100 of Fig. 1). It is also appreciated that training system 1000 may include one or more components or modules separate from and communicatively coupled to the charged-particle beam inspection system. In some embodiments, training system 1000 may include one or more components (e.g., software modules) that can be implemented in controller 109 or 296, or system 290 or 199, etc. as discussed herein. In some embodiments, training system 1000 and controller 480 are implemented on separate computing devices or on a same computing device. In some embodiments, training system 1000 may be part of controller 380 of Fig. 3 or 480 of Fig. 4. As shown in Fig. 10, training system 1000 may comprise a model trainer 1020 that is configured to train inference model 1021.

[0097] According to some embodiments of the present disclosure, model trainer 1020 can receive training load value data 1011 as training data. Training load value data 1011 can be data collected by load sensor 490 for each DC power supply of 461-464 for a certain time period. In some embodiments, model trainer 1020 can receive training magnetic field data 1012 as training data. Similarly, training magnetic field data 1012 can be data collected by magnetic field sensor 491 for each DC power supply of 461-464 for a certain time period.

[0098] According to some embodiments of the present disclosure, model trainer 1020 is configured to train inference model 1021 to predict the phase or frequency of each AC power to be supplied to each DC power supply of 461-464 that can minimize or reduce the combined total magnetic field leakage from DC power supplies 461-464. According to some embodiments of the present disclosure, model trainer 1020 is configured to train inference model 1021 under supervised learning. Under supervised learning, model trainer 1020 can determine whether the inferenced phase or frequency for each of the DC power supplies 461-464 matches the predetermined optimum phase or frequency. If the inference is not correct or sufficiently accurate, model trainer 1020 can modify or update its parameters accordingly. According to some embodiments of the present disclosure, model trainer 1020 is configured to train inference model 1021 under unsupervised learning. Under unsupervised learning, model trainer 1020 can determine whether the inferenced phase or frequency for each of DC power supplies 461-464 reduces or minimizes the combined total magnetic field leakage, e.g., based on analysis of an inspection image. If the inference is not correct or sufficiently accurate, model trainer 1020 can modify or update its parameters accordingly.

[0099] According to some embodiments of the present disclosure, model trainer 1020 can train inference model 1021 using a plurality of sets of training load value data 1011 and training magnetic field data 1012. The objective of model trainer 1020 is to predict the phase or frequency for each of the DC power supplies 461-464 that can reduce or minimize the combined total magnetic field leakage. According to some embodiments of the present disclosure, trained inference model 1021 can be implemented as controller 380 or 480, among others, to determine a phase or frequency for each of the DC power supplies 461-464 given the data related to characteristics of loads 1-4 from load sensor 490 or magnetic field information from magnetic field sensor 491. Controller 480 can, in turn, generate a control signal based on the determination for each of inverters 451-454 to produce AC power with the determined phase or frequency.

[0100] According to some embodiments of the present disclosure, inference model 1021 can also perform inference with real time data, e.g., from load sensor 490 or magnetic field sensor 491, which can be used to analyze how the power loading or other load characteristics will change with different SEM conditions or operating modes. Accordingly, controller 480 or any other component implementing inference model 1021 can determine the phase or frequency for each DC power supply by predicting a load or load characteristic change under a certain SEM condition or operating mode. According to some embodiments of the present disclosure, a component such as controller 480 can predict in real time using inference model 1021 the phase or frequency of each AC power to be supplied to each DC power supply based on real time feedback from load sensor 490 or magnetic field sensor 491. According to some embodiments, optimization or selection of phase or frequency is based on real time data feedback and the combined total magnetic field leakage can be dynamically controlled.

[0101] Fig. 11 is a flow chart of an example method for reducing a combined total magnetic field leakage from multiple DC power supplies, consistent with some embodiments of the present disclosure. The steps of method 1100 can be performed by controller 480 of Fig. 4, among other components. It is appreciated that the illustrated method 1100 can be altered to modify the order of steps and to include additional steps.

[0102] At step 1110, sensor information is received. In some embodiments, sensor information can include data related to loads (e.g., loads 1-4) collected by load sensor 490 for each DC power supply of 461 to 464. In some embodiments, the sensor information can include magnetic field information collected by magnetic field sensor 491 for each DC power supply of 461-464.

[0103] At step 1120, based on the sensor information, such as a characteristic related to any or all of loads 1-4 driven by DC power supplies 461-464, can be determined. In some embodiments, a characteristic, such as a load value, can be determined based on an amount of current flowing through DC power supply 461-464. In some embodiments, sensor information can include an amount of current flowing through DC power supply 461-464, and the characteristic of the load can be determined based on the sensor information.

[0104] At step 1130, based on the determined load characteristic, at least one of phase and frequency for each DC power supply of 461-464 can be determined. In some embodiments, a phase can be determined to reduce the combined total magnetic field leakage of the DC power supplies. In some embodiments, a frequency for each DC power supply can be determined to reduce the combined magnetic field leakage from the DC power supplies.

[0105] At step 1140, for each DC power supply 461-464, one or more control signals to drive an AC power supply with the determined at least one of the phase and the frequency can be generated. In some embodiments, a control signal(s) to drive an AC power supply with the determined phase or frequency can be generated to reduce magnetic field leakage. In some embodiments, each DC power supply 461-464 may have its own AC power supply 451-454, and each AC power supply may have its own control signal(s).

[0106] The figures may illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer hardware or software products according to various exemplary embodiments of the present disclosure. For example, each block or step in process 1100 may represent certain arithmetical or logical operation that may be implemented using hardware such as an electronic circuit. The steps may also represent a module, segment, or portion of code that comprises one or more executable instructions for implementing the specified logical functions. It should be understood that in some alternative implementations, functions indicated in a step may occur out of the order noted in the figures. For example, two steps shown in succession may be executed or implemented substantially concurrently, or two steps may sometimes be executed in reverse order, depending upon the functionality involved. Further, some steps may also be omitted. It should also be understood that each step of the flow chart of Fig. 11, and combination of the steps, may be implemented by special purpose hardware-based systems that perform the specified functions or acts, or by combinations of special purpose hardware and computer instructions.

[0107] The embodiments may be further described using the following clauses:1. A method of reducing a combined magnetic field leakage from a plurality of DC power supplies of a charged particle system, comprising: receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a phase for said DC power supply, wherein the determined phase facilitates reducing the combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause an AC power supply corresponding to said DC power supply to drive an output with the determined phase.2. The method of clause 1, wherein the combined magnetic field leakage is determined based on an inspection image.3. The method of clause 1 or 2, wherein each DC power supply of the plurality of DC power supplies has a corresponding AC power supply, each providing AC power to a corresponding DC power supply among the plurality of DC power supplies.4. The method of any one of clauses 1 to 3, wherein the sensor information comprises current information.5. The method of any one of clauses 1 to 4, wherein the sensor information further comprises magnetic field strength information for the plurality of DC power supplies.6. The method of clause 5, wherein determining, based on the determined load characteristic, the phase for said DC power supply comprises: determining, based on the determined load characteristic and the magnetic field strength information, a phase for said DC power supply.7. The method of any one of clauses 1 to 6, wherein determining, based on the determined load characteristic, the phase for said DC power supply comprises: determining, based on the determined load characteristic, a phase or frequency for said DC power supply.8. The method of clause 7, wherein generating the control signal comprises: generating a control signal to cause the AC power supply corresponding to said DC power supply to drive an output with the determined phase or frequency.9. The method of any one of clauses 1 to 8, wherein determining, based on the determined load characteristic, the phase for said DC power supply comprises: determining, based on the determined load characteristic, a phase for said DC power supply using a machine learning model.10. A method of reducing a combined magnetic field leakage from a plurality of DC power supplies of a charged particle system, comprising: receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a value associated with an AC power supply corresponding to said DC power supply, wherein the determined value facilitates reducing the combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause the AC power supply to drive an output with the determined value.11. The method of clause 10, wherein the combined magnetic field leakage is determined based on an inspection image.12. The method of clause 10 or 11, wherein each DC power supply of the plurality of DC power supplies has a corresponding AC power supply, each providing AC power to a corresponding DC power supply among the plurality of DC power supplies.13. The method of any one of clauses 10 to 12, wherein the sensor information comprises current information.14. The method of any one of clauses 10 to 13, wherein the sensor information further comprises magnetic field strength information for the plurality of DC power supplies.15. The method of clause 14, wherein determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply comprises: determining, based on the determined load characteristic and the magnetic field strength information, the value associated with the AC power supply corresponding to said DC power supply.16. The method of any one of clauses 10 to 15, wherein determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply comprises: determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply using a machine learning model.17. The method of any one of clauses 10 to 16, wherein the value comprises at least one of phase and frequency.18. A power system of a charged particle system, comprising: a plurality of DC power supplies configured to receive AC power and to provide DC power to components of the charged particle system; a plurality of AC power supplies configured to provide AC power to the plurality of DC power supplies; and a controller comprising circuitry configured to perform: receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a phase for said DC power supply, wherein the determined phase facilitates reducing a combined magnetic field leakage from the plurality of DC power supplies; andgenerating a control signal to cause an AC power supply among the plurality of AC power supplies corresponding to said DC power supply to drive an output with the determined phase.19. The power system of clause 18, wherein the combined magnetic field leakage is determined based on an inspection image.20. The power system of clause 18 or 19, wherein the plurality of AC power supplies are arranged such that each AC power supply provides AC power to a corresponding DC power supply among the plurality of DC power supplies.21. The power system of any one of clauses 18 to 20, wherein the sensor information comprises current information.22. The power system of any one of clauses 18 to 21, wherein the sensor information further comprises magnetic field strength information for the plurality of DC power supplies.23. The power system of clause 22, wherein, in determining, based on the determined load characteristic, the phase for said DC power supply, the controller further comprises circuitry configured to perform: determining, based on the determined load characteristic and the magnetic field strength information, a phase for said DC power supply.24. The power system of any one of clauses 18 to 23, wherein, in determining, based on the determined characteristic, the phase for said DC power supply, the controller further comprises circuitry configured to perform: determining, based on the determined load characteristic, a phase or frequency for said DC power supply.25. The power system of clause 24, wherein, in generating the control signal, the controller further comprises circuitry configured to perform: generating a control signal to cause the AC power supply corresponding to said DC power supply to drive an output with the determined phase or frequency.26. The power system of any one of clauses 18 to 25, wherein, determining, based on the determined load characteristic, the phase for said DC power supply, the controller further comprises circuitry configured to perform: determining, based on the determined load characteristic, a phase for said DC power supply using a machine learning model.27. A power system of a charged particle system, comprising: a plurality of DC power supplies configured to receive AC power and to provide DC power to components of the charged particle system; a plurality of AC power supplies configured to provide AC power to the plurality of DC power supplies; and a controller comprising circuitry configured to perform:receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a value associated with an AC power supply corresponding to said DC power supply, wherein the determined value facilitates reducing a combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause the AC power supply to drive an output with the determined value.28. The power system of clause 27, wherein the combined magnetic field leakage is determined based on an inspection image.29. The power system of clause 27 or 28, wherein the plurality of AC power supplies are arranged such that each AC power supply provides AC power to a corresponding DC power supply among the plurality of DC power supplies.30. The power system of any one of clauses 27 to 29, wherein the sensor information comprises current information.31. The power system of any one of clauses 27 to 30, wherein the sensor information further comprises magnetic field strength information for the plurality of DC power supplies.32. The power system of clause 31, wherein, in determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply, the controller further comprises circuitry configured to perform: determining, based on the determined load characteristic and the magnetic field strength information, the value associated with the AC power supply corresponding to said DC power supply.33. The power system of any one of clauses 27 to 32, wherein, in determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply, the controller further comprises circuitry configured to perform: determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply using a machine learning model.34. The system of any one of clauses 27 to 33, wherein the value comprises at least one of phase and frequency.35. A non-transitory computer readable medium that stores a set of instructions that is executable by at least on processor of a computing device to cause the computing device to perform operations of reducing a combined magnetic field leakage from a plurality of DC power supplies of a charged particle system, the operations comprising: receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a phase for said DC power supply, wherein the determined phase facilitates reducing the combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause an AC power supply corresponding to said DC power supply to drive an output with the determined phase.36. The computer readable medium of clause 35, wherein the combined magnetic field leakage is determined based on an inspection image.37. The computer readable medium of clause 35 or 36, wherein each DC power supply of the plurality of DC power supplies has a corresponding AC power supply, each providing AC power to a corresponding DC power supply among the plurality of DC power supplies.38. The computer readable medium of any one of clauses 35 to 37, wherein the sensor information comprises current information.39. The computer readable medium of any one of clauses 35 to 38, wherein the sensor information further comprises magnetic field strength information for the plurality of DC power supplies.40. The computer readable medium of clause 39, wherein, in determining, based on the determined load characteristic, the phase for said DC power supply, the set of instructions that is executable by at least one processor of the computing device cause the computing device to further perform: determining, based on the determined load characteristic and the magnetic field strength information, a phase for said DC power supply.41. The computer readable medium of any one of clauses 35 to 40, wherein, in determining, based on the determined load characteristic, the phase for said DC power supply, the set of instructions that is executable by at least one processor of the computing device cause the computing device to further perform: determining, based on the determined load characteristic, a phase or frequency for said DC power supply.42. The computer readable medium of clause 41, wherein, in generating the control signal, the set of instructions that is executable by at least one processor of the computing device cause the computing device to further perform: generating a control signal to cause the AC power supply corresponding to said DC power supply to drive an output with the determined phase or frequency.43. The computer readable medium of any one of clauses 35 to 42, wherein, in determining, based on the determined load characteristic, the phase for said DC power supply, the set of instructions that is executable by at least one processor of the computing device cause the computing device to further perform: determining, based on the determined load characteristic, a phase for said DC power supply using a machine learning model.44. A non-transitory computer readable medium that stores a set of instructions that is executable by at least on processor of a computing device to cause the computing device to perform operations for reducing a combined magnetic field leakage from a plurality of DC power supplies of a charged particle system, the operations comprising: receiving sensor information; for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a value associated with an AC power supply corresponding to said DC power supply, wherein the determined value facilitates reducing the combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause the AC power supply to drive an output with the determined value.45. The computer readable medium of clause 44, wherein the combined magnetic field leakage is determined based on an inspection image.46. The computer readable medium of clause 44 or 45, wherein each DC power supply of the plurality of DC power supplies has a corresponding AC power supply, each providing AC power to a corresponding DC power supply among the plurality of DC power supplies.47. The computer readable medium of any one of clauses 44 to 46, wherein the sensor information comprises current information.48. The computer readable medium of any one of clauses 44 to 47, wherein the sensor information further comprises magnetic field strength information for the plurality of DC power supplies.49. The computer readable medium of clause 48, wherein, in determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply, the set of instructions that is executable by at least one processor of the computing device cause the computing device to further perform: determining, based on the determined load characteristic and the magnetic field strength information, the value associated with the AC power supply corresponding to said DC power supply.50. The computer readable medium of any one of clauses 44 to 49, wherein, in determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply, the set of instructions that is executable by at least one processor of the computing device cause the computing device to further perform: determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply using a machine learning model.51. The computer readable medium of any one of clauses 44 to 50, wherein the value comprises at least one of phase and frequency.52. A charged particle beam apparatus comprising: a charged-particle beam source configured to generate a charged-particle beam for sample scanning; a projection optical system configured to project the charged-particle beam towards the sample; a wafer stage holding the sample; and a power system comprising: a plurality of DC power supplies configured to receive AC power and to provide DC power to components of the charged particle system; a plurality of AC power supplies configured to provide AC power to the plurality of DC power supplies; and a controller comprising circuitry configured to perform: receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a phase for said DC power supply, wherein the determined phase facilitates reducing a combined magnetic field leakage from the plurality of DC power supplies; andgenerating a control signal to cause an AC power supply among the plurality of AC power supplies corresponding to said DC power supply to drive an output with the determined phase.53. The charged particle beam apparatus of clause 52, wherein the combined magnetic field leakage is determined based on an inspection image.54. The charged particle beam apparatus of clause 52 or 53, wherein the plurality of AC power supplies are arranged such that each AC power supply provides AC power to a corresponding DC power supply among the plurality of DC power supplies.55. The charged particle beam apparatus of any one of clauses 52 to 54, wherein the sensor information comprises current information.56. The charged particle beam apparatus of any one of clauses 52 to 55, wherein the sensor information further comprises magnetic field strength information for the plurality of DC power supplies.57. The charged particle beam apparatus of clause 56, wherein, in determining, based on the determined load characteristic, the phase for said DC power supply, the controller further comprises circuitry configured to perform: determining, based on the determined load characteristic and the magnetic field strength information, a phase for said DC power supply.

[0108] It will be appreciated that the embodiments of the present disclosure are not limited to the exact construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes may be made without departing from the scope thereof. The present disclosure has been described in connection with various embodiments, other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

Claims

CLAIMS1. A power system of a charged particle system, comprising: a plurality of DC power supplies configured to receive AC power and to provide DC power to components of the charged particle system; a plurality of AC power supplies configured to provide AC power to the plurality of DC power supplies; and a controller comprising circuitry configured to perform operations comprising: receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a value associated with an AC power supply corresponding to said DC power supply, wherein the determined value facilitates reducing a combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause the AC power supply to drive an output with the determined value.

2. The power system of claim 1, wherein the combined magnetic field leakage is determined based on an inspection image.

3. The power system of claim 1, wherein the plurality of AC power supplies are arranged such that each AC power supply provides AC power to a corresponding DC power supply among the plurality of DC power supplies.

4. The power system of claim 1, wherein the sensor information comprises current information.

5. The power system of claim 1, wherein the sensor information further comprises magnetic field strength information for the plurality of DC power supplies.

6. The power system of claim 5, wherein, in determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply, the controller further comprises circuitry configured to perform operations comprising: determining, based on the determined load characteristic and the magnetic field strength information, the value associated with the AC power supply corresponding to said DC power supply.

7. The power system of claim 1, wherein, in determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply, the controller further comprises circuitry configured to perform operations comprising: determining, based on the determined load characteristic, the value associated with the AC power supply corresponding to said DC power supply using a machine learning model.

8. The power system of claim 1, wherein the value comprises at least one of phase and frequency.

9. A non-transitory computer readable medium that stores a set of instructions that is executable by at least on processor of a computing device to cause the computing device to perform operations of reducing a combined magnetic field leakage from a plurality of DC power supplies of a charged particle system, the operations comprising: receiving sensor information; and for each DC power supply of the plurality of DC power supplies: determining, based on the sensor information, a load characteristic for said DC power supply; determining, based on the determined load characteristic, a phase for said DC power supply, wherein the determined phase facilitates reducing the combined magnetic field leakage from the plurality of DC power supplies; and generating a control signal to cause an AC power supply corresponding to said DC power supply to drive an output with the determined phase.

10. The computer readable medium of claim 9, wherein the combined magnetic field leakage is determined based on an inspection image.

11. The computer readable medium of claim 9, wherein each DC power supply of the plurality of DC power supplies has a corresponding AC power supply, each providing AC power to a corresponding DC power supply among the plurality of DC power supplies.

12. The computer readable medium of claim 9, wherein the sensor information comprises current information; or magnetic field strength information for the plurality of DC power supplies.

13. The computer readable medium of claim 12, wherein, in determining, based on the determined load characteristic, the phase for said DC power supply, the set of instructions that is executable by at least one processor of the computing device cause the computing device to further perform operations comprising:determining, based on the determined load characteristic and the magnetic field strength information, a phase for said DC power supply.

14. The computer readable medium of claim 9, wherein, in determining, based on the determined load characteristic, the phase for said DC power supply, the set of instructions that is executable by at least one processor of the computing device cause the computing device to further perform operations comprising: determining, based on the determined load characteristic, a phase or frequency for said DC power supply; or determining, based on the determined load characteristic, a phase for said DC power supply using a machine learning model.

15. The computer readable medium of claim 14, wherein, in generating the control signal, the set of instructions that is executable by at least one processor of the computing device cause the computing device to further perform: generating a control signal to cause the AC power supply corresponding to said DC power supply to drive an output with the determined phase or frequency.

Images