Disturbance compensation for substrate processing recipes

Abstract

This provides a method for compensating for disturbances in substrate processing recipes. [Solution] The method determines disturbance data when it receives first sensor data associated with a first iteration of a first recipe operation of a substrate processing recipe. The disturbance data is the difference between the first sensor data and first setpoint data of the first recipe operation. The method also determines a first operating value associated with one or more components of the processing chamber, at least in part, based on the disturbance data. The operation of one or more components according to the first operating value compensates for the disturbance data. The method further causes the operation of one or more components based on the first operating value during subsequent iterations of the first recipe operation of the substrate processing recipe to compensate for the disturbance data.

Description

Technical Field

[0001] The present disclosure relates to disturbance compensation, and more particularly to disturbance compensation for substrate processing recipes.

Background Art

[0002] Manufacturing equipment produces products by performing operations of a product recipe. For example, substrate processing equipment produces substrates by performing recipe operations of a substrate processing recipe. A substrate processing recipe has a plurality of recipe operations. Some of the recipe operations are repeated in the substrate processing 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 is not an extensive overview of the present disclosure. This summary neither identifies key or important elements of the present disclosure nor defines the scope of particular implementations or the scope of the claims of the present disclosure. Its sole purpose is to present some concepts of the present disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0004] In one aspect of the present disclosure, a method includes receiving first sensor data associated with a first iteration of a first recipe operation of a substrate processing recipe. The method further includes determining disturbance data, wherein the disturbance data is a difference between the first sensor data and first setpoint data of the first recipe operation. The method further includes determining a first operating value associated with one or more components of a processing chamber, at least partially based on the disturbance data. Actuation of the one or more components according to the first operating value compensates for the disturbance data. The method further includes causing actuation of the one or more components based on the first operating value during subsequent iterations of the first recipe operation of the substrate processing recipe to compensate for the disturbance data.

[0005] In another aspect of the present disclosure, a non-temporary machine-readable storage medium for storing instructions, the instructions causing a processing device to receive first sensor data associated with a first iteration of a first recipe operation of a substrate processing recipe when executed. The processing device is to determine disturbance data, further determining disturbance data which is the difference between first sensor data and first setpoint data of a first recipe operation. The processing device is to further determine, at least in part, a first operating value associated with one or more components of a processing chamber based on the disturbance data. The operation of one or more components according to the first operating value compensates for the disturbance data. The processing device is to further cause the operation of one or more components based on the first operating value during subsequent iterations of the first recipe operation of the substrate processing recipe to compensate for the disturbance data.

[0006] In another aspect of this disclosure, the system includes a memory and a processing device coupled to the memory. The processing device is for receiving first sensor data associated with a first iteration of a first recipe operation of a substrate processing recipe. The processing device is for determining disturbance data, further determining disturbance data which is the difference between the first sensor data and first setpoint data of the first recipe operation. The processing device is for further determining first operating values ​​associated with one or more components of a processing chamber, at least in part on the disturbance data. The operation of one or more components according to the first operating values ​​compensates for the disturbance data. The processing device is for further causing operation of one or more components based on the first operating values ​​during subsequent iterations of the first recipe operation of the substrate processing recipe in order to compensate for the disturbance data.

[0007] This disclosure is not limited to but is illustrative, as shown in the attached drawings. [Brief explanation of the drawing]

[0008] [Figure 1] This block diagram shows an exemplary system architecture in several embodiments. [Figure 2A] This is a flowchart of a method associated with disturbance compensation for substrate processing recipes, according to several embodiments. [Figure 2B] This is a flowchart of a method associated with disturbance compensation for substrate processing recipes, according to several embodiments. [Figure 3A] This is a sequence chart associated with disturbance compensation for a substrate processing recipe, according to several embodiments. [Figure 3B] This is a sequence chart associated with disturbance compensation for a substrate processing recipe, according to several embodiments. [Figure 4] This is a block diagram showing a computer system in several embodiments. [Modes for carrying out the invention]

[0009] Techniques for disturbance compensation for substrate processing recipes (e.g., selective disturbance compensators for improved tracking performance that use performance-based selective active learning to remove unmodeled disturbances in volume to improve tracking performance) are described herein.

[0010] Manufacturing equipment produces products by performing the actions of a product recipe. For example, substrate processing equipment processes substrates (e.g., wafers, semiconductors, displays, etc.) by performing the recipe actions of a substrate processing recipe. A substrate processing recipe includes one or more recipe actions, such as transfer actions (e.g., a robot transporting the substrate to different locations), processing actions (e.g., processing the substrate in a processing chamber), and cleaning actions (e.g., cleaning the processing chamber after the processing actions). In some examples, multilayer features are fabricated on a substrate using a substrate processing recipe that includes a specific sequence of repeating recipe actions.

[0011] Different recipe operations will occur at specific recipe setpoints (e.g., pressure, temperature, etc.). For example, the recipe operation for depositing material on a substrate will occur at specific pressure and / or temperature. The difference between the recipe setpoint and the actual conditions is called a disturbance (for example, a pressure disturbance is the difference between the setpoint pressure value and the actual pressure value), and this causes the production of defective substrates.

[0012] In some examples, the recipe operation involves supplying a fluid in a gaseous state to a processing chamber and then applying radio frequency (RF) energy to the fluid to change it from a gaseous state to a plasma state (e.g., called plasma striking). When the fluid changes from a gaseous state to a plasma state, there is a dissociation of fluid molecules, which increases the pressure in the processing chamber. The transition of the fluid from a gaseous state to a plasma state is not instantaneous and takes time to stabilize (e.g., there are pockets of fluid in the partially changed state). Disturbances (e.g., actual pressure and temperature values ​​that do not meet the recipe setpoint values) cause the fluid to take longer to stabilize in the plasma state, leading to the production of defective substrates (e.g., variations in the thickness of the substrate layers).

[0013] Conventional systems determine the difference (e.g., disturbances) between the recipe setpoint and the actual conditions during each recipe operation and take reactive actions during each recipe operation to try to match the actual conditions to the recipe setpoint. Some disturbances change rapidly and are not modeled. The reactive actions of conventional systems do not take effect until the disturbance begins to adversely affect the formation of features on the substrate. Uncompensated disturbances can reduce the quality of the substrate being processed and decrease substrate-to-substrate consistency. For example, a process that changes a fluid from a gaseous state to a plasma state by applying RF energy causes a change in pressure in the processing chamber. Conventional systems attempt to react to the pressure change after part of the substrate processing has been performed, which causes the substrate to be processed in a non-uniform manner and have inconsistent features (e.g., variations in thickness).

[0014] The methods and systems disclosed herein provide disturbance compensation for substrate processing recipes (e.g., selective disturbance compensators for improved tracking performance during recipe operation of a substrate processing recipe).

[0015] The processing device receives first sensor data associated with a first iteration of a first recipe operation of a substrate processing recipe. The first iteration is the first execution of the first recipe operation. A substrate processing recipe comprises multiple recipe operations, some of which are repeated. For example, a substrate processing recipe may have multiple nitride deposition operations, multiple oxide deposition operations, multiple transition operations, and so on. In some embodiments, each recipe operation includes the operation of one or more components associated with the processing chamber. In some embodiments, the first recipe operation results in a change in the internal conditions of the processing chamber. For example, the first recipe operation includes the operation of a component for adjusting the RF energy supplied to the processing chamber, causing the plasma or gas in the chamber to react in a different way. In some embodiments, the first recipe operation includes the operation of a valve for adjusting the flow rate of gas to the processing chamber. In some embodiments, the first recipe operation includes the operation of a heater for heating one or more components associated with the processing chamber. In some embodiments, the first recipe operation results in a change in the internal pressure and / or temperature of the processing chamber. For example, an increase in RF energy supplied to the processing chamber can increase the pressure inside the chamber when the fluid in the chamber changes from a gaseous state to a plasma state.

[0016] In some embodiments, the first sensor data includes one or more of pressure data, temperature data, or other data associated with the processing chamber. The first sensor data tracks or indicates one or more conditions inside the processing chamber during the first recipe operation. In some embodiments, the processing device receives sensor data from one or more sensors disposed in the processing chamber and sensing one or more conditions inside the processing chamber.

[0017] After receiving the first sensor data, the processing device determines disturbance data. The disturbance data is the difference between the first sensor data and the first setpoint data for the first recipe operation. In some embodiments, the first setpoint data is the ideal condition that should exist in the processing chamber during the first recipe operation, and the first sensor data is the actual condition that exists in the processing chamber during the first recipe operation. For example, the first setpoint data may indicate that the internal pressure of the processing chamber should be at a predetermined pressure value for the duration of the first recipe operation, or, in another example, the first setpoint data may indicate that the internal pressure of the processing chamber should change from a first predetermined pressure value to a second predetermined pressure value during the first recipe operation. In some embodiments, the first setpoint data is the first pressure setpoint data. In some embodiments, the first setpoint data is the first temperature setpoint data. In another example, the first setpoint data may indicate that the internal temperature of the processing chamber should be at a predetermined temperature value, or that its internal temperature should change from a first predetermined temperature value to a second predetermined temperature value during the first recipe operation.

[0018] The processing device determines a first actuation value associated with one or more components of the processing chamber. In some embodiments, the components of the processing chamber include one or more of the following: heaters (e.g., heating a substrate, heating a susceptor on which the substrate is disposed), cooling components (e.g., a heat transfer fluid flowing through the susceptor on which the substrate is disposed), throttle valves (e.g., allowing fluid to exit the processing chamber), mass flow control valves (e.g., allowing fluid to enter the processing chamber), or RF generators (e.g., applying RF energy to the processing chamber to change the fluid from a gaseous state to a fluid state). In some embodiments, the first recipe operation includes an actuation value that causes the throttle valve to act, the heater to act, or the RF generator to act, etc. The first actuation value may be an adjustment value to the actuation value of the first recipe operation. The first actuation value is one or more inputs for one or more of the components that, when the components are actuated, at least partially reduce disturbances in the processing chamber, the disturbances are indicated by disturbance data. In some embodiments, the first operating value is associated with the input to the process chamber heater. In some embodiments, the first operating value is associated with the position of the process chamber throttle valve. In some embodiments, the first operating value is associated with the input to the process chamber mass flow controller. In some embodiments, the first operating value is associated with the input to the process chamber RF generator. For example, the processing device determines an operating value (e.g., input) for the process chamber heater to compensate for a temperature disturbance. In another example, the processing device determines an operating value (e.g., input) for the process chamber throttle valve to compensate for a pressure disturbance. In some embodiments, the processing device determines the position of the throttle valve to regulate the flow rate of gas from the processing chamber. In some embodiments, the processing device determines one or more positions (e.g., values) of the throttle valve to compensate for a pressure disturbance in the processing chamber indicated by disturbance data.For example, after an increase in RF input to the plasma in the processing chamber, the pressure in the chamber may be disturbed, and the processing device determines one or more positions (e.g., operating values) of the throttle valve to stabilize the disturbance. Stabilizing the disturbance causes the first sensor data to track the first setpoint data.

[0019] Based on a first activation value, the processing device triggers the activation of one or more components of the processing chamber during subsequent iterations of the first recipe operation of the substrate processing recipe. In some embodiments, the processing device signals one or more of the components based on the first activation value. In some embodiments, the processing device triggers the recipe to be updated based on the first activation value. In some embodiments, the first activation value is associated with the first recipe operation by an identifier, and the identifier is unique to the first recipe operation. In some embodiments, the first activation value is retrieved by the identifier for use with subsequent iterations of the first recipe operation. In some embodiments, the processing device triggers the throttle valve of the processing chamber to be adjusted (e.g., opened or closed) by an amount based on the first activation value. In some embodiments, the processing device triggers the heater of the processing chamber to be adjusted by a specified amount based on the first activation value. In some embodiments, the processing device triggers the power supplied to the heater to be activated based on the first activation value. Disturbance data (e.g., the difference between sensor data and setpoint data) is reduced by triggering the activation of one or more components during subsequent iterations of the first process recipe.

[0020] Aspects of the present disclosure result in technical advantages. The present disclosure anticipates and compensates for disturbances in a processing chamber for subsequent recipe operations as compared to conventional solutions that reactively attempt to compensate for disturbances. By anticipating and compensating for disturbances, the present disclosure has less impact on recipe operations as compared to conventional solutions. The present disclosure produces substrates that are of higher quality and more consistent as compared to substrates produced by conventional systems. Additionally, the present disclosure provides more accurate and precise substrate processing recipe operations by compensating for disturbances associated with the processing chamber, which leads to more consistent layer deposition on the substrate and enables more consistent and higher quality finished substrates as compared to conventional systems. The improved accuracy and precision reduces layer defects and layer roughness of the substrate by the present disclosure.

[0021] Some embodiments of the present disclosure are described with respect to compensating for processing chamber disturbances in a substrate processing system, but in some embodiments, the present disclosure is applicable to other systems such as manufacturing systems that perform operations over time.

[0022] FIG. 1 is a block diagram illustrating a system 100 (e.g., an exemplary system architecture, computing environment) according to some embodiments. The system 100 includes a client device 192, manufacturing equipment 184 (e.g., substrate processing equipment), sensors 186, a controller device 190 (e.g., a controller, server), and a data store 140.

[0023] The client device 192, the manufacturing equipment 184, the sensor 186, the controller device 190, and the data store 140 are connected to each other via a network 180. In some embodiments, the network 180 is a public network providing the client device 192 with access to the controller device 190, the data store 140, and other publicly available computing devices. In some embodiments, the network 180 is a private network providing the client device 192 with access to the manufacturing equipment 184, the sensor 186, the data store 140, and other privately available computing devices. The network 180 includes 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.

[0024] The controller device 190 (e.g., controller, server) includes one or more computing devices, such as a rack-mount server, router computer, server computer, personal computer, mainframe computer, laptop computer, tablet computer, desktop computer, graphics processing unit (GPU), and accelerator application-specific integrated circuit (ASIC) (e.g., tensor processing unit (TPU)). In some embodiments, the controller device 190 includes a disturbance compensation component 194 to cause the board to be processed based on operating values ​​176. In some embodiments, the disturbance compensation component 194 is used to perform one or more operations of methods 200A-200B in Figures 2A-2B or sequences 300A-300B in Figures 3A-3B. In some embodiments, the disturbance compensation component 194 includes a model-based controller. In some embodiments, the model of the model-based controller is a physics-based model. In some embodiments, the controller device 190 is a controller of a semiconductor processing system and is used to control manufacturing equipment 184. In some embodiments, the controller device 190 includes a software bridge.

[0025] Client device 192 includes computing devices such as personal computers (PCs), laptops, mobile phones, smartphones, tablet computers, netbook computers, network-connected televisions ("smart TVs"), network-connected media players (e.g., Blu-ray players), set-top boxes, over-the-top (OTT) streaming devices, operator boxes, and the like. In some embodiments, client device 192 displays a graphical user interface (GUI) to receive inputs and display outputs. In some embodiments, client device 192 includes a disturbance compensation component 194 to cause the substrate to be processed based on the operating value 176. In some embodiments, the disturbance compensation component 194 is used to perform one or more operations of the methods 200A-200B of FIGS. 2A-2B or the sequences 300A-300B of FIGS. 3A-3B. In some embodiments, client device 192 includes a software bridge.

[0026] In some embodiments, the disturbance compensation component 194 is a disturbance control component. The disturbance compensation component 194 receives sensor data 170 associated with the recipe operation 154 of the recipe 150 (e.g., a substrate processing recipe) (e.g., from a sensor 186, from a data store 140), determines disturbance data 174 (e.g., the difference between the sensor data 170 and the setpoint data 172 of the recipe operation 154), determines an actuation value 176 (e.g., associated with one or more components of the manufacturing equipment 184 to compensate for the disturbance data), and triggers an actuation of one or more components of the manufacturing equipment 184 during subsequent iterations of the first recipe operation. In some embodiments, the disturbance compensation component 194 triggers an actuation of one or more components by transmitting the actuation value 176 (e.g., to a data store 140, to a controller device 190, to a client device 192). In some embodiments, the controller device 190 triggers the substrate to be processed by the manufacturing equipment 184 based on the actuation value 176.

[0027] In some embodiments, the manufacturing equipment 184 (e.g., a cluster tool) is part of a substrate processing system (e.g., an integrated processing system). The manufacturing equipment 184 includes one or more of the following: enclosure systems (e.g., substrate carriers, front-opening unified pods (FOUPs), auto-teach FOUPs, process kit enclosure systems, substrate enclosure systems, cassettes, etc.), side storage pods (SSPs), aligner devices (e.g., aligner chambers), factory interfaces (e.g., equipment front-end modules (EFEMs)), load locks, transfer chambers, one or more processing chambers, robotic arms (e.g., positioned in the transfer chamber, positioned in the front interface, etc.). The enclosure systems, SSPs, and load locks are attached to the factory interface, and the robotic arms positioned in the factory interface are for transferring contents (e.g., substrates, process kit rings, carriers, verification wafers, etc.) between the enclosure systems, SSPs, load locks, and the factory interface. The aligner devices are positioned in the factory interface for aligning the contents. The load lock and processing chamber are attached to the transfer chamber, and a robotic arm positioned within the transfer chamber is used to transfer contents (e.g., substrates, process kit rings, carriers, verification wafers, etc.) between the load lock, processing chamber, and transfer chamber.

[0028] The manufacturing equipment 184 includes one or more processing chambers for producing a substrate based on a recipe 150 and operating values. The recipe 150 includes a set of recipe operations 154 for producing features on the substrate (e.g., depositing layers, creating multilayer features).

[0029] Sensor 186 provides sensor data 170 (e.g., sensor values, trace data) associated with manufacturing equipment 184 (e.g., associated with manufacturing equipment 184 producing substrates). Manufacturing equipment 184 produces substrates according to recipe 150. Sensor data 170 is received from different sensors 186 over time periods (e.g., corresponding to at least part of recipe 150 or recipe operation 154).

[0030] The data store 140 is memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or another type of component or device capable of storing data. The data store 140 includes multiple storage components (e.g., multiple drives or multiple databases) across multiple computing devices (e.g., multiple server computers). The data store 140 stores the recipe 150, sensor data 170, setpoint data 172, disturbance data 174, and operating values ​​176.

[0031] Recipe 150 includes recipe operations 154. Recipe operations 154 include processing operations (e.g., chamber operations) and cleaning operations. Recipe 150 includes a set of instructions to be performed by components of the manufacturing equipment 184 to process or manufacture a product. Recipe operations 154 are a subset of instructions for recipe 150. In some embodiments, recipe 150 includes multiple identical recipe operations 154. In some embodiments, recipe operation 154 is one of several different manufacturing or processing operations, including nitride deposition operations, oxide deposition operations, transient operations (e.g., changes in setpoints within the chamber), or cleaning operations. In some embodiments, recipe 150 is provided to a client device 192 (e.g., via user input). Recipe 150 describes what recipe operations the substrate will undergo at different stages and the processes to be performed in each chamber.

[0032] Sensor data 170 (e.g., sensor values, trace data) is received from a sensor 186 associated with the manufacturing equipment 184 (e.g., associated with the manufacturing equipment 184 in producing the substrate). Sensor data 170 may be received from sensor 186 during the execution of recipe operation 154.

[0033] The setpoint data 172 includes processing chamber setpoints for a recipe operation 154 associated with the substrate processing system. The setpoint data 172 are predetermined desired conditions for the process within the processing chamber, such as an ideal pressure value or an ideal temperature value during the recipe operation. For example, the recipe operation 154 may specify that the recipe operation should be performed at a predetermined pressure or temperature. The setpoint data 172 indicates predetermined desired conditions under which the recipe operation 154 should be performed. In some embodiments, the setpoint data 172 includes pressure setpoint data. In some embodiments, the setpoint data 172 includes temperature setpoint data. Similarly, the sensor data 170 indicates the actual conditions within the processing chamber during the recipe operation 154, such as an actual pressure value or an actual temperature value.

[0034] The disturbance data 174 includes the difference between the setpoint data 172 associated with the recipe operation 154 and the sensor data 170 received during the execution of the recipe operation 154. In some embodiments, the disturbance data 174 includes multiple values ​​that reflect the difference between the setpoint data 172 and the sensor data 170 over time. In some embodiments, the disturbance data 174 is a curve that reflects a comparison between a curve fit to the setpoint data 172 and a curve fit to the sensor data 170.

[0035] The operating value 176 is a value determined by the disturbance compensation component 194 to compensate for disturbances indicated by disturbance data 174 during recipe operation 154. The client device 192 generates the operating value 176 for disturbance compensation (for example, based on setpoint data 172 and disturbance data 174), thereby allowing the substrate to be processed by the manufacturing equipment 184 in a consistent manner with improved throughput. The operating value 176 may be associated with several components of the manufacturing equipment 184 and may be used during subsequent iterations of the same recipe operation 154 by the manufacturing equipment 184. The use of the operating value 176 during subsequent iterations of the same recipe operation 154 reduces the difference between setpoint data 172 and sensor data 170.

[0036] In some embodiments, the data store 140 stores sensor data 170 from the sensor 186. The sensor data 170 includes one or more values ​​from among temperature (e.g., heater temperature), spacing (SP), pressure, high frequency radio frequency (HFRF), low frequency radio frequency (LFRF), high frequency (RF) power, electrostatic chuck (ESC) voltage, current, flow, power, voltage, etc. In some embodiments, the sensor data 170 is associated with or indicates manufacturing parameters, such as hardware parameters of the manufacturing equipment (e.g., settings or components of the manufacturing equipment 184 (e.g., size, type, etc.)) or process parameters. The sensor data is provided while the manufacturing equipment 184 is performing a manufacturing process (e.g., recipe operation 154, equipment readings as the product is being processed). In some embodiments, the sensor data 170 is different for each substrate and / or layer.

[0037] In some embodiments, sensor data 170 and / or disturbance data 174 are used to determine whether the recipe 150 should be updated (for example, to improve the quality of the substrate, the health of the manufacturing equipment 184, energy usage, etc.).

[0038] In some embodiments, the functions of the client device 192 and the controller device 190 are provided by fewer machines. In some embodiments, the client device 192 and the controller device 190 are integrated into a single machine.

[0039] In some embodiments, one or more functions described as being performed by the client device 192 may also be performed on the controller device 190, where appropriate. In some embodiments, one or more functions described as being performed by the controller device 190 may also be performed on the client device 192, where appropriate. Furthermore, functions that are designated as belonging to a particular component may be performed by different or multiple components working together. For example, in some embodiments, the controller device 190 determines the operating value 176, and in some embodiments, the client device 192 determines the operating value 176.

[0040] Furthermore, the functionality of a particular component may be performed by different or multiple components working together. In some embodiments, the controller device 190 is accessed as a service provided to other systems or devices through a suitable application programming interface (API).

[0041] In some embodiments, “User” is represented as a single individual. However, other embodiments of this disclosure include “User” being an entity controlled by multiple users and / or automated sources. For example, a set of individual users federated as a group of administrators may be considered “User.”

[0042] While a portion of this disclosure refers to causing a substrate to be processed (for example, based on operating values ​​176) via substrate processing in a substrate chamber of a substrate processing system, in some embodiments, this disclosure generally applies to performing other processes (for example, via a manufacturing system) based on setpoint data and / or disturbance data.

[0043] Figures 2A and 2B are flowcharts of methods 200A to 200B associated with disturbance compensation for board processing recipes, according to several embodiments. Methods 200A to 200B are implemented by processing logic, including hardware (e.g., circuits, dedicated logic, programmable logic, microcode, processing devices, etc.), software (e.g., instructions running on processing devices, general-purpose computer systems, or dedicated machines), firmware, microcode, or a combination thereof. In some embodiments, methods 200A to 200B are implemented in part by a controller device 190 (e.g., disturbance compensation component 194). In some embodiments, methods 200A to 200B are implemented in part by a client device 192 (e.g., disturbance compensation component 194). In some embodiments, a non-temporary computer-readable storage medium stores instructions, and when the instructions are executed by a processing device (e.g., a controller device 190, a client device 192, etc.), the processing device implements one or more of methods 200A to 200B.

[0044] For the sake of simplicity, methods 200A–200B are presented and described as a series of operations. However, the operations according to this disclosure may be performed in various orders and / or simultaneously, as well as in conjunction with other operations not presented and described herein. Furthermore, in some embodiments, not all illustrated operations may be performed to implement methods 200A–200B according to the disclosed subject matter. Furthermore, those skilled in the art will understand and acknowledge that methods 200A–200B may alternatively be represented as a series of interrelated states via a state diagram or events.

[0045] Figure 2A is a flowchart of Method 200A (e.g., a process sequence for determining and compensating for disturbances in a substrate processing recipe) for substrate processing equipment (e.g., a processing chamber, manufacturing equipment 184 in Figure 1) according to several embodiments.

[0046] In block 202 of method 200A, the processing logic receives first sensor data (e.g., sensor data 170 in Figure 1) associated with the first iteration of a first recipe operation (e.g., recipe operation 154 in Figure 1). The first recipe operation is one of several recipe operations in a substrate processing recipe. The first recipe operation will be repeated multiple times in the substrate processing recipe. In some embodiments, in block 202, the first iteration of the first recipe operation is the first operation (e.g., iteration) of the first recipe operation, which will later be followed by subsequent iterations of the first recipe operation. In some embodiments, the processing logic is configured to receive second sensor data associated with the subsequent iteration during the subsequent iteration. In some embodiments, in block 202, the first sensor data includes one or more of pressure data, temperature data, or flow data.

[0047] In some embodiments, the first recipe operation is an oxide layer deposition operation, a nitride layer deposition operation, a transition operation (e.g., between deposition operations), a purging operation, etc.

[0048] In some embodiments, the first sensor data includes one or more of the following: pressure data, RF data, flow rate data, temperature data, fluid conductance data, etc.

[0049] In block 204, the processing logic determines disturbance data (e.g., disturbance data 174 in Figure 1). The disturbance data is the difference between the first sensor data in block 202 and the setpoint data associated with the recipe operation 154 in block 202 (e.g., setpoint data 172 in Figure 1). In some embodiments, the setpoint data is of the same type as the sensor data (e.g., temperature data or pressure data). The first sensor data is associated with one or more actual conditions in the processing chamber. The setpoint data includes one or more ideal conditions for the processing chamber associated with the first recipe operation. In some embodiments, the setpoint data adjusts from the beginning to the end of the recipe operation (e.g., transient). The disturbance data may arise from sensor data in the processing chamber that indicates an unbalanced change in the state in the chamber (e.g., disturbance).

[0050] In block 206, the processing logic determines a first actuation value associated with one or more components of the processing chamber based on disturbance data (e.g., disturbance data 174 in Figure 1). The operation of one or more components according to the first actuation value compensates for the disturbance data. In some embodiments, the processing logic determines the first actuation value by using a processing chamber model, iterative learning control, or lookup table. In some embodiments, the processing chamber model is a physics-based model. In some embodiments, the first actuation value is associated with a first recipe operation by an identifier. The first actuation value is based on disturbance data. In some embodiments, the first actuation value is a value at which sensor data approaches setpoint data when one or more components of the processing chamber are actuated according to the first actuation value. In some embodiments, the first actuation value is associated with a throttle valve of the processing chamber, a heater of the processing chamber, or a mass flow controller of the processing chamber. In some embodiments, the actuation value is a set of values ​​(e.g., transient data) corresponding to a set of component operations. In some embodiments, the operating values ​​indicate that the throttle valve of the processing chamber should be opened and / or closed in a particular manner to compensate for pressure disturbances within the processing chamber. For example, a model of the processing chamber calculates an equivalent flow corresponding to the pressure disturbance within the processing chamber, and the throttle valve of the processing chamber is opened by an amount corresponding to the equivalent flow. In some embodiments, the throttle valve is opened to adjust the flow rate of gas from the processing chamber to approximately equal the equivalent flow rate.

[0051] In block 208, the processing logic triggers the activation of one or more components of the processing chamber based on a first activation value during subsequent iterations of the first recipe operation to compensate for disturbance data. In some embodiments, the first activation value is retrieved by identifier for subsequent iterations of the first recipe operation. Subsequent iterations of the first recipe operation occur during subsequent operations of the first recipe operation (e.g., during processing of the same board, during processing of subsequent boards). In some embodiments, the processing logic receives second sensor data associated with the subsequent iterations. In some embodiments, the difference between the sensor data and the setpoint data is reduced, which allows for improved inter-board consistency and faster stabilization of transient conditions, leading to increased throughput. For example, after a change in setpoint data in the processing chamber, the reduction in the difference between the sensor data and the setpoint data causes the sensor data to reach the new setpoint data more quickly.

[0052] In some embodiments, during subsequent iterations of the first recipe operation, the processing logic receives second sensor data associated with the subsequent iteration. In some embodiments, the processing logic then determines second disturbance data based on the difference between a first setpoint value and the second sensor data. In some embodiments, the first operating value is updated by the processing logic based on the second disturbance data for use in further iterations of the first recipe operation (for example, the second operating value is determined based on the second disturbance data for use in further iterations of the first recipe operation). For example, if the second disturbance data meets a predetermined threshold, the first operating value is not updated, but if the second disturbance data does not meet the predetermined threshold, the first operating value is updated.

[0053] In some examples, in block 202, a first recipe operation includes applying RF energy to the fluid in the processing chamber (e.g., an RF strike) to cause the fluid to change from a gaseous state to a plasma state, and the first sensor data is pressure data inside the processing chamber (e.g., provided by a pressure sensor disposed inside the processing chamber) associated with the RF strike. The first recipe operation may be associated with (e.g., include) first setpoint data, which includes a pressure setpoint (e.g., one or more ideal pressure values ​​for carrying out the first recipe operation). In block 204, disturbance data (e.g., a difference in pressure data) may be the difference between the first sensor data and the pressure setpoint. In block 206, the first actuation value may be an actuation value for a throttle valve in the processing chamber to allow more or less fluid flow from the processing chamber (e.g., to increase or decrease the pressure value inside the processing chamber). The first actuation value is determined based on the disturbance data.

[0054] In some embodiments, a model (e.g., a physics-based model, a dynamic pressure model) illustrates the relationship between manufacturing parameters and the operation of components in a processing chamber. In some examples, a model (e.g., a physics-based model) illustrates the relationship between pressure values ​​in the processing chamber (e.g., during recipe operation) and the operation of a throttle valve. The processing device may provide disturbance data (e.g., a difference in pressure data) as input to the model (e.g., a physics-based model) and receive an output of a first operating value (e.g., indicating how the throttle valve should be operated to match the pressure value to a pressure setpoint).

[0055] In some embodiments, a lookup table shows the relationship between a manufacturing parameter (e.g., pressure value) and the operation of a component of the processing chamber (e.g., the operation of a throttle valve). The processing device may use the lookup table to determine that a first operating value corresponds to disturbance data.

[0056] In some embodiments, iterative learning control is used to determine a first operating value. Sensor data from sensors associated with the processing chamber (e.g., during the execution of a recipe operation) at different operating values ​​of the components of the processing chamber may be determined. For example, pressure values ​​at different positions of the throttle valve (e.g., closed, 25% open, 50% open, 75% open, 100% open) may be determined. In some embodiments, changes in pressure values ​​may be determined for different changes in the position of the throttle valve (e.g., a 10% decrease in pressure due to opening the throttle valve an additional 25%). The relationship between the difference in sensor data and the operating valve may be determined iteratively through test runs or actual recipe operations. In some embodiments, one or more operations in Figure 2A are performed until the disturbance data falls below a threshold.

[0057] In some embodiments, the disturbance data is the difference in pressure data, and the operating value is the valve operating value (for example, a throttle valve for adjusting the amount of fluid leaving the processing chamber, or a mass flow control valve for adjusting the amount of fluid entering the processing chamber).

[0058] In some embodiments, the disturbance data is the difference in temperature data, and the operating value is the power supplied to the heater. In some embodiments, the disturbance data is the difference in temperature data, and the operating value is the cooling or heating of the heat transfer fluid flowing through the susceptor on which the substrate is located.

[0059] In some embodiments, the disturbance data is the difference in temperature data, and the operating value is the cooling or heating of the heat transfer fluid flowing through the susceptor on which the substrate is placed.

[0060] In some embodiments, disturbance data is associated with the RF energy applied to the fluid in the processing chamber (e.g., pressure data, chemical composition data, energy data, temperature data), and the operating value adjusts the amount of RF energy applied to the processing chamber (e.g., via a variable capacitor to match the system impedance, an RF emitter, etc.).

[0061] In block 206, the processing device may determine multiple operating values ​​associated with different components, and in block 208, the processing device may trigger the operation of multiple components based on each of the different operating values.

[0062] In some embodiments, disturbance data is the difference between chemical composition data from an emission spectroscopy (OES) sensor (for example, data about the actual plasma state in the processing chamber). The processing device may operate one or more of the following based on corresponding operating values ​​derived from the chemical composition data: throttle valves, mass flow control valves, heaters, RF emitters, etc.

[0063] In some embodiments, sensor data, setpoint data, and disturbance data are sets of values ​​from a first time point (e.g., the start of the recipe operation) to a second time point (e.g., the end of the recipe operation). Actuation values ​​may be a set of actuation values ​​to be used between the first and second time points in subsequent iterations of the recipe operation (e.g., different positions for the throttle valve). Triggering the operation of one or more components based on the set actuation values ​​may cause the components to be actuated over time (e.g., gradually adjusting the throttle valve).

[0064] In some embodiments, the processing device associates an identifier with a first recipe operation that is to be associated with a first operating value. The processing device causes subsequent iterations of the first recipe operation to be performed based on the first operating value by using the identifier to retrieve both the recipe operation and the first operating value.

[0065] In some embodiments, the processing device updates a first recipe operation based on a first operating value.

[0066] In some embodiments, Method 200A is performed separately for each processing chamber (for example, to account for differences between chambers). In some embodiments, Method 200A is performed separately for each recipe of the recipe operation. In some embodiments, Method 200A is restarted periodically for the same processing chamber (for example, to account for changes in the processing chamber over time). In some embodiments, the operating values ​​determined via Method 200A for a recipe performed in a processing chamber are used as starting points for one or more of other recipes, other processing chambers, or other time points. Method 200A may be restarted to update the operating values ​​and / or recipe operation.

[0067] In some embodiments, Method 200A is performed to determine the trigger values ​​for the recipe operation of a recipe. When Method 200A is performed again, the processing device may determine that one or more trigger values ​​should be adjusted by a certain value or percentage, or it may adjust all of the trigger values ​​by the same value or percentage.

[0068] Figure 2B is a flowchart of Method 200B associated with disturbance compensation for a substrate processing recipe, according to several embodiments. In some embodiments, Method 200B is used to determine disturbance data (e.g., block 204 in Figure 2A) and to determine a first operating value associated with one or more components of the processing chamber (e.g., block 206 in Figure 2A).

[0069] In block 220, the processing logic identifies data (e.g., sensor data, disturbance data). In some embodiments, the data is sensor data associated with the iteration of the recipe operation (e.g., block 202 in Figure 2A). In some embodiments, the data is disturbance data associated with the iteration of the recipe operation (e.g., block 204 in Figure 2A). The disturbance data is the difference between the setpoint data of the recipe operation (e.g., ideal data) and the sensor data associated with the iteration of the recipe operation (e.g., actual data).

[0070] In some embodiments, the processing logic performs one or more post-processing operations on the identified data. In block 222, the processing logic performs moving average filtering of the data. In some embodiments, moving average filtering of the data includes creating a set of averages of different subsets of the complete dataset. Performing moving average filtering of the data smooths or reduces noise in the data. In some embodiments, moving average filtering is performed by one or more of a simple moving average filter, a cumulative moving average filter, or a weighted moving average filter.

[0071] In block 224, the processing logic performs time-shift compensation for the data. Due to the nature of the moving average filter, if the output is shifted backward in time, time-shift compensation for the data should be performed to make the data output from the moving average filter correspond to the real-time data. In some embodiments, the data output from the moving average filter is shifted forward in time so that the data corresponds to the original data.

[0072] In block 226, the processing logic performs the updating of data compensator parameters. In some embodiments, the compensator parameters include the time (e.g., duration) and amplitude of the data (e.g., sensor data, disturbance data).

[0073] In some embodiments, the activation value (e.g., activation value 176 in Figure 1) is determined at least in part on data updated by Method 200B (e.g., sensor data, disturbance data). The activation value may be used to trigger the activation of one or more components of the processing chamber during subsequent iterations of the recipe operation (e.g., so that a disturbance is reduced by the components of the processing chamber during subsequent iterations of the recipe operation).

[0074] Figures 3A and 3B show sequence flowcharts associated with disturbance compensation for substrate processing recipes according to several embodiments.

[0075] Figure 3A shows flow 300A for disturbance compensation associated with substrate processing recipes in several embodiments.

[0076] Blocks 310-338 may be implemented as part of learning operation 350.

[0077] In block 310, a first iteration of the first recipe operation is performed in the processing chamber. In some embodiments, the first recipe operation is one of the following: a nitride deposition operation, an oxide deposition operation, a transient operation, etc.

[0078] In block 322, sensor data is received by a model-based controller 320 (running on, for example, a real-time server, controller device 190 in Figure 1, or client device 192 in Figure 1) from one or more sensors associated with the processing chamber during the first recipe operation. In some embodiments, the model-based controller 320 utilizes a physically based model of the processing chamber. In some embodiments, the sensor data includes one or more of pressure data, temperature data, or flow data.

[0079] In some embodiments, the model-based controller is part of a disturbance compensator component (e.g., disturbance compensator component 194 in Figure 1) of a client device (e.g., client device 192 in Figure 1) or a controller device (e.g., controller device 190 in Figure 1). In block 322, sensor data may be recorded in memory (e.g., data store 140 in Figure 1).

[0080] In block 324, the processing logic determines the disturbance data (for example, block 204 in Figure 2A). The disturbance data is the difference between the sensor data and the setpoint data for the first recipe operation.

[0081] In some embodiments, the disturbance data then undergoes post-processing 330 (e.g., blocks 332-338). Post-processing is performed as part of a learning operation 350. In some embodiments, post-processing includes multiple operations. In block 332 (e.g., block 222 in Figure 2B), the disturbance data is passed through a moving average filter. The moving average filter creates a series of averages of the complete dataset and corresponding different subsets of data. The moving average filter reduces noise in the disturbance data. As an inherent consequence of the moving average filter, the data is shifted in time. In block 334 (e.g., block 224 in Figure 2B), a time-shift operation is performed on the data to shift it in time so that it correlates with sensor data from a first recipe operation (e.g., to correlate with real-time data). In block 336, the processing logic performs an update to the compensator parameters (e.g., the compensator parameters in block 226 in Figure 2B).

[0082] In block 338, the processing logic determines an actuation value (e.g., actuation value 176 in Figure 1) based on disturbance data that has passed through post-processing 330. The actuation value is a value that reduces the difference between sensor data and setpoint data for a recipe operation when the components of the processing chamber are actuated based on that actuation value. The data is then supplied to a controller (e.g., a compensation controller) in block 340. In some embodiments, the controller in block 340 is a model-based controller (e.g., a compensation controller) that utilizes the compensation value. The compensation controller utilizes the actuation value in conjunction with data from the first recipe operation to perform real-time compensation 360 during subsequent iterations of the first recipe operation. Disturbances sensed and learned during the learning operation 350 are compensated during real-time compensation 360 (e.g., subsequent iterations of the first recipe operation). Any further operations of the recipe operation are compensated using the compensation controller.

[0083] Figure 3B shows a flow 300B for updating disturbance compensation associated with the recipe operation of a substrate processing recipe in several embodiments. Sensor data 321 is supplied to a model-based controller 320. In some embodiments, the model-based controller 320 is a disturbance compensation component (e.g., disturbance compensation component 194 in Figure 1) of a client device (e.g., client device 192 in Figure 1) or a controller device (e.g., controller device 190 in Figure 1). The sensor data correlates to a first recipe operation of block 310. In some embodiments, the recipe operation of block 310 is a change in the RF input to the processing chamber. When the recipe operation of block 310 is performed, a software bridge 370 (e.g., running on a front-end server) applies stored operating values ​​in block 371. In some embodiments, the software bridge is a component of the disturbance compensation component of a client device (e.g., client device 192 in Figure 1) or a controller device (e.g., controller device 190 in Figure 1). In some embodiments, the stored operating value is the operating value 176 in Figure 1, which is stored in the data store 140 in Figure 1.

[0084] In block 323, the model-based controller 320 receives sensor data 321 associated with the execution of a recipe operation in block 310 and the activation of one or more components based on activation values ​​(for example, simultaneous execution of a recipe operation and the activation of components based on activation values).

[0085] In block 325, the model-based controller determines whether the sensor data (associated with the execution of, for example, the execution of a recipe operation and the execution of components based on the execution value) satisfies the setpoint data for the recipe operation. If the setpoint data is satisfied, the flow proceeds to block 327, and there is no update to the execution value. If the setpoint data is not satisfied, the flow proceeds to block 329, and the model-based controller 320 performs an update to the execution value. In some embodiments, method 200A shown in Figure 2A is performed to update the execution value.

[0086] In some embodiments, sensor data and / or disturbance data (e.g., the difference between sensor data and setpoint data) are compared to a threshold (e.g., block 327 or block 329) to determine whether an update is required. In some embodiments, the threshold is a predetermined tolerance for the setpoint data or disturbance data. In some embodiments, a sum-squared error is performed (e.g., for disturbance data), and the sum-squared error is compared to the threshold. In some embodiments, the error is calculated at different points in time (e.g., sample measurement points) (e.g., disturbance data, the difference between sensor data and setpoint data). The error may be squared, summed, and then compared to a threshold error value.

[0087] The updated operating values ​​are sent to the software bridge for use in block 371 for subsequent iterations of the recipe operation.

[0088] Figure 4 is a block diagram showing a computer system 400 according to several embodiments. In some embodiments, the computer system 400 is a client device 192. In some embodiments, the computer system 400 is a controller device 190 (for example, a server).

[0089] In some embodiments, the computer system 400 is connected to other computer systems (for example, via a network such as a local area network (LAN), intranet, extranet, or the Internet). The computer system 400 operates as a server computer 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, the computer system 400 is provided by a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), cellular telephone, web appliance, server, network router, switch or bridge, or any device capable of executing a set of instructions (sequential or otherwise) specifying the actions to be taken by that device. Furthermore, the term “computer” includes any set of computers that individually or collectively execute a set of instructions (or sets of instructions) to perform one or more of the methods described herein.

[0090] In some embodiments, the computer system 400 includes processing devices 402, volatile memory 404 (e.g., random access memory (RAM)), non-volatile memory 406 (e.g., read-only memory (ROM) or electrically erasable programmable ROM (EEPROM)), and a data storage device 416, all of which communicate with each other via a bus 408.

[0091] In some embodiments, the processing device 402 is provided by one or more processors, such as a general-purpose processor (e.g., a composite 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 types of instruction sets), or a dedicated processor (e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), or a network processor). In some embodiments, the processing device 402 is provided by one or more of the following: a single processor, multiple processors, a single processor having multiple processing cores, etc.

[0092] In some embodiments, the computer system 400 further includes a network interface device 422 (for example, coupled to a network 474). In some embodiments, the computer system 400 includes one or more input / output (I / O) devices. In some embodiments, the computer system 400 also includes a video display unit 410 (for example, a liquid crystal display (LCD)), an alphanumeric input device 412 (for example, a keyboard), a cursor control device 414 (for example, a mouse), and / or a signal generation device 420.

[0093] In some implementations, the data storage device 418 (e.g., disk drive storage, fixed storage devices and / or removable storage devices, fixed disk drives, removable memory cards, optical storage, network-attached storage (NAS), and / or storage area networks (SANs)) includes a non-temporary computer-readable storage medium 424 that stores instructions 426 for encoding one or more of the methods or functions described herein, including instructions for encoding the components of Figure 1 (e.g., disturbance compensation component 194), and for implementing the methods described herein.

[0094] In some embodiments, the instruction 426 also resides entirely or partially in the volatile memory 404 and / or processing device 402 during its execution by the computer system 400, and therefore, in some embodiments, the volatile memory 404 and processing device 402 also constitute a machine-readable storage medium.

[0095] Although computer-readable storage medium 424 is shown as a single medium in the exemplary examples, the term “computer-readable storage medium” includes a single or multiple mediums that store one or more sets of executable instructions (e.g., a centralized or distributed database, and / or associated caches and servers). The term “computer-readable storage medium” also includes any tangible medium capable of storing or encoding a set of instructions for a computer to execute, causing the computer to perform one or more of the methods described herein. The term “computer-readable storage medium” includes, but is not limited to, solid memory, optical media, and magnetic media.

[0096] 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 circuits within a hardware device. Furthermore, the methods, components, and features are implemented by any combination of hardware devices and computer program components, or by computer programs.

[0097] Unless otherwise specified, terms such as “identify,” “determine,” “cause,” “receive,” “generate,” “implement,” and “update” refer to actions and processes performed or implemented by a computer system that manipulate data represented as physical (electronic) quantities in computer system registers and memory, and convert that data into other data similarly represented as physical quantities in computer system memory or registers, or other such information storage, transmission, or display devices. Furthermore, terms such as “first,” “second,” “third,” and “fourth” as used herein are intended as labels to distinguish different elements from each other and do not imply any numerical order.

[0098] The examples described herein also relate to apparatus for carrying out the methods described herein. In some embodiments, the apparatus is constructed specifically for carrying out 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 in a computer-readable tangible storage medium.

[0099] The methods and illustrative examples described herein are not inherently related to any particular computer or other device. Various general-purpose systems may be used in accordance with the teachings described herein, or in some embodiments it may be convenient to construct more specialized devices to carry out the methods and / or each of their individual functions, routines, subroutines, or operations described herein. Examples of structures for various such systems are described above.

[0100] The above description is illustrative and not limiting. While this disclosure has been described with reference to certain exemplary examples and implementations, it should be acknowledged that this disclosure is not limited to the examples and implementations described. The scope of this disclosure should be determined with reference to the following claims, along with the entire scope of the equivalent for which the claims are granted.

Claims

1. Receiving first sensor data associated with the first iteration of the first recipe operation of the substrate processing recipe, Determining disturbance data, wherein the disturbance data is the difference between the first sensor data and the first setpoint data of the first recipe operation. Determining a first operating value associated with one or more components of a processing chamber, at least in part, based on the disturbance data, wherein the operation of the one or more components according to the first operating value compensates for the disturbance data. In order to compensate for the disturbance data, during subsequent iterations of the first recipe operation of the substrate processing recipe, the operation of one or more components based on the first operating value is triggered. Methods that include...

2. The method according to claim 1, wherein the first iteration of the first recipe operation is associated with processing a substrate in the processing chamber, and the subsequent iterations of the first recipe operation are associated with further processing the substrate in the processing chamber.

3. The method according to claim 1, wherein the first sensor data includes pressure data, the first setpoint data includes first pressure setpoint data, and the operation of one or more components includes, based on the first operating value, operating a throttle valve of the processing chamber to adjust the flow rate from the processing chamber so that the processing chamber satisfies the first pressure setpoint data.

4. The method according to claim 1, wherein the first sensor data includes temperature data, the first setpoint data includes first temperature setpoint data, and the operation of one or more components includes, based on the first operating value, activating power supplied to a heater of the processing chamber to adjust the temperature in the processing chamber so that the processing chamber satisfies the first temperature setpoint data.

5. Receiving second sensor data associated with the subsequent iteration of the first recipe operation, Determining a second disturbance data, wherein the second disturbance data is the difference between the second sensor data and the first setpoint data. In response to the second disturbance data satisfying a threshold, a second operating value is determined based on the second disturbance data for use with further iterations of the first recipe operation. The method according to claim 1, further comprising:

6. Moving average filtering of the first sensor data or the disturbance data, Time shift compensation for the first sensor data or the disturbance data, or Updating the compensator parameters for the first sensor data or the disturbance data. The method according to claim 1, further comprising carrying out one or more of the following.

7. The method according to claim 1, wherein determining the first operating value includes providing the disturbance data to a physics-based model and receiving an output from the physics-based model indicating the first operating value.

8. The method according to claim 1, wherein triggering the operation of one or more components includes associating the first operation value with an identifier of the first recipe operation, and the first operation value is retrieved for use with the subsequent iterations of the first recipe operation.

9. A non-temporary machine-readable storage medium for storing instructions, wherein when an instruction is executed, a processing device... Receiving first sensor data associated with the first iteration of the first recipe operation of the substrate processing recipe, Determining disturbance data, wherein the disturbance data is the difference between the first sensor data and the first setpoint data of the first recipe operation. Determining a first operating value associated with one or more components of a processing chamber, at least in part, based on the disturbance data, wherein the operation of the one or more components according to the first operating value compensates for the disturbance data. In order to compensate for the disturbance data, during subsequent iterations of the first recipe operation of the substrate processing recipe, the operation of one or more components based on the first operating value is triggered. A non-temporary, machine-readable storage medium that causes this to happen.

10. The non-temporary machine-readable storage medium according to claim 9, wherein the first iteration of the first recipe operation is associated with processing a substrate in the processing chamber, and the subsequent iterations of the first recipe operation are associated with further processing the substrate in the processing chamber.

11. The processing device is Receiving second sensor data associated with the subsequent iteration of the first recipe operation, Determining a second disturbance data, wherein the second disturbance data is the difference between the second sensor data and the first setpoint data. In response to the second disturbance data satisfying a threshold, a second operating value is determined based on the second disturbance data for use with further iterations of the first recipe operation. A non-temporary machine-readable storage medium according to claim 9, for further purposes.

12. The aforementioned processing device Moving average filtering of the first sensor data or the disturbance data, Time shift compensation for the first sensor data or the disturbance data, or Updating the compensator parameters for the first sensor data or the disturbance data. A non-temporary machine-readable storage medium according to claim 9, further for carrying out one or more of the above.

13. A non-temporary machine-readable storage medium according to claim 9, wherein the processing device is for providing the disturbance data to a physics-based model and receiving an output from the physics-based model indicating the first operating value, in order to determine the first operating value.

14. The non-temporary machine-readable storage medium according to claim 9, wherein the processing device associates the first activation value with an identifier of the first recipe operation in order to trigger the operation of one or more of the components, and the first activation value is retrieved for use with the subsequent iterations of the first recipe operation.

15. Memory and A processing device coupled to the memory and A system comprising, the processing device, Receiving first sensor data associated with the first iteration of the first recipe operation of the substrate processing recipe, Determining disturbance data, wherein the disturbance data is the difference between the first sensor data and the first setpoint data of the first recipe operation. Determining a first operating value associated with one or more components of a processing chamber, at least in part, based on the disturbance data, wherein the operation of the one or more components according to the first operating value compensates for the disturbance data. In order to compensate for the disturbance data, during subsequent iterations of the first recipe operation of the substrate processing recipe, the operation of one or more components based on the first operating value is triggered. A system designed to perform a specific task.

16. The system according to claim 15, wherein the first iteration of the first recipe operation is associated with processing a substrate in the processing chamber, and the subsequent iterations of the first recipe operation are associated with further processing the substrate in the processing chamber.

17. The processing device is Receiving second sensor data associated with the subsequent iteration of the first recipe operation, Determining a second disturbance data, wherein the second disturbance data is the difference between the second sensor data and the first setpoint data. In response to the second disturbance data satisfying a threshold, a second operating value is determined based on the second disturbance data for use with further iterations of the first recipe operation. The system according to claim 15, for further carrying out the above.

18. The aforementioned processing device Moving average filtering of the first sensor data or the disturbance data, Time shift compensation for the first sensor data or the disturbance data, or Updating the first sensor data or the compensator parameters of the difference. The system according to claim 15, further for carrying out one or more of the following.

19. The system according to claim 15, wherein the processing device provides the disturbance data to a physics-based model and receives an output from the physics-based model indicating the first operating value in order to determine the first operating value.

20. The system according to claim 15, wherein the processing device associates the first activation value with an identifier of the first recipe operation in order to trigger the operation of the one or more components, and the first activation value is retrieved for use with the subsequent iterations of the first recipe operation.

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