Multi-Level RF Pulse Monitoring and RF Pulsing Parameter Optimization in a Manufacturing System

Abstract

A method and system are provided for multi-level RF pulse monitoring and RF pulsing parameter optimization in a manufacturing system. A radio frequency (RF) signal is pulsed in a process chamber according to a set of RF pulsing parameters. Sensor data indicative of a multi-level RF pulse waveform detected in the process chamber based on the RF signal pulsing is received from one or more sensors. One or more peaks are identified in the detected multi-level RF pulse waveform. Each identified peak corresponds to at least one RF signal pulse of the RF signal pulsing in the process chamber. A determination is made whether the detected multi-level RF pulse waveform corresponds to a target multi-level RF pulse waveform based on the identified one or more peaks. An indication of whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform is provided to a client device.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to manufacturing systems, and more particularly to multilevel RF pulse monitoring and pulse parameter optimization in manufacturing systems.

Background Art

[0002] A substrate process (e.g., an etching process, a deposition process, etc.) can be performed for a substrate in a processing chamber of a manufacturing system. Each substrate process performed in the processing chamber can be performed according to a specific process recipe. In some cases, one or more operations of the process recipe can include pulsing one or more radio frequency (RF) signals in the processing chamber (e.g., via an RF generator within the processing chamber or an RF generator coupled to the processing chamber). For example, a user of the manufacturing system (e.g., an operator, a technician, etc.) can provide one or more RF pulsing parameters (e.g., a pulsed duty cycle parameter, a pulsed bias shift parameter, etc.) via a client device connected to the manufacturing system, and one or more RF generators in the manufacturing system can pulse an RF signal in the processing chamber according to the one or more provided RF pulsing parameters. It can be difficult for a user of the manufacturing system to tune the RF pulsing parameters to cause a target multilevel RF pulse waveform to be generated in the processing chamber according to a substrate process recipe.

Summary of the Invention

[0003] The following is a simplified summary of the present disclosure to provide a basic understanding of some aspects of the present disclosure. This summary of the invention is not an extensive overview of the present disclosure. It is neither intended to identify key or critical elements of the present disclosure nor to delineate any scope of the specific implementations of the present disclosure or any scope of the claims. Its sole purpose is to present some concepts of the present disclosure in a simplified form as a prelude to the more detailed description presented later.

[0004] In one aspect of the present disclosure, a method includes pulsing a radio frequency (RF) signal in a processing chamber of a manufacturing system according to a set of RF pulsing parameters. The set of RF pulsing parameters corresponds to a target multi-level RF pulse waveform that is to be generated in the processing chamber based on RF signal pulsing by one or more RF generators associated with the processing chamber. The method further includes receiving sensor data from one or more sensors associated with the processing chamber, the sensor data indicating a multi-level RF pulse waveform detected in the processing chamber based on RF signal pulsing. The method further includes identifying one or more peaks in the detected multi-level RF pulse waveform. Each of the identified one or more peaks corresponds to at least one RF signal pulse of the RF signal pulsing in the processing chamber. The method further includes determining, based on the identified one or more peaks, whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform. The method further includes providing an indication to a client device connected to the manufacturing system as to whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform.

[0005] In another aspect of the present disclosure, a non-transitory machine-readable storage medium stores instructions that, when executed, cause a processing device to pulse an RF signal within a processing chamber of a manufacturing system according to a set of radio frequency (RF) pulsing parameters. The set of RF pulsing parameters corresponds to a target multi-level RF pulse waveform that is to be generated within the processing chamber based on RF signal pulsing by one or more RF generators associated with the processing chamber. The processing device is further configured to receive sensor data from one or more sensors associated with the processing chamber indicating a multi-level RF pulse waveform detected within the processing chamber based on RF signal pulsing. The processing device is further configured to identify one or more peaks in the detected multi-level RF pulse waveform. Each of the identified one or more peaks corresponds to at least one RF signal pulse of the RF signal pulsing within the processing chamber. The processing device is further configured to determine, based on the identified one or more peaks, whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform. The processing device is further configured to provide an indication to a client device connected to the manufacturing system as to whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform.

[0006] In another aspect of the present disclosure, the system includes a memory and a processing device coupled to the memory. The processing device is for pulsing an RF signal within a processing chamber of a manufacturing system according to a set of radio frequency (RF) pulsing parameters. The set of RF pulsing parameters corresponds to a target multi-level RF pulse waveform to be generated within the processing chamber based on RF signal pulsing by one or more RF generators associated with the processing chamber. The processing device is further for receiving sensor data from one or more sensors associated with the processing chamber indicating a multi-level RF pulse waveform detected within the processing chamber based on RF signal pulsing. The processing device is further for identifying one or more peaks in the detected multi-level RF pulse waveform. Each of the identified one or more peaks corresponds to at least one RF signal pulse of the RF signal pulsing within the processing chamber. The processing device is further for determining, based on the identified one or more peaks, whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform. The processing device is further for providing an indication to a client device connected to the manufacturing system as to whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform.

[0007] The present disclosure is illustrated in the figures of the accompanying drawings by way of example and not limitation. Note that different references to "an" or "one" embodiment in the present disclosure are not necessarily to the same embodiment, and such references are meant to mean at least one.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5A - B

Figure 5C

Figure 6

Figure 7

[0009] The embodiments disclosed herein include methods and systems for multi-level RF pulse monitoring and RF pulsing parameter optimization in a manufacturing system. The manufacturing system can include manufacturing equipment configured to perform a process on a substrate (referred to herein as a substrate process). The substrate can include a wafer, semiconductor, display, etc. The substrate process can include a deposition process (e.g., a chemical vapor deposition (CVD) process, an atomic layer deposition (ALD) process, a physical vapor deposition (PVD) process, etc.) for depositing a thin film on the surface of the substrate. In other or similar embodiments, the substrate process can include an etching process for etching away portions of the material deposited on the surface of the substrate to create a target pattern. The substrate processing equipment of the manufacturing system (e.g., a processing chamber, etc.) can process the substrate by performing one or more operations of a process recipe (e.g., a deposition process recipe, an etching process recipe, etc.) associated with a particular substrate process. The process recipe can include a plurality of recipe operations such as transfer operations (e.g., a robot transporting the substrate to different locations), processing operations (e.g., processing the substrate in a processing chamber), cleaning operations (e.g., cleaning the processing chamber after a processing operation), etc.

[0010] In some substrate processes, a fluid is flowed into a processing chamber in a gaseous state, and radio frequency (RF) energy is applied to the fluid to change the state of the fluid from a gaseous state to a plasma state (e.g., called striking a plasma) for recipe operations. The applied RF energy is referred to herein as an RF signal. When the fluid changes from a gaseous state to a plasma state, there is dissociation of the fluid's molecules, which increases the pressure in the processing chamber. In some instances, the RF signal is applied as a series of pulses. In some instances, pulsing the RF signal includes generating the RF signal and oscillating the RF signal at a particular frequency. In some instances, the pulsed RF signal emits RF energy at varying levels (e.g., a higher energy level followed by a lower or zero energy level). A sensor in the processing chamber (e.g., an optical frequency sensor (OFS)), or a sensor coupled to the processing chamber, can detect the pulse of the RF signal as a waveform. The waveform refers to the change in frequency detected within the processing chamber based on the RF pulsing over time. For example, one or more RF pulse generators can generate an RF pulsed waveform within the processing chamber.

[0011] In a conventional system, a single-level RF pulse is provided within a processing chamber. Such a single-level RF pulse is detectable as a single-level pulsed waveform. A single-level pulsed waveform refers to a waveform that is detectable (e.g., by a sensor in the processing chamber) for an RF signal oscillated at a single energy level within an RF pulsing period. The single-level pulsed waveform of the conventional method has a single frequency and a single duty cycle (in other words, the ratio of the time that the circuit related to providing energy for the RF signal is on to the time that the circuit is off) associated with the RF signal provided in the chamber. The gas or plasma within the processing chamber reacts to the RF signal pulsing, and thus, the processing chamber sensor can detect the single-level pulsed waveform. The detected waveform indicates the reaction of the gas or plasma to the RF signal pulsing.

[0012] Conventionally, only single-level pulses with a single duty cycle have been used in process operations due to the ease of measuring and controlling such pulses. The pulses of the conventional system or method are square pulses, which means that the RF signal oscillates between a baseline frequency (in other words, when the circuit related to providing energy for the RF signal is off) and a single energy level (e.g., single-level). (In other words, the waveform detected for such a pulse indicates a square shape.) A multi-level pulse (e.g., multi-level) refers to an RF signal that oscillates between multiple frequency levels within a single RF pulsing period. For example, a single-level pulse modifies the RF energy in the chamber to a single level for each oscillation, while a multi-level pulse can modify the RF energy to multiple different levels for each oscillation.

[0013] As indicated above, due to the difficulty of monitoring and controlling such multilevel pulsing, conventional systems implement only single-level pulsing and do not perform the more complex operations enabled by multilevel pulsing. The methods and techniques used to monitor and control single-level pulses cannot be used to monitor and control multilevel pulses. Ever more complex waveforms can be used to result in ever more complex and smaller scale products (e.g., substrates, wafers, semiconductors, etc.), but this cannot be achieved using single-level RF pulsing. Conventional methods for monitoring and controlling pulsed waveforms are insufficient for measuring multilevel waveforms, so multilevel RF pulsing cannot be implemented in conventional systems for manufacturing such ever smaller and complex products.

[0014] The methods and systems disclosed herein are directed to multi-level RF pulse monitoring and pulse parameter optimization in a manufacturing system. A controller for a manufacturing system (referred to herein as a system controller) can cause one or more RF generators to pulse an RF signal within a processing chamber of the manufacturing system. The RF generator can pulse the RF signal according to a set of RF pulse parameters. In some embodiments, the RF generator generates a pulsed RF signal waveform based on RF pulse parameters that are detectable within the processing chamber, as described above. The RF pulse parameters can correspond to and / or include a pulsed duty cycle parameter, a pulsed power parameter, a pulsed frequency parameter, a pulsed amplitude parameter, a pulsed drop parameter, a pulsed bias shift parameter, and / or a pulsed time shift (e.g., delay) parameter. The RF pulse parameters can correspond to a target multi-level RF pulse waveform that is to be generated within the processing chamber for a recipe operation. In some embodiments, the pulse parameters are provided by a user (e.g., an operator, a technician, etc.) of the manufacturing system via a client device connected to the manufacturing system. In other or similar embodiments, one or more process recipe operations related to a substrate process in the processing chamber can specify the pulse parameters. In some embodiments, the pulse parameters are specified and provided to the RF generator, which causes the RF generator to generate an RF signal for generating a target response of a gas or plasma within the processing chamber corresponding to a target level or waveform. For example, one or more frequencies of the RF signal can be provided to the RF generator based on a target response of a fluid (e.g., a gas or plasma) within the processing chamber during a process operation. Such frequencies can correspond to a target waveform that is to be detected within the chamber.

[0015] As indicated above, the RF generator can generate and pulse an RF signal according to RF pulsing parameters. Each generated RF signal can contribute to a multi-level pulsed RF signal waveform that is detectable within the process chamber (e.g., by an optical frequency sensor (OFS)). In some embodiments, multiple RF generators can each generate an RF pulse signal that can be different from the RF pulse signals generated by other RF generators. Such RF pulse signals can contribute to a multi-level RF pulse waveform that is detectable within the processing chamber (e.g., by an OFS). For example, each RF signal generated by each RF generator can excite (e.g., strike a plasma) the fluid within the process chamber from a gaseous state to a plasma state according to a distinct frequency level and one or more duty cycles associated with each RF pulse signal. One or more sensors in the chamber (e.g., an OFS), or one or more sensors coupled to the chamber, can detect an optical response of the plasma based on the multiple RF signals. The optical response can correspond to the multi-level RF pulse waveform within the chamber.

[0016] The system controller can receive sensor data indicating a multi-level RF pulse waveform from one or more sensors that monitor the processing chamber. The system controller can identify one or more peaks in the multi-level RF pulse waveform based on the received sensor data. Each identified peak in the multi-level RF pulse waveform can correspond to at least one RF signal pulse of RF signal pulsing within the processing chamber. In some embodiments, the system controller can identify peaks in the multi-level RF pulse waveform by identifying one or more regions of the detected multi-level RF pulse waveform associated with RF signal spikes. In some embodiments, the RF signal spike corresponds to a pulse of the RF signal within the processing chamber between a first state and a second state. In some embodiments, the first state is an elevated energy level and the second state is a lower energy level or vice versa. As indicated above, each of the identified regions corresponds to a respective peak of the detected multi-level RF pulse waveform. In some embodiments, the transition area of the RF signal from the first state to the second state corresponds to each respective peak of the detected multi-level RF pulse. In additional or alternative embodiments, the system controller can use one or more machine learning models to identify regions of the multi-level RF pulse waveform that include peaks, as described in more detail herein.

[0017] Based on one or more peaks identified in the detected multi-level RF pulse waveform, the system controller can determine whether the detected multi-level RF pulse waveform corresponds to a target multi-level RF pulse waveform. For example, the system controller can determine that the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform if one or more peaks identified in the detected multi-level RF pulse waveform (e.g., the magnitude of the peak, the position of the peak, etc.) sufficiently match (e.g., approximately match) the corresponding peaks of the target multi-level RF pulse waveform (e.g., within a predetermined threshold). The system controller can send an indication to a client device connected to the manufacturing system as to whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform. For example, if the system controller determines that the detected waveform matches (or approximately matches) the target multi-level RF pulse waveform (e.g., within a range of a predetermined threshold), the system controller can send an indication to the user indicating that to a client device connected to the manufacturing system. The system controller can also, in some embodiments, send data related to one or more peaks identified in the detected multi-level RF pulse waveform.

[0018] In some embodiments, a user of the manufacturing system (e.g., an operator, a technician, etc.) can update (e.g., tune) the RF pulsing parameters based on data associated with one or more peaks identified in the detected multi-level RF pulse waveform. For example, the user can update the RF pulsing parameters provided to generate the detected multi-level RF pulse waveform to increase or decrease the level of RF pulsing for a future substrate process to be performed in the processing chamber. The RF signal generated based on the updated RF pulsing parameters can contribute to an updated multi-level RF pulse waveform (in other words, the multi-level RF pulse waveform detected in the processing chamber as described above). In one exemplary instance, the system controller can determine that the updated multi-level RF pulse waveform more closely corresponds to a target multi-level RF pulse waveform, according to the embodiments described previously. Thus, the user can tune the RF pulsing parameters for the substrate process to optimize the manufacturing process recipe. Optimizing the process recipe can lead to the substrate being processed according to the target state.

[0019] Implementations of the present disclosure address the above-described deficiencies of the current art by providing techniques for monitoring multi-level RF pulse waveforms and enabling a user (e.g., an operator, a technician, etc.) to optimize the RF pulsing parameters of a manufacturing system. Embodiments of the present disclosure provide techniques for accurately measuring complex RF waveforms detected within a processing chamber, including multi-level RF pulse waveforms. More accurate measurement of such waveforms enables the use of increasingly complex waveforms (e.g., multi-level RF pulse waveforms) as part of the process operation, which enables the fabrication of increasingly smaller and more complex products. Additionally, embodiments of the present disclosure enable a user (e.g., an operator, a technician) to optimize and / or adjust the RF pulsing parameters provided to one or more RF generators to generate a multi-level RF signal. Accordingly, the user can optimize and / or adjust the RF signal to more closely match the response of a fluid (e.g., a gas or a plasma) within the processing chamber to a target response, resulting in more accurate and precise manufacturing of the product. Thus, embodiments of the present disclosure can enable the manufacture of products (e.g., wafers, substrates, semiconductors, etc.) having increased accuracy, precision, consistency, and / or quality. Manufacturing products having increased accuracy, precision, and / or quality can lead to higher efficiency and throughput of the manufacturing system and a reduction in overall latency. Accordingly, the system controller consumes fewer computing resources (e.g., processing cycles, memory space, etc.) to perform substrate processing in the manufacturing system, which improves overall efficiency and reduces the overall latency of the computing system associated with the manufacturing system.

[0020] FIG. 1 is a block diagram illustrating an exemplary system architecture according to an aspect of the present disclosure. In some embodiments, computer system architecture 100 may be included as part of a manufacturing system for processing substrates. Computer system architecture 100 includes a client device 120, a system controller device 121 (e.g., a controller, a server), manufacturing equipment 124, measurement equipment 128, a prediction server 112 (e.g., for generating prediction data, for providing model adaptation, for using a knowledge base, etc.), and a data store 140. The prediction server 112 may be part of a prediction system 110. The prediction system 110 may further include server machines 170 and 180. The manufacturing equipment 124 may include a sensor 126 configured to capture data about the substrate being processed by the manufacturing system. In some embodiments, the manufacturing equipment 124 and the sensor 126 may be part of a sensor system that includes a sensor server (e.g., a field service server (FSS) in a manufacturing facility) and a sensor identifier reader (e.g., a front opening unified pod (FOUP) radio frequency identification (RFID) reader for a sensor system). In some embodiments, the measurement equipment 128 may be part of a measurement system that includes a measurement server (e.g., a measurement database, a measurement folder, etc.) and a measurement identifier reader (e.g., a FOUP RFID reader for a measurement system).

[0021] Manufacturing machine 124 produces products by following a recipe or by operating over a certain time period. The manufacturing machine 124 may include one or more sensors 126 configured to generate data about substrates during a substrate process (referred to as sensor data). The sensor data may include one or more values such as temperature (e.g., heater temperature), interval (SP), pressure, high frequency radio frequency (HFRF), voltage of an electrostatic chuck (ESC), current, flow, power, voltage, optical frequency data, waveform data (e.g., multi-level RF pulse waveform data). The sensor data may be related to or indicative of manufacturing parameters such as hardware parameters of the manufacturing machine 124's settings or components (e.g., size, type, etc.) or process parameters of the manufacturing machine 124. The sensor data may indicate a waveform (e.g., multi-level RF pulse waveform) within the processing chamber of the manufacturing machine 124. In some embodiments, the sensor data may be provided while the manufacturing machine 124 is performing a manufacturing process (e.g., instrument readings when processing a product), or in additional or alternative embodiments, the sensor data may be provided after the manufacturing machine 124 has completed a manufacturing process.

[0022] The metrology instrument 128 provides metrology data related to a substrate (e.g., a wafer, etc.) processed by the manufacturing instrument 124. In some embodiments, the metrology instrument 128 may be included in or connected to one or more components of the manufacturing instrument 124. Thus, in some embodiments, the metrology instrument 128 can generate metrology data related to the substrate without the substrate being removed from the vacuum environment of the manufacturing instrument 124. In other or similar embodiments, the metrology instrument 128 may be an external instrument to the manufacturing instrument 124. In such embodiments, the substrate can be measured in the metrology instrument 128 before the substrate is introduced into the vacuum environment and / or after the substrate is removed from the vacuum environment of the manufacturing instrument 124. The metrology data generated by the metrology instrument 128 may include one or more values such as film property data (e.g., wafer spatial film properties), dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects, etc. In some embodiments, the metrology data may further include values of one or more surface profile property data (e.g., etching rate, etching rate uniformity, critical dimensions of one or more features included on the surface of the substrate, critical dimension uniformity across the surface of the substrate, edge placement error, etc.). The metrology data can be of a finished or semi-finished product. The metrology data can be different for each substrate. In some embodiments, the metrology instrument 128 can generate metrology data for the substrate before and / or after processing. In some embodiments, the metrology data can be used to train one or more machine learning models and / or to generate training data for detecting the positions of one or more peaks related to multi-level RF pulse waveforms detected within the processing chamber.

[0023] The client device 120 includes computing devices such as a personal computer (PC), laptop, mobile phone, smartphone, tablet computer, netbook computer, network-connected television (“smart TV”), network-connected media player (e.g., Blu-ray player), set-top box, over-the-top (OTT) streaming device, operator box, etc. In some embodiments, measurement data may be received from the client device 120. In some embodiments, the client device 120 displays a graphical user interface (GUI), where the GUI enables the user to provide, as input, measurement values measured for a substrate processed in the manufacturing system. In other or similar embodiments, the client device 120 can display another GUI that enables the user to provide, as input, an indication of the type of substrate to be processed in the manufacturing system, the type of process to be performed on the substrate, and / or the type of equipment in the manufacturing system. The client device includes one or more processing engines. For example, the client device 120 may include the RF signal engine 122 described in FIG. 3.

[0024] The system controller device 121 (e.g., controller, server) includes one or more computing devices such as a rack-mounted server, router computer, server computer, personal computer, mainframe computer, laptop computer, tablet computer, desktop computer, graphics processing unit (GPU), application-specific integrated circuit (ASIC) accelerator (e.g., tensor processing unit (TPU)). In some embodiments, the system controller device 121 includes the RF signal engine 122 described in FIG. 3. In some embodiments, the system controller device 121 is a controller of a semiconductor processing system and is used to control the manufacturing equipment 124.

[0025] The data store 140 can be a memory (e.g., random access memory), a drive (e.g., hard drive, flash drive), a database system, or another type of component or device capable of storing data. The data store 140 can include multiple storage components (e.g., multiple drives or multiple databases) that can span multiple computing devices (e.g., multiple server computers). The data store 140 can store data related to processing substrates in the manufacturing equipment 124. For example, the data store 140 can store data collected by the sensor 126 in the manufacturing equipment 124 before, during, or after a substrate process (referred to as process data). The process data can refer to historical process data (e.g., process data generated for previous substrates processed in the manufacturing system), and / or current process data (e.g., process data generated for the current substrate being processed in the manufacturing system). The current process data can be the data for which prediction data is generated. In some embodiments, the data store can store measurement data including historical measurement data (e.g., measurement readings for previous substrates processed in the manufacturing system).

[0026] In some embodiments, the data store 140 can store data related to multi-level RF pulse waveforms. For example, the data store 140 can store RF signal peak data including RF pulse data, RF signal spike data, RF pulse parameter data, and RF signal peak position data. In some embodiments, the data store 140 can store historical data related to historical multi-level RF pulse waveforms. For example, the data store 140 can store historical RF signal peak data including historical sensor data, historical RF pulse data, historical RF signal spike data, historical RF pulse parameter data, and historical RF signal peak position data.

[0027] The data store 140 can also store context data related to one or more substrates processed in the manufacturing system. The context data can include, for example, recipe names, recipe operation numbers, preventive maintenance indicators, operators, and the like.

[0028] In some embodiments, the data store 140 can be configured to store data that is not accessible to the users of the manufacturing system. For example, process data, spectral data, non-spectral data, and / or location data obtained for substrates being processed in the manufacturing system may not be accessible to the users of the manufacturing system. In some embodiments, all of the data stored in the data store 140 may be inaccessible to the users (e.g., operators) of the manufacturing system. In other or similar embodiments, some portions of the data stored in the data store 140 may be inaccessible to the users, while other portions of the data stored in the data store 140 may be accessible to the users. In some embodiments, one or more portions of the data stored in the data store 140 are encrypted using an encryption mechanism unknown to the user (e.g., the data is encrypted using a private encryption key). In other or similar embodiments, the data store 140 includes a plurality of data stores, where data that is inaccessible to the user is stored in one or more first data stores, and data that is accessible to the user is stored in one or more second data stores.

[0029] In some embodiments, the prediction system 110 includes a server machine 170 and a server machine 180. The server machine 170 includes a training set generator 172 that is capable of generating a training data set (e.g., a set of data inputs and a set of target outputs) for training, validating, and / or testing the machine learning model 190. Some operations of the training set generator 172 are described in detail below with respect to FIG. 5A. In some embodiments, the training set generator 172 can split the training data into a training set, a validation set, and a test set. In some embodiments, the prediction system 110 generates multiple sets of training data.

[0030] Server machine 180 includes a training engine 182, a verification engine 184, a selection engine 186, and / or a test engine 188. An engine may refer to hardware (e.g., circuit configuration, dedicated logic unit, programmable logic unit, microcode, processing device, etc.), software (e.g., instructions run on a processing device, general-purpose computer system, or dedicated machine), firmware, microcode, or a combination thereof. The training engine 182 may be capable of training a machine learning model 190. The machine learning model 190 may refer to a model artifact created by the training engine 182 using training data that includes training inputs and corresponding target outputs (the correct answers for each training input). The training engine 182 can discover patterns in the training data that map the training inputs to the target outputs (predicted answers) and provide a machine learning model 190 that captures these patterns. In some embodiments, the machine learning model 190 uses one or more of support vector machine (SVM), radial basis function (RBF), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, k-nearest neighbor algorithm (k-NN), linear regression, supervised regression, random forest, neural network (e.g., artificial neural network), etc.

[0031] The verification engine 184 may be able to verify the trained machine learning model 190 using a corresponding set of features of the validation set from the training set generator 172. The verification engine 184 can determine the accuracy of each of the trained machine learning models 190 based on a corresponding set of features of the validation set. The verification engine 184 can discard the trained machine learning models 190 that have an accuracy that does not meet the threshold accuracy. In some embodiments, the selection engine 186 may be able to select the trained machine learning models 190 that have an accuracy that meets the threshold accuracy. In some embodiments, the selection engine 186 may be able to select the trained machine learning model 190 that has the highest accuracy among the trained machine learning models 190.

[0032] The testing engine 188 may be able to test the trained machine learning model 190 using a corresponding set of features of the test set from the training set generator 172. For example, a first trained machine learning model 190 trained using a first set of features of the training set can be tested using a first set of features of the test set. The testing engine 188 can determine the trained machine learning model 190 that has the highest accuracy among all of the trained machine learning models based on the test set.

[0033] During the current process being performed for the first set of substrates, prediction server 112 is capable of providing data collected by one or more sensors in the manufacturing system as input to trained machine learning model 190, and running trained machine learning model 190 on that input to obtain one or more outputs. In some embodiments, prediction component 114 is also capable of extracting RF signal spike region data from one or more of the obtained outputs of the trained machine learning model, and using that RF signal spike region data to determine one or more regions of the detected multi-level RF pulse waveform associated with the RF signal spike.

[0034] RF signal spike region data may include an indication of one or more areas of data associated with a multi-level RF pulse waveform in a processing chamber corresponding to one or more RF signal spikes. In some embodiments, the RF signal spike region data includes an indication of one or more positions of one or more peaks in the detected multi-level RF pulse waveform. For example, the RF signal spike region data can identify a region of sensor data associated with a multi-level RF pulse waveform where a peak in the RF signal occurs (e.g., corresponding to one or more RF signal spikes).

[0035] Client device 120, system controller device 121, manufacturing equipment 124, sensor 126, measurement device 128, prediction server 112, data store 140, server machine 170, and server machine 180 may be coupled to each other via network 130. In some embodiments, network 130 is a public network that provides client device 120 access to prediction server 112, data store 140, and other publicly available computing devices. In some embodiments, network 130 is a private network that provides client device 120 access to manufacturing equipment 124, measurement device 128, data store 140, and other privately available computing devices. Network 130 may include one or more wide area networks (WANs), local area networks (LANs), wired networks (e.g., Ethernet networks), wireless networks (e.g., 802.11 networks or Wi-Fi networks), cellular networks (e.g., Long Term Evolution (LTE) networks), routers, hubs, switches, server computers, cloud computing networks, and / or combinations thereof.

[0036] Note that in some other implementations, the functions of server machines 170 and 180, as well as prediction server 112, may be provided by a smaller number of machines. For example, in some embodiments, server machines 170 and 180 may be integrated into a single machine, and in some other or similar embodiments, server machines 170 and 180, as well as prediction server 112, may be integrated into a single machine.

[0037] Generally, the functions described in one implementation as being performed by server machine 170, server machine 180, and / or prediction server 112 may be performed on client device 120. Additionally, the functionality attributed to a particular component may be performed by different or multiple components operating together.

[0038] In an embodiment, a "user" may be represented as an individual person (e.g., an operator, technician, engineer, etc.). However, other embodiments of the present disclosure include a "user" that is an entity controlled by multiple users and / or automated sources. For example, a set of individual users associated as a group of administrators may be considered a "user".

[0039] Some embodiments of the present disclosure describe measuring optical waveforms and multi-level RF pulse waveforms in a substrate processing system, but the present disclosure is applicable in some embodiments to other systems such as manufacturing systems that perform operations over time.

[0040] FIG. 2 is a flowchart of a method for RF pulse monitoring according to an aspect of the present disclosure. Method 200 is implemented by processing logic including hardware (e.g., circuit configuration, dedicated logic unit, programmable logic unit, microcode, processing device, etc.), software (e.g., instructions run on a processing device, general-purpose computer system, or dedicated machine), firmware, microcode, or a combination thereof. In some embodiments, method 200 is implemented in part by an RF signal engine 122 (e.g., resident in system controller device 121 and / or client device 120 as described with respect to FIG. 1). In other or similar embodiments, method 200 is implemented in part by a prediction system (e.g., prediction system 110). In some embodiments, a non-transitory storage medium stores instructions that cause a processing device (e.g., prediction system 110, RF signal engine 122, etc.) to perform method 200 when executed by the processing device. In some embodiments, the storage medium is a non-transitory machine-readable storage medium that stores instructions that cause a processing device (e.g., prediction system 110, RF signal engine 122, etc.) to perform method 200 when executed by the processing device.

[0041] For simplicity of explanation, method 200 is depicted and described as a series of acts. However, acts in accordance with the present disclosure may occur in various orders and / or concurrently, and with other acts not presented and described herein. Moreover, in some embodiments, not all illustrated acts are necessarily performed to implement method 200 according to the disclosed subject matter. Additionally, one of ordinary skill in the art will understand and appreciate that method 200 may alternatively be represented as a series of interrelated states or events via a state diagram.

[0042] Referring to FIG. 2, a flowchart of a method 200 (e.g., a process sequence for measuring waveforms and multi-level pulsing within a processing chamber) for substrate processing equipment (e.g., a processing chamber, manufacturing equipment 124 of FIG. 1) according to an aspect of the present disclosure is shown.

[0043] In block 202 of method 200, the processing logic unit pulses an RF signal within the processing chamber according to a set of RF pulsing parameters. The RF pulsing parameters correspond to a target multi-level RF pulse waveform that is to be generated within the processing chamber based on the RF signal pulsing. For example, the target multi-level RF pulse waveform corresponds to the target waveform of a process recipe, and the set of RF pulsing parameters is specified to correspond to the target multi-level RF pulse waveform. As another example, a user can specify the RF pulsing parameters based on a target response (e.g., a target behavior, a target multi-level RF pulse waveform) that is to be generated in the fluid of the processing chamber. In such an example, the RF pulsing parameters do not directly correspond to the target response, but instead may correlate to the RF signal that induces the target response.

[0044] In some embodiments, a set of RF pulsing parameters is provided by a processing device as an input to one or more RF signal generators associated with a processing chamber. In some embodiments, the RF pulsing parameters may be provided to a plurality of RF signal generators connected to the processing chamber. In some embodiments, a set of RF signal generators is associated with the processing chamber. In some embodiments, the set of RF signal generators includes three RF signal generators. Each RF signal generator may be configured to output an RF signal based on the received set of RF pulsing parameters. Each RF signal generator may also be configured to pulse or oscillate the generated RF signal based on the received set of RF pulsing parameters. In some embodiments, the RF signal pulsing provided by the RF signal generator corresponds to a multi-level RF pulse waveform detectable within the processing chamber. The multi-level RF pulse waveform may correspond to the RF pulsing parameters.

[0045] In block 204, the processing device receives sensor data from one or more sensors associated with the processing chamber. In some embodiments, the one or more sensors include at least an optical frequency sensor (OFS). In some embodiments, the optical frequency sensor senses an optical response of a plasma within the processing chamber to an RF signal output by a set of RF signal generators. The sensor data received from the one or more sensors indicates a multi-level RF pulse waveform detected within the processing chamber based on RF signal pulsing. In some embodiments, the multi-level RF pulse waveform detected within the processing chamber corresponds to the RF signal pulsing provided by the set of RF signal generators.

[0046] In block 206, the processing logic unit identifies one or more peaks in the detected multi-level RF pulse waveform. In some embodiments, the peaks in the detected multi-level RF pulse waveform correspond to regions of the detected multi-level RF pulse waveform associated with RF signal spikes. In some embodiments, the RF signal spikes correspond to transitions of the RF signal within the processing chamber from a first state to a second state (in other words, from high energy to low energy, etc.). In some embodiments, the RF signal spikes correspond to transitions of the optical response of the plasma within the processing chamber to the RF signal output by a set of RF signal generators.

[0047] In some embodiments, one or more peaks in the detected multi-level RF pulse waveform are identified by the processing logic unit applying one or more data conversion operations to the sensor data to obtain the converted multi-level RF pulse waveform. In some embodiments, the data conversion operation is to amplify the intensity of one or more RF signal spikes associated with the detected multi-level RF pulse waveform. In some embodiments, one or more regions associated with the RF signal spikes are identified by the processing logic unit based on the converted multi-level RF pulse waveform. For example, the data conversion operation can emphasize the relevant data in the sensor data (e.g., the multi-level RF pulse waveform) from which the regions associated with the RF signal spikes can then be identified.

[0048] In block 208, the processing logic unit determines whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform based on the identified one or more peaks. For example, the processing logic unit verifies that the plasma response to the RF signal output by a set of RF signal generators corresponds to the target multi-level RF pulse waveform. In some embodiments, determining whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform includes determining whether at least one of the position or magnitude of each identified region of the detected multi-level RF pulse waveform corresponds to at least one of the target position or target magnitude of the target signal spike region of the target multi-level RF pulse waveform. For example, the processing logic unit can determine that the position of one or more peaks of the multi-level RF pulse waveform corresponds to the target position of the target signal spike of the target multi-level RF pulse waveform. Alternatively, the processing logic unit can determine that the magnitude of one or more peaks of the multi-level RF pulse waveform corresponds to the target magnitude of the target signal spike of the target multi-level RF pulse waveform.

[0049] Additionally, in some embodiments, determining whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform further includes determining that the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform. In some embodiments, the determination is made in response to determining that at least one of the position or magnitude of each identified region corresponds to at least one of the target position or target magnitude of the target signal spike region. For example, if the position of the identified region of the detected multi-level RF pulse waveform associated with the RF signal spike corresponds to the target position of the target signal spike region, the processing logic unit determines that the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform.

[0050] In block 210, the processing device provides an indication as to whether the detected multi-level RF pulse waveform corresponds to a target multi-level RF pulse waveform. In some embodiments, the indication is provided via a GUI of a client device (e.g., client device 120 of FIG. 1) connected to the manufacturing system. In some embodiments, the indication includes providing the position of each of one or more peaks in the detected multi-level RF pulse waveform. In some embodiments, the position of each peak corresponds to the position of a region associated with an RF signal spike. For example, the processing device provides data indicating the position of one or more peaks to the GUI, and the GUI may display the data to the user. In some embodiments, the GUI displays the position of one or more peaks.

[0051] FIG. 3 is a block diagram illustrating an exemplary radio frequency (RF) signal engine associated with measuring waveforms and multi-level pulsing within a processing chamber, according to aspects of the present disclosure. In some embodiments, RF signal engine 322 corresponds to RF signal engine 122 of FIG. 1. In some embodiments, RF signal engine 322 includes one or more components. For example, RF signal engine 322 may include data conversion component 392, spike data extraction component 394, data noise suppression component 196, and / or peak detection component 398. In some embodiments, each component of RF signal engine 322 performs one or more operations.

[0052] In some embodiments, RF signal engine 322 receives sensor data 350. The sensor data is received from one or more sensors associated with the processing chamber. In some embodiments, the sensor data indicates a multi-level RF pulse waveform detected within the processing chamber. As described above, the multi-level RF pulse waveform detected within the processing chamber corresponds to an optical response of a plasma within the processing chamber to an RF signal output by one or more RF signal generators.

[0053] In some embodiments, the data conversion component 392 receives sensor data 350 from one or more sensors in the manufacturing equipment 124. In other or similar embodiments, the data conversion component 392 can obtain the sensor data 350 (e.g., from the data store 140). In some embodiments, the data conversion component 392 applies one or more data conversion operations to the sensor data to obtain the converted multi-level RF pulse waveform data. In some embodiments, the one or more data conversion operations include a base-10 logarithmic conversion operation, a natural logarithmic conversion operation, an inverse conversion operation, an inverse logarithmic conversion operation, or a no-conversion operation. The data conversion operation can amplify the intensity of one or more RF signal spikes associated with the detected multi-level RF pulse waveform. In some embodiments, a machine learning model can be used to identify the data conversion operation to use from a set of data conversion operations. The sensor data can be provided as an input to the machine learning model. Machine learning can be trained to identify the data conversion to be applied to a given sensor data based on historical sensor data and historical data conversion operations applied to the historical sensor data. The data conversion component 392 can identify the data conversion operation to apply to the sensor data based on one or more outputs of the machine learning model, and as described above, apply the identified data conversion operation to the sensor data. In some embodiments, an optimization model is used to identify the data conversion to use from a set of data conversion operations.

[0054] In some embodiments, the data conversion component 392 provides the converted multi-level RF pulse waveform data to the spike data extraction component 394. In some embodiments, the spike data extraction component 394 determines spike data associated with the converted multi-level RF pulse waveform data (see, e.g., the spike signal in FIG. 4). In some embodiments, the spike data is based on the converted multi-level RF pulse waveform data. In some embodiments, the spike data extraction component 394 determines transitions of the converted multi-level RF pulse waveform data. The spike data may correspond to transitions of the converted multi-level RF pulse waveform data. In some embodiments, the spike data extraction component 394 determines the magnitude of the rate of change of one or more regions of the converted multi-level RF pulse waveform data. For example, the spike data extraction component 394 may identify the magnitude of the rate of change of the converted multi-level RF pulse waveform data. In some embodiments, the magnitude of some regions of the converted multi-level RF pulse waveform data may have a greater rate of change with a greater magnitude than other regions. The spike data can indicate one or more maximum or minimum values (e.g., spikes) corresponding to one or more regions of the converted multi-level RF pulse waveform data having a rate of change with a greater magnitude than one or more other regions (see, e.g., the peaks in FIG. 4). In some embodiments, the spike data extraction component 394 employs one or more operators to determine spike data from the converted multi-level RF pulse waveform data. For example, the spike data extraction component 394 can employ a Teager Kaiser energy operator to determine spike data from the converted multi-level RF pulse waveform data.

[0055] In some embodiments, the spike data extraction component 394 provides spike data to the data noise suppression component 396. The data noise suppression component 396 may be configured to reduce (e.g., attenuate) noise in the spike data. Noise may refer to additional or extra data from that portion of the data. By reducing the amount of noise in the data, the RF signal engine 322 can better isolate the peaks in the spike data, where the peaks correspond to regions of the detected multi-level RF pulse waveform associated with RF signal spikes. In some embodiments, the data noise suppression component 396 can perform one or more data averaging operations on the spike data. In some embodiments, the data averaging operation performed by the data noise suppression component 396 may correspond to a first-in first-out (FIFO) queue operation. In other or similar embodiments, the data noise suppression component 396 uses a moving average filter to reduce noise in the spike data.

[0056] In some embodiments, the data noise suppression component 396 provides spike data with reduced noise to the peak detection component 398. In some embodiments, the peak detection component 398 identifies the number of peaks in the spike data. In some embodiments, the peak detection component 398 can use a maximum value method to identify the peaks in the spike data. For example, the peak detection component 398 can identify the maximum value in the region associated with the spike data that is separated from another region associated with the spike data corresponding to another maximum value (e.g., based on a threshold distance). The maximum value of each region can correspond to each peak of the waveform. For example, the peak detection component 398 can determine that two maximum values located within a threshold distance from each other correspond to a single peak. In another example, the peak detection component 398 can determine that two maximum values located outside a threshold distance from each other correspond to at least two distinct peaks. In some embodiments, the peak detection component 398 determines the position and magnitude of each identified peak. In some embodiments, each detected peak can correspond to a peak specified (e.g., by a user) to be detected within the spike data. In such embodiments, the processing logic unit determines, based on the specified number of peaks, the number of peaks that the peak detection component 398 detects within the spike data.

[0057] In some embodiments, peak detection component 398 provides peak data 380. In some embodiments, peak data 380 may include indications of one or more peak positions and / or peak magnitudes associated with each peak identified for the multi-level RF pulse waveform. In some embodiments, the peak detection component provides peak data 380 to data noise suppression component 396. Data noise suppression component 396 performs one or more data averaging operations on peak data 380 to reduce (e.g., attenuate) the noise in peak data 380. In some embodiments, after the noise in peak data 380 is reduced, peak data 380 may indicate one or more average positions and / or one or more average magnitudes of one or more corresponding peaks of the spike signal (e.g., the spike signal of FIG. 4) associated with the multi-level RF pulse waveform.

[0058] In some embodiments, one or more of the functions of RF signal engine 322 are performed by one or more machine learning models. In some embodiments, a trained machine learning model determines peak data 380 based on receiving sensor data 350 as input. The machine learning implementations of the present disclosure are discussed in more detail in connection with FIGS. 5A-5C.

[0059] FIG. 4 is a graph illustrating a multi-level RF pulse waveform, a related spike signal, and related peaks, according to aspects of the present disclosure. In some embodiments, the sensor data (e.g., sensor data 350 of FIG. 3) includes an optical frequency sensor (OFS) waveform. In some embodiments, the OFS waveform is a multi-level RF pulse waveform detected in a processing chamber. In some embodiments, the OFS waveform indicates a response to one or more RF signals generated by one or more RF generators associated with the processing chamber according to a set of RF pulsing parameters. In some embodiments, the OFS waveform is an optical response of a plasma within the processing chamber to one or more signals generated by one or more RF generators.

[0060] In some embodiments, the spike signal is identified based on the OFS waveform (e.g., by spike data extraction component 394 of FIG. 3). In some embodiments, the spike signal corresponds to spike data (in other words, spike data generated by spike data extraction component 394). In some embodiments, data noise in either or both of the OFS waveform and / or the spike signal is reduced (e.g., attenuated) by a data noise attenuation component (e.g., data noise suppression component 396 of FIG. 3). In some embodiments, the spike signal includes one or more maxima or minima. In some embodiments, the maxima of the spike signal correspond to one or more regions of the OFS waveform having a high rate of change.

[0061] In some embodiments, one or more peaks of the OFS waveform correspond to one or more maxima of the spike signal. In some embodiments, one or more peaks are identified by a peak detection component (e.g., peak detection component 398 of FIG. 3). In some embodiments, one or more peaks are associated with spikes (e.g., RF signal spikes) in the spike signal. In some embodiments, the position of each of the one or more peaks (see the triangles on the x-axis of FIG. 4) is provided to the client device (e.g., via network 130). In some embodiments, the position of each peak is provided to the user via the GUI of the client device. In some embodiments, the position of each of the one or more peaks is the average position of each of the one or more peaks based on a plurality of samples of the OFS waveform. In some embodiments, a corrective action is performed based on the position of each of the one or more peaks.

[0062] Figures 5A-5C are flowcharts of methods 500A-500C for training and using a machine learning model to detect RF peaks in a detected multi-level RF waveform, according to aspects of the present disclosure. Methods 500A-500C may be implemented by processing logic that may include hardware (e.g., circuit configurations, dedicated logic units, programmable logic units, microcode, processing devices, etc.), software (e.g., instructions run on a processing device, general-purpose computer system, or dedicated machine), firmware, microcode, or combinations thereof. In some embodiments, methods 500A-500C may be implemented, in part, by prediction system 110. Method 500A may be implemented, in part, by prediction system 110 (e.g., server machine 170 and training set generator 172 of FIG. 1). Prediction system 110 may use method 500A to generate a data set for performing at least one of training, validating, or testing a machine learning model according to embodiments of the present disclosure. Method 500B may be implemented by server machine 180 (e.g., training engine 182, etc.). Method 500C may be implemented by prediction server 112 (e.g., prediction component 114). In some embodiments, a non-transitory storage medium stores instructions that cause a processing device (e.g., of prediction system 110, server machine 180, prediction server 112, etc.) to perform one or more of methods 500A-500C when executed by the processing device. In some embodiments, the storage medium is a non-transitory machine-readable storage medium that stores instructions that cause a processing device (e.g., prediction system 110, client device 120, etc.) to perform methods 500A-500C when executed by the processing device.

[0063] To simplify the description, methods 500A - 500C are depicted and described as a series of operations. However, the operations according to the present disclosure can be performed in various orders and / or simultaneously, as well as with other operations not presented and described herein. Moreover, not all of the illustrated operations may be implemented to carry out methods 500A - 500C according to the disclosed subject matter. Additionally, those skilled in the art will understand and appreciate that methods 500A - 500C may alternatively be represented as a series of interrelated states or events via a state diagram.

[0064] FIG. 5A is a flow diagram of method 500A for generating a dataset for training a machine learning model (e.g., model 190 of FIG. 1) to detect RF peaks (e.g., peak data 380 of FIG. 3) in a detected multi - level RF pulse waveform according to an aspect of the present disclosure.

[0065] Referring to FIG. 5A, in some embodiments, at block 501, the processing logic unit implementing method 500A initializes the training set T to an empty set.

[0066] At block 502, the processing logic unit generates a first data input (e.g., a first training input, a first validation input) that may include sensor data (e.g., sensor data 350 of FIG. 3). In some embodiments, the first data input may include a first set of features about the type of data, and the second data input may include a second set of features about the type of data.

[0067] At block 503, the processing logic unit generates a first target output for one or more of the data inputs (e.g., the first data input). In some embodiments, the first target output is a characteristic value received as an output from the RF signal engine 322 (e.g., peak data 380).

[0068] In block 504, the processing logic unit optionally generates mapping data indicating an input / output mapping. The input / output mapping (or mapping data) can refer to a data input (e.g., one or more of the data inputs described herein), a target output for the data input, and an association between the data input and the target output.

[0069] In block 505, the processing logic unit adds the mapping data generated in block 504 to the data set T in some embodiments.

[0070] In block 506, the processing logic unit branches based on whether the data set T is sufficient for at least one of training, validating, and / or testing the machine learning model 190 of FIG. 1. If so, the execution proceeds to block 507; otherwise, the execution returns to block 502. Note that in some embodiments, whether the data set T is sufficient can be determined simply based on the number of inputs that are mapped to outputs in the data set, and in some other implementations, whether the data set T is sufficient can be determined based on one or more other criteria (e.g., a measure of the diversity of the data examples, accuracy, etc.) in addition to or instead of the number of inputs.

[0071] In block 507, the processing logic unit provides the data set T (e.g., to the server machine 180 of FIG. 1) to train, validate, and / or test the machine learning model 190. In some embodiments, the data set T is a training set and is provided to the training engine 182 of the server machine 180 to perform training. In some embodiments, the data set T is a validation set and is provided to the validation engine 184 of the server machine 180 to perform validation. In some embodiments, the data set T is a test set and is provided to the test engine 188 of the server machine 180 to perform testing.

[0072] Through multiple training sets, the machine learning model selects an appropriate value for a parameter that controls the number of non-zero coefficients in the degree reduction model. After block 507, the machine learning model (e.g., machine learning model 190) can be trained using the training engine 182 of the server machine 180, verified using the verification engine 184 of the server machine 180, or tested using the test engine 188 of the server machine 180, or at least one of these. The trained machine learning model can be implemented by the prediction component 114 (of the prediction server 112) to perform signal processing or to generate peak data 380 for performing corrective actions related to the manufacturing equipment 124.

[0073] FIG. 5B is a method 500B for training a machine learning model (e.g., model 190 of FIG. 1) to detect RF peaks in a detected multi-level RF pulse waveform, according to an aspect of the present disclosure.

[0074] Referring to FIG. 5B, in block 510 of method 500B, the processing logic unit pulses an RF signal in the processing chamber according to a set of RF pulsing parameters. In some embodiments, the processing logic unit causes the pulsing of the RF signal by one or more RF signal generators based on the set of RF pulsing parameters. In some embodiments, the RF signal generator causes a response in the processing chamber in the form of a multi-level RF pulse waveform in the fluid of the processing chamber, as described above.

[0075] In block 512, the processing logic unit receives sensor data from one or more sensors associated with the processing chamber. The sensor data indicates a multi-level RF pulse waveform detected within the processing chamber based on RF signal pulsing by an RF generator. In some embodiments, the sensor data indicates an optical response of the plasma within the processing chamber to the RF signal pulsing. The sensor data may be retrieved by the processing logic unit from a memory (e.g., data store 140 of FIG. 1) rather than directly from one or more sensors.

[0076] In block 514, the processing logic unit trains a machine learning model (e.g., model 190 of FIG. 1). The machine learning model is trained using a data input that includes historical sensor data and historical RF pulsing parameter data. In some embodiments, the historical sensor data corresponds to the sensor data and the historical sensor RF pulsing parameter data corresponds to the RF pulsing parameters (e.g., RF pulsing parameter data). The machine learning model is trained using a target output data that includes historical RF signal spike region data. In some embodiments, the historical RF signal spike region data corresponds to regions of one or more historical multi-level RF pulse waveforms detected within the processing chamber that are associated with RF signal spikes of the one or more historical multi-level RF pulse waveforms. The training data input to the machine learning model is mapped to the training target output. The machine learning model is trained to identify one or more regions of a multi-level RF pulse waveform (e.g., of a multi-level RF pulse waveform) that are associated with RF signal spikes (e.g., indicated by the sensor data). In some embodiments, the machine learning model is trained to output RF signal spike region data (e.g., one or more positions or magnitudes of one or more peaks associated with the RF signal spikes).

[0077] In some embodiments, two or more machine learning models can be part of a single composite machine learning model. In this case, training one component of this composite model can involve receiving an output from another component of the model as a training input to the component of the model being trained.

[0078] In block 516, the machine learning model can be retrained using additional data. The machine learning model can be retrained using a data input that includes sensor data and RF pulsing parameter data (e.g., RF pulsing parameters). The machine learning model can be retrained using target output data that includes RF signal spike region data. The machine learning model can be further retrained to identify one or more regions in a future multi-level RF pulse waveform that are associated with RF signal spikes of the multi-level RF pulse waveform. In some embodiments, the machine learning model can be continuously trained to account for drifts in manufacturing equipment, sensors, measurement devices, etc., or to reflect changes to procedures or recipes, etc.

[0079] FIG. 5C is a method 500C for using a trained machine learning model (e.g., model 190 of FIG. 1) to detect RF peaks in a detected multi-level RF pulse waveform, according to aspects of the present disclosure.

[0080] Referring to FIG. 5C, in block 520 of method 500C, the processing logic unit pulses an RF signal in the processing chamber according to a set of RF pulsing parameters. In some embodiments, the processing logic unit causes the RF signal to be pulsed based on a set of RF pulsing parameters (e.g., by one or more RF signal generators).

[0081] In block 522, the processing logic unit receives sensor data indicating a multi-level RF pulse waveform detected within the processing chamber. The multi-level RF pulse waveform is based on RF signal pulsing. The sensor data type may correspond to that provided in block 514 of FIG. 5B for training a machine learning model.

[0082] In block 522, the processing logic unit receives sensor data from one or more sensors associated with the processing chamber, indicating a multi-level RF pulse waveform detected by one or more sensors within the processing chamber, where the multi-level RF pulse waveform is based on RF signal pulsing. In some embodiments, the sensor data indicates an OFS waveform (e.g., the OFS waveform of FIG. 4).

[0083] In block 524, the processing logic unit provides a set of sensor data and RF pulsing parameters as an input to a trained machine learning model (e.g., model 190 of FIG. 1). The machine learning model may be trained according to the embodiments described with respect to FIGS. 5A and 5B. For example, the machine learning model is trained to predict one or more regions of each multi-level RF pulse waveform associated with an RF signal spike, as described above. In another example, the machine learning model is trained to predict one or more peak positions of the multi-level RF pulse waveform data, where the one or more peak positions correspond to one or more regions associated with an RF signal spike, as described above.

[0084] In block 526, the processing logic unit obtains one or more outputs of the machine learning model. In some embodiments, one or more outputs of the machine learning model indicate one or more regions of a multi-level RF pulse waveform detected in the processing chamber that are related to RF signal spikes (e.g., RF signal pulsing). In some embodiments, one or more outputs of the machine learning model indicate one or more peaks of a multi-level RF pulse waveform detected in the processing chamber.

[0085] In block 528, the processing logic unit extracts RF signal spike data from one or more obtained outputs of the machine learning model. In some embodiments, the RF signal spike data indicates one or more regions of the detected multi-level RF pulse waveform that include the RF signal spikes. In some embodiments, one or more peaks in the detected multi-level RF pulse waveform correspond to one or more regions (e.g., one or more maxima of the spike signal in FIG. 4) indicated by the RF signal spike data that include the RF signal spikes. In some embodiments, one or more regions of the detected multi-level pulse waveform indicate RF signal spikes corresponding to a transition of the RF signal in the processing chamber from a first state to a second state. In some embodiments, each respective region of the one or more regions corresponds to each respective peak of one or more peaks of the multi-level RF pulse waveform.

[0086] FIG. 6 is a flowchart of a method for updating a set of RF pulsing parameters according to an aspect of the present disclosure. Method 600 may be implemented by processing logic including hardware (e.g., circuit configurations, dedicated logic units, programmable logic units, microcode, processing devices, etc.), software (e.g., instructions run on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or combinations thereof. In some embodiments, method 600 is implemented in part by an RF signal engine 122 (e.g., resident in system controller device 121 and / or client device 120 as described with respect to FIG. 1). In other or similar embodiments, method 600 is implemented in part by a prediction system (e.g., prediction system 110). In some embodiments, a non-transitory storage medium stores instructions that cause a processing device (e.g., prediction system 110, RF signal engine 122, etc.) to perform method 600 when executed by the processing device. In some embodiments, the storage medium is a non-transitory machine-readable storage medium that stores instructions that cause a processing device (e.g., prediction system 110, RF signal engine 122, etc.) to perform method 600 when executed by the processing device.

[0087] For simplicity of explanation, method 600 is depicted and described as a series of acts. However, acts according to the present disclosure may occur in various orders and / or concurrently, and with other acts not presented and described herein. Moreover, in some embodiments, not all illustrated acts are necessarily performed to implement method 600 according to the disclosed subject matter. Additionally, one of ordinary skill in the art will understand and appreciate that method 600 may alternatively be represented as a series of interrelated states or events via a state diagram.

[0088] Referring to FIG. 6, a flowchart of a method 600 (e.g., a process sequence for updating a set of RF pulsing parameters) for substrate processing equipment (e.g., a processing chamber, manufacturing equipment 124 of FIG. 1) according to a particular embodiment is shown.

[0089] In block 602 of method 600, the processing logic unit receives a first set of RF pulsing parameters. In some embodiments, the first pulsing parameters may be provided via a GUI of a client device (e.g., client device 120 of FIG. 1). The processing logic unit can receive the first set of RF pulsing parameters from the client device (e.g., via a network). The first pulsing parameters may correspond to a target multi-level RF pulse waveform of a process operation. For example, a user may specify the first pulsing parameters via the GUI of the client device based on the target multi-level RF pulse waveform.

[0090] In block 604, the processing logic unit pulses an RF signal in the processing chamber according to the first set of RF pulsing parameters. In some embodiments, the system controller can provide the first pulsing parameters to a plurality of RF signal generators. The RF signal generators can emit an RF signal based on the first pulsing parameters according to the previously described embodiments.

[0091] In block 606, the processing logic unit receives first sensor data indicating a first multi-level RF pulse waveform detected in the processing chamber from one or more sensors (e.g., OFS). The first detected multi-level RF pulse waveform may be based on RF signal pulsing based on the first set of RF pulsing parameters. The first detected multi-level RF pulse waveform may be related to a first substrate process.

[0092] In block 608, the processing logic unit determines whether the detected first multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform. In some embodiments, the processing logic unit determines whether the detected first multi-level RF pulse waveform sufficiently matches (e.g., substantially matches) the target multi-level RF pulse waveform (e.g., within a predetermined threshold). In some embodiments, the client device receives data related to one or more peak positions of the target multi-level RF pulse waveform.

[0093] In block 610, the processing logic unit provides an indication to a client device (e.g., client device 120 of FIG. 1) connected to the manufacturing system as to whether the detected first multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform.

[0094] In block 612, the processing logic unit receives a second set of RF pulsing parameters. The client device can receive the second set of RF pulsing parameters via the GUI of the client device. The system controller can receive the second set of RF pulsing parameters from the client device (i.e., via the network). In some embodiments, one or more of the second RF pulsing parameters may be different from one or more of the first RF pulsing parameters. For example, the second frequency parameter may be different from the first frequency parameter. In some embodiments, the user adjusts one or more of the first RF pulsing parameters based on the peak data associated with the first multi-level RF pulse waveform. One or more adjustments to the one or more first RF pulsing parameters may be reflected in the second set of RF pulsing parameters. In some embodiments, the client device can receive one or more proposed instructions related to the RF pulsing parameters that will cause the future multi-level RF pulse waveform to correspond to the target multi-level RF pulse waveform. In some embodiments, the user may specify the second set of RF pulsing parameters based on data related to one or more peak positions of the target multi-level RF pulse waveform received by the client device.

[0095] In block 614, the processing logic unit pulses the RF signal in the processing chamber according to the second set of RF pulsing parameters. In some embodiments, the processing logic unit provides the second pulsing parameters to a plurality of RF signal generators. The RF signal generator may emit an RF signal based on the second pulsing parameters.

[0096] In block 616, the processing logic unit receives second sensor data indicating a second multi-level RF pulse waveform detected within the processing chamber from one or more sensors (e.g., OFS). The second detected multi-level RF pulse waveform can be based on RF signal pulsing based on a second set of RF pulsing parameters. The second detected multi-level RF pulse waveform can be related to a second substrate process.

[0097] In block 618, the processing logic unit determines whether the detected second multi-level RF pulse waveform corresponds to a target multi-level RF pulse waveform. In some embodiments, the processing logic unit determines whether the detected second multi-level RF pulse waveform sufficiently (e.g., substantially) matches the target multi-level RF pulse waveform (e.g., within a predetermined threshold).

[0098] In block 620, the processing logic unit provides an indication to a client device (e.g., client device 120 of FIG. 1) connected to the manufacturing system as to whether the detected second multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform. In some embodiments, blocks 612-620 are repeated until the detected future multi-level RF pulse waveform corresponds to the target multi-level pulse waveform.

[0099] FIG. 7 is a block diagram illustrating a computer system 700 according to aspects of the present disclosure. In some embodiments, the computer system 700 is a client device 120 (e.g., of FIG. 1). In some embodiments, the computer system 700 is a controller device (e.g., a server).

[0100] In some embodiments, computer system 700 is connected to other computer systems (e.g., via a network such as a local area network (LAN), intranet, extranet, or the Internet). Computer system 700 operates within the capabilities of a server or client computer in a client-server environment, or as a peer computer in a peer-to-peer or distributed network environment. In some embodiments, computer system 700 is provided by a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), cellular phone, web appliance, server, network router, switch or bridge, or any device capable of executing a set of (sequential or otherwise) instructions that specify actions to be taken by that device. Further, the term "computer" shall be taken to include any collection of computers that individually or jointly execute a set of (or multiple sets of) instructions to perform any one or more of the methods described herein.

[0101] In some embodiments, computer system 700 includes a processor 702, volatile memory 704 (e.g., random access memory (RAM)), non-volatile memory 706 (e.g., read only memory (ROM) or electrically erasable programmable ROM (EEPROM)), and / or data storage device 716 that communicate with each other via bus 708.

[0102] In some embodiments, processor 702 is provided by one or more processors, such as a general-purpose processor (e.g., a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of multiple types of instruction sets), or a special processor (e.g., an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor, etc.). In some embodiments, processor 702 is provided by one or more of a single processor, multiple processors, a single processor having multiple processing cores, etc.

[0103] In some embodiments, computer system 700 further includes a network interface device 722 (e.g., coupled to network 774). In some embodiments, computer system 700 includes one or more input / output (I / O) devices. In some embodiments, computer system 700 also includes a video display unit 710 (e.g., an LCD), an alphanumeric input device 712 (e.g., a keyboard), a cursor control device 714 (e.g., a mouse), and / or a signal generation device 720.

[0104] In some implementations, a data storage device 718 (e.g., disk drive storage, fixed and / or removable storage devices, fixed disk drives, removable memory cards, optical storage, network attached storage (NAS), and / or storage area network (SAN)) encodes any one or more of the methods or functions described herein, includes instruction encoding components (e.g., RF signal engine 122, etc.) of FIG. 1, and includes a non-transitory computer-readable storage medium 724 that stores instructions 726 for implementing the methods described herein. In some embodiments, processor 702 includes RF signal engine 122. In some embodiments, peak data, multi-level RF pulse waveform data, and / or RF pulsing parameter data are stored in data storage device 718.

[0105] In some embodiments, instructions 726 may also reside, in whole or in part, within volatile memory 704 and / or within processor 702 during execution of instructions 726 by computer system 700, and thus, volatile memory 704 and processor 702 also constitute a machine-readable storage medium in some embodiments.

[0106] Computer-readable storage medium 724 is shown as an example in the context of a single medium, but the term "computer-readable storage medium" shall include a single medium or multiple media (e.g., centralized or distributed databases, and / or associated caches and servers) that store one or more sets of executable instructions. The term "computer-readable storage medium" shall also include any tangible medium that can store or encode a set of instructions for execution by a computer to cause the computer to perform any one or more of the methods described herein. The term "computer-readable storage medium" shall include, without limitation, solid state memory, optical media, and magnetic media.

[0107] In some embodiments, the methods, components, and features described herein are implemented by individual hardware components or integrated into the functionality of other hardware components such as ASICs, FPGAs, DSPs, or similar devices. In some embodiments, the methods, components, and features are implemented by firmware modules or functional circuitry configurations within a hardware device. Further, the methods, components, and features are implemented in any combination of a hardware device and computer program components or by a computer program.

[0108] Unless otherwise specified, terms such as "identifying," "calculating," "determining," "applying," "obtaining," "causing," "receiving," "pulsing," "providing," "generating," etc. refer to actions and processes implemented or performed by a computer system that manipulate and transform data represented as physical (electronic) quantities within the registers and memories of the computer system into other data similarly represented as physical quantities within the memories or registers of the computer system or other such information storage, transmission, or display devices. Also, as used herein, the terms "first," "second," "third," "fourth," etc. are labels used to distinguish different elements and do not have a meaning of order by their numerical representation.

[0109] The examples described herein also relate to an apparatus for implementing the methods described herein. In some embodiments, this apparatus is specially constructed to implement the methods described herein or the apparatus includes a general-purpose computer system selectively programmed by a computer program stored in the computer system. Such a computer program is stored on a computer-readable tangible storage medium.

[0110] The methods and illustrative examples described herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used in accordance with the teachings described herein, or in some embodiments, it may prove convenient to construct more specialized apparatus to perform the methods and / or each of their individual functions, routines, subroutines, or operations described herein. Examples of structures for various such systems are described in the above description.

[0111] The above description is intended as an example and not as a limitation. While the present disclosure has been described with respect to specific illustrative examples and implementations, it will be recognized that the present disclosure is not limited to the examples and implementations described. The scope of the present disclosure should be determined with respect to the following claims, along with the full scope of equivalents to which the claims are entitled.

Claims

**Claim 1** Pulsing a radio frequency (RF) signal within a processing chamber of a manufacturing system according to a set of RF pulsing parameters, wherein the set of RF pulsing parameters corresponds to a target multi-level RF pulse waveform to be generated within the processing chamber based on the RF signal pulsing by one or more RF generators associated with the processing chamber, and pulsing the RF signal; Receiving sensor data from one or more sensors associated with the processing chamber, the sensor data indicating a multi-level RF pulse waveform detected within the processing chamber based on the RF signal pulsing; Identifying one or more peaks in the detected multi-level RF pulse waveform, wherein each of the identified one or more peaks corresponds to at least one RF signal pulse of the RF signal pulsing within the processing chamber, and identifying the one or more peaks; Determining, based on the identified one or more peaks, whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform; Providing an indication to a client device connected to the manufacturing system as to whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform A method comprising the steps above. **Claim 2** Identifying the one or more peaks in the detected multi-level RF pulse waveform comprises: Identifying one or more regions of the detected multi-level RF pulse waveform associated with RF signal spikes, wherein the RF signal spikes correspond to pulses of the RF signal within the processing chamber between a first state and a second state, and each of the one or more identified regions corresponds to a respective one of the identified one or more peaks, and identifying the one or more regions The method according to claim 1, comprising the steps above. **Claim 3** Determining whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform comprises: Determining whether at least one of the positions or magnitudes of the identified regions of each of the detected multi-level RF pulse waveforms corresponds to at least one of the target position or target magnitude of the target signal spike region of the target multi-level RF pulse waveform; In response to determining that at least one of the position or magnitude of each of the identified regions corresponds to at least one of the target position or target magnitude of the target signal spike region, determining that the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform; The method according to claim 2, comprising:

4. Identifying the one or more peaks in the detected multi-level RF pulse waveform comprises: Applying one or more data conversion operations to the sensor data to obtain converted multi-level RF pulse waveform data, wherein the data conversion operations amplify the intensity of one or more RF signal spikes associated with the detected multi-level RF pulse waveform, and the one or more regions associated with the RF signal spikes are identified based on the converted multi-level RF pulse waveform, applying one or more data conversion operations; The method according to claim 2, further comprising:

5. Identifying the one or more peaks in the detected multi-level RF pulse waveform comprises: Providing a set of the sensor data and the RF pulsing parameters as an input to a machine learning model, wherein the machine learning model is trained to predict one or more regions of each multi-level RF pulse waveform associated with an RF signal spike, providing the set of the sensor data and the RF pulsing parameters; Obtaining one or more outputs of the machine learning model; extracting RF signal spike data from the one or more obtained outputs, wherein the RF signal spike data indicates one or more regions of the detected multi-level RF pulse waveform that include RF signal spikes, and the one or more peaks in the detected multi-level RF pulse waveform correspond to the one or more regions indicated by the RF signal spike data and include the RF signal spikes, and extracting the RF signal spike data The method according to claim 1, comprising: **Claim 6** The method according to claim 1, wherein the RF pulsing parameter includes at least one of a pulsing duty cycle parameter, a pulsing bias shift parameter, a pulsing power parameter, a pulsing frequency parameter, or a pulsing amplitude parameter, and the one or more RF generators associated with the processing chamber generate a pulsed RF signal waveform based on the RF pulsing parameter. **Claim 7** The method according to claim 1, wherein the one or more sensors associated with the processing chamber include an optical frequency sensor (OFS). **Claim 8** The method according to claim 1, wherein the one or more RF generators associated with the processing chamber include a plurality of RF generators connected to the processing chamber, and the multi-level RF pulse waveform indicates an RF pulsed response detected based on the RF signal pulsed by the plurality of RF generators. **Claim 9** A non-transitory machine-readable storage medium storing instructions that, when executed, cause a processing device to pulse an RF signal within a processing chamber of a manufacturing system according to a set of radio frequency (RF) pulsing parameters, wherein the set of RF pulsing parameters corresponds to a target multi-level RF pulse waveform to be generated within the processing chamber based on the RF signal pulsing by one or more RF generators associated with the processing chamber, and pulse the RF signal receive sensor data indicating a multi-level RF pulse waveform detected within the processing chamber based on the RF signal pulsing from one or more sensors associated with the processing chamber Identifying one or more peaks in the detected multi-level RF pulse waveform, wherein each of the identified one or more peaks corresponds to at least one RF signal pulse of the RF signal pulsing in the processing chamber, identifying one or more peaks; Determining whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform based on the identified one or more peaks; Providing an indication to a client device connected to the manufacturing system as to whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform A non-transitory machine-readable storage medium that causes the above to be performed.

10. Identifying the one or more peaks in the detected multi-level RF pulse waveform is Identifying one or more regions of the detected multi-level RF pulse waveform associated with RF signal spikes, wherein the RF signal spikes correspond to pulses of the RF signal in the processing chamber between a first state and a second state, and each of the one or more identified regions corresponds to a respective peak of the identified one or more peaks, identifying one or more regions The non-transitory machine-readable storage medium according to claim 9, comprising:

11. Determining whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform is Determining whether at least one of the position or magnitude of each identified region of the detected multi-level RF pulse waveform corresponds to at least one of the target position or target magnitude of the target signal spike region of the target multi-level RF pulse waveform; In response to determining that the at least one of the position or the magnitude of each of the identified regions corresponds to the at least one of the target position or the target magnitude of the target signal spike region, determining that the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform The non-transitory machine-readable storage medium according to claim 10, comprising:

12. Identifying the one or more peaks in the detected multi-level RF pulse waveform is applying one or more data conversion operations to the sensor data to obtain converted multi-level RF pulse waveform data, the data conversion operations amplifying the intensity of one or more RF signal spikes associated with the detected multi-level RF pulse waveform, and the one or more regions associated with the RF signal spikes being identified based on the converted multi-level RF pulse waveform, applying one or more data conversion operations The non-transitory machine-readable storage medium according to claim 10, further comprising.

13. Identifying the one or more peaks in the detected multi-level RF pulse waveform is providing a set of the sensor data and the RF pulsing parameters as an input to a machine learning model, the machine learning model being trained to predict one or more regions of each multi-level RF pulse waveform associated with an RF signal spike, providing the set of the sensor data and the RF pulsing parameters, obtaining one or more outputs of the machine learning model, and extracting RF signal spike data from the one or more obtained outputs, the RF signal spike data indicating one or more regions of the detected multi-level RF pulse waveform including the RF signal spike, and the one or more peaks in the detected multi-level RF pulse waveform corresponding to the one or more regions indicated by the RF signal spike data including the RF signal spike, extracting RF signal spike data The non-transitory machine-readable storage medium according to claim 9, comprising.

14. The non-transitory machine-readable storage medium according to claim 9, wherein the RF pulsing parameters include at least one of a pulsing duty cycle parameter, a pulsing bias shift parameter, a pulsing power parameter, a pulsing frequency parameter, or a pulsing amplitude parameter, and the one or more RF generators associated with the processing chamber generate a pulsed RF signal waveform based on the RF pulsing parameters.

15. The non-transitory machine-readable storage medium of claim 9, wherein the one or more sensors associated with the processing chamber include an optical frequency sensor (OFS).

16. A memory, A processing device connected to the memory, wherein the processing device Pulses an RF signal in a processing chamber of a manufacturing system according to a set of radio frequency (RF) pulsing parameters, wherein the set of RF pulsing parameters corresponds to a target multi-level RF pulse waveform generated in the processing chamber based on the RF signal pulsing by one or more RF generators associated with the processing chamber, pulsing the RF signal; Receiving sensor data indicating a multi-level RF pulse waveform detected in the processing chamber based on the RF signal pulsing from one or more sensors associated with the processing chamber; Identifying one or more peaks in the detected multi-level RF pulse waveform, each of the identified one or more peaks corresponding to at least one RF signal pulse of the RF signal pulsing in the processing chamber, identifying one or more peaks; Determining whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform based on the identified one or more peaks; Providing an indication to a client device connected to the manufacturing system as to whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform for performing, a processing device, and A system including.

17. Identifying the one or more peaks in the detected multi-level RF pulse waveform comprises Identifying one or more regions of the detected multi-level RF pulse waveform associated with RF signal spikes, wherein the RF signal spikes correspond to pulses of the RF signal in the processing chamber between a first state and a second state, and each of the one or more identified regions corresponds to a respective peak of the identified one or more peaks, identifying one or more regions The system of claim 16, comprising.

18. Determining whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform involves determining whether at least one of the positions or magnitudes of the identified regions of the detected multi-level RF pulse waveform corresponds to at least one of the target position or target magnitude of the target signal spike region of the target multi-level RF pulse waveform, and in response to determining that at least one of the position or magnitude of each of the identified regions corresponds to at least one of the target position or target magnitude of the target signal spike region, determining that the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform The system according to claim 17, comprising.

19. Identifying the one or more peaks in the detected multi-level RF pulse waveform involves applying one or more data conversion operations to the sensor data to obtain converted multi-level RF pulse waveform data, wherein the data conversion operations amplify the intensity of one or more RF signal spikes associated with the detected multi-level RF pulse waveform, and the one or more regions associated with the RF signal spikes are identified based on the converted multi-level RF pulse waveform, applying one or more data conversion operations The system according to claim 17, further comprising.

20. Identifying the one or more peaks in the detected multi-level RF pulse waveform involves providing a set of the sensor data and the RF pulsing parameters as an input to a machine learning model, wherein the machine learning model is trained to predict one or more regions of each multi-level RF pulse waveform associated with an RF signal spike, providing the set of the sensor data and the RF pulsing parameters, and obtaining one or more outputs of the machine learning model extracting RF signal spike data from the one or more obtained outputs, wherein the RF signal spike data indicates one or more regions of the detected multi-level RF pulse waveform that include RF signal spikes, and the one or more peaks in the detected multi-level RF pulse waveform correspond to the one or more regions indicated by the RF signal spike data that include the RF signal spikes, and extracting the RF signal spike data The system according to claim 16, comprising.

Images