Method for early thermal performance assessment of a heater component

Abstract

In various forms, methods and systems are provided for evaluating the performance of a heater component. A method includes: controlling power to the heater component; receiving sensor data measured, by a sensor, of a heater component while the power is controlled to the heater; generating first thermal image data of the heater component based on the sensor data; determining a power distribution of the heater component based on a first model of the heater component and the first thermal image data; computing second thermal image data of the heater component based on a second model of at least one of the heater component and an assembly comprising the heater component, and the power distribution; and evaluating a performance of the heater component based on the second thermal image data.

Description

METHOD FOR EARLY THERMAL PERFORMANCE ASSESSMENT OF A HEATERCOMPONENTCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Patent Application No. 63 / 663,364, filed on June 24, 2024. The disclosure of the above application is incorporated herein by reference.FIELD

[0002] The present disclosure relates to thermal evaluation systems, and more particularly to methods and systems for evaluating the thermal performance of a heated component, such as by way of example a ceramic pedestal heater for use in semiconductor processing equipment.BACKGROUND

[0003] Pedestals may be used for the purpose of heating silicon wafers of a semiconductor in a film deposition chamber. A pedestal assembly may include a heater plate (e.g., an Aluminum Nitride (AIN) sintered ceramic plate including embedded heating circuitry within) with a ceramic top and an attached (e.g., metal bonded) ceramic shaft. The ceramic shaft includes a transition apparatus responsible for mounting and sealing the pedestal to a lower floor of the processing chamber. Within the shaft are heater power pins, a control thermocouple, and any ancillary gas or vacuum connections in addition to any grid, antenna, and / or ground plane electrical power connections.

[0004] Often, the thermal performance of the pedestal assembly is not learned and understood until the pedestal assembly is installed into a processing chamber and thermally characterized with an instrumented wafer, or upon measurements taken from the films deposited on the initial wafers. Some film attribute measurements include variations based on other process elements (i.e., gas flow, plasma density, etc.) such that the true performance of the pedestal assembly itself is not clear. Any delays in the evaluation of the thermal performance of a pedestal assembly can impact the fabrication process.

[0005] Understanding the thermal performance of a heated component and / or system and making appropriate adjustments are addressed by the present disclosure.SUMMARY

[0006] This section provides a general summary of the disclosure and is not a comprehensive disclosure of its full scope or all of its features.

[0007] In various forms, a method for evaluating a performance of a heater component is provided. The method includes: controlling power to the heater component; receiving sensor data measured, by a sensor, of the heater component while the power is controlled to the heater component; generating first thermal image data of the heater component based on the sensor data; determining a power distribution of the heater component based on a first model of the heater component and the first thermal image data; computing second thermal image data of the heater component based on a second model of at least one of the heater component and an assembly comprising the heater component, and the power distribution; and evaluating a performance of the heater component based on the second thermal image data.

[0008] In various forms, the controlling power to the heater component includes controlling a defined power for a period of time.

[0009] In various forms, the generating the first thermal image data includes: segmenting the first model into a plurality of pixels; mapping the sensor data to the plurality of pixels of the first model to produce measured thermal image data; and wherein the determining the power distribution is based on the measured thermal image data.

[0010] In various forms, the determining the power distribution includes: selectively distributing the power to each of the plurality of pixels of the first model; computing thermal image data for each pixel of the plurality of pixels of the first model based on the distributed power to produce predicted thermal image data, and wherein the determining the power distribution is further based on a comparison of the measured thermal image data and the predicted thermal image data.

[0011] In various forms, the selectively distributing the power includes: distributing a uniform power to each pixel of the plurality of pixels of the first model; computing the predicted thermal image data based on the uniform power; comparingthe measured thermal image data to the predicted thermal image data; and selectively increasing or decreasing power of one or more pixels of the plurality of pixels of the first model based on the comparing.

[0012] In various forms, the computing the second thermal image includes: segmenting the heater component of the second model into a plurality of pixels; mapping the power distribution to the plurality of pixels of the second model; and selectively increasing and decreasing the power to each of the pixels to produce the second thermal image based on the mapped power distribution.

[0013] In various forms, the method includes: computing a standard deviation from thermal data of the second thermal image; comparing the standard deviation to a threshold; and iterating the selectively increasing and decreasing the power to each of the pixels based on the comparing.

[0014] In various forms, the sensor is an infrared sensor that measures a top surface of the heater component.

[0015] In various forms, at least one of the first model and the second model is a finite element analysis model.

[0016] In various forms, the second model includes parameters defining a geometry of the heater component and parameters associated with processing conditions of a processing environment of the heater component.

[0017] In various forms, the method includes generating a recommendation of quality of the heater component based on the evaluating.

[0018] In various forms a system for evaluating a performance of a heater component is provided. The system includes: one or more processors; and a computer-readable storage medium storing instructions which, when executed by the one or more processors, cause the one or more processors to: control power to the heater component; receive sensor data measured, by a sensor, of the heater component while the power is controlled to the heater component; generate first thermal image data of the heater component based on the sensor data; determine a power distribution of the heater component based on a first model of the heater component and the first thermal image data; compute second thermal image data of the heater component based on a second model of at least one of the heater component and an assembly comprising the heater component, and the power distribution; and evaluate a performance of the heater component based on the second thermal image data.

[0019] In various forms, the one or more processors are configured to control power to the heater component by controlling a defined power for a period of time.

[0020] In various forms, the one or more processors are configured to generate the first thermal image data by: segmenting the first model into a plurality of pixels; mapping the sensor data to the plurality of pixels of the first model to produce measured thermal data; and wherein the determining the power distribution is based on the measured thermal data.

[0021] In various forms, the one or more processors are configured to determine the power distribution by: selectively distributing the power to each of the plurality of pixels of the first model; computing thermal image data for each pixel of the plurality of pixels of the first model based on the distributed power to produce predicted thermal image data, and wherein the determining the power distribution is further based on a comparison of the measured thermal image data and the predicted thermal image data.

[0022] In various forms, the one or more processors are configured to selectively distribute the power by: distributing a uniform power to each pixel of the plurality of pixels of the first model; computing the predicted thermal data based on the uniform power; comparing the measured thermal data to the predicted thermal data; and selectively increasing or decreasing power of one or more pixels of the plurality of pixels of the first model based on the comparing.

[0023] The system of claim 12, wherein the one or more processors are configured to compute the second thermal image by: segmenting the heater component of the second model into a plurality of pixels; mapping the power distribution to the plurality of pixels of the second model; and selectively increasing and decreasing the power to each of the pixels to produce the second thermal image data based on the mapped power distribution.

[0024] In various forms, the one or more processors are further configured to compute a standard deviation from thermal data of the second thermal image data; comparing the standard deviation to a threshold; and iterating the selectively increasing and decreasing the power to each of the pixels based on the comparing.

[0025] In various forms, the sensor is an infrared sensor that measures a top surface of the heater component.

[0026] In various forms, at least one of the first model and the second model is a finite element analysis model.

[0027] In various forms, the second model comprises parameters defining a geometry of the heater component and parameters associated with processing conditions of a processing environment of the heater component.

[0028] In various forms, the processor is further configured to generate a recommendation of quality of the heater component based on the evaluating.

[0029] In various forms, a computer-readable storage device storing instructions which, when executed by one or more processors, cause the one or more processors io: control power to a heater component; receive sensor data measured, by a sensor, of the heater component while the power is controlled to the heater component; generate first thermal image data of the heater component based on the sensor data; determine a power distribution of the heater component based on a first model of the heater component and the first thermal image data; compute second thermal image data of the heater component based on a second model of at least one of the heater component and an assembly comprising the heater component, and the power distribution; and evaluate a performance of the heater component based on the second thermal image data.

[0030] Further areas of applicability will become apparent from the description provided herein. It should be understood that the description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.BRIEF DESCRIPTION OF DRAWINGS

[0031] In order that the disclosure may be well understood, there will now be described various forms thereof, given by way of example, reference being made to the accompanying drawings, in which:

[0032] FIG. 1 is a functional block diagram illustrating a thermal performance evaluation system associated with a resistive element of a heating component in accordance with various forms;

[0033] FIGS. 2A and 2B are illustrations of heater components in accordance with various forms;

[0034] FIG. 2C is an illustration of an assembly including a heater component in accordance with various forms;

[0035] FIG. 3 is a dataflow diagram illustrating a control module of the thermal performance evaluation system in accordance with various forms; and

[0036] FIGS. 4, 5, 6, 7, 8, 9, 10, and 11 are flowcharts illustrating processes that may be performed by the control module of the thermal performance evaluation system in accordance with various forms.

[0037] The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.DETAILED DESCRIPTION

[0038] The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features.

[0039] With reference to FIG. 1 , a thermal performance evaluation system is shown generally at 100. The thermal performance evaluation system 100 evaluates the thermal performance of a heater component 102 as a stand-alone component or as part of an assembly. The thermal performance evaluation system 100 performs the evaluation, in general, by controlling power to the heater component 102 via an actuator 103 (e.g., a control box including a power supply coupled to the heater component 102) and, in response, determining a power distribution of the heater component 102. The thermal performance evaluation system 100 uses the determined power distribution and a model to predict a thermal response of the heater component 102 under various conditions. As will be discussed in more detail, the model will vary based on the conditions of the processing environment (e.g., a processing chamber) of the heater component 102 and / or whether the evaluation is performed on the stand-alone component or the assembly.

[0040] As shown in FIG. 2A, an example heater component 102a includes at least one resistive element 104 that is controlled to generate heat. In various forms, the resistive element 104 can be in the form of a wire or other geometrical shape (e.g., sheets, films, etc.) that is straight, coiled, wound, etc. and embedded within, for example, an aluminum nitride (AIN) (or other material) ceramic bottom plate. For example, the resistive element 104 may be thermally sprayed, a resistive foil embedded within a polyimide material, a tubular heater, or a ceramic material, among others. The resistive element 104 is embedded within a plate 105,thereby preventing direct evaluation of the thermal performance of the resistive element 104.

[0041] As shown in FIG. 2B, another example heater component 102b may include two or more resistive elements 104a and 104b that each are controlled to generate heat to a respective zone of the heater component 102b. As can be appreciated, any number of resistive elements 104 may be included in the heater component 102 with various different configurations to provide various different zones as the disclosure is not limited to the present examples.

[0042] As shown in FIG. 2C, the heater component 102 may be part of an assembly 106, such as, but not limited to, a pedestal assembly having an attached (e.g., metal bonded), or integral, ceramic shaft 108 outfitted with a base portion 110 configured for mounting and sealing the shaft 108 to a lower floor of a processing environment. Within the shaft 108 are heater power pins, a control thermocouple, and any ancillary gas or vacuum connections in addition to any grid, antenna, and / or ground plane electrical power connections (not shown), among other components that are required to be hermetically sealed from an environment of a processing chamber.

[0043] As can be appreciated, the heater component 102 and / or the assembly 106 may vary from those shown in the drawings, for example, and a heater component and / or assembly that is incorporated into industrial equipment (e.g., heat exchangers, other semiconductor equipment, injection molding equipment, chemical processing, exhaust emissions, scientific equipment, motor vehicles, and powerplants, among many others), to provide temperature control of such equipment / applications. As can be appreciated, these particular examples are non-limiting and are illustrated and described herein for the sake of demonstrating exemplary forms of the present disclosure. Accordingly, it should be understood that the thermal performance evaluation system 100 may be configured to provide thermal performance information associated with a resistive element 104, whether or embedded or exposed, of any type of component and / or assembly having a single zone or a multiple zone configuration while remaining within the scope of the present disclosure.

[0044] With reference back to FIG. 1 , the thermal performance evaluation system 100 generally includes a sensing environment 111 (referred to as a transient sensing environment) including a sensor 112 such as, but not limited to, one or more infrared (IR) sensors that measure observable conditions of the heater component 102 as it is being controlled by the actuator 103. For example, the sensor112 measures a temperature of a top surface of the ceramic plate of the heater component 102. The sensor 112 communicates the measured data to a control module 114. In various forms, the measured data includes a video or multiple frames of infrared data captured of the top surface of the heater component 102 over a measured time period.

[0045] The control module 114 generally includes at least an input device 116, an output device 118, at least one processor 120 and memory 122 and, optionally, a communication device 124. In various forms, the input device 116 is configured to receive the measured data from the sensor 112 and / or other devices such as, but not limited to, user input devices (not shown). Similarly, the output device 118 is configured to provide thermal performance data including power distribution data 126, component performance data 128, and / or assembly performance data 130 that may be used by one or more evaluation devices to evaluate the performance of the heater component 102 and / or assembly 106. In various forms, the performance data can include a recommendation of overall quality and / or use of the heater component 102.

[0046] In various forms, the communication device 124 is configured to receive data and / or communicate data including the power distribution data 126, component performance data 128, and / or assembly performance data 130 to a remote system or other entity for further evaluation by way of a wired or wireless communication protocol.

[0047] In various forms, the processor 120 can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the processor 120, a semiconductor based microprocessor (in the form of a microchip or chip set), a macro processor, any combination thereof, or generally any device for executing instructions. Although only one processor 120 is shown, various form of the thermal performance evaluation system 100 100 can include any number of processors 120 that communicate over any suitable communication medium or a combination of communication mediums. The processors 120 cooperate to process the sensor data, perform logic, calculations, methods, processes and / or algorithms, generate control signals to the actuator 103, and / or communicate the thermal performance data via the output device 118 and / or the communication device 124.

[0048] In various forms, the memory 122 may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the processor 120 is powered down. The memory 122 may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the processor 120 in performing the methods and / or processes of the thermal performance evaluation system 100.

[0049] In various forms, the memory 122 stores the programming instructions, which may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions associated with the methods are processes described herein. The instructions, when executed by the processor 120, perform logic, calculations, methods, and / or algorithms of predefined methods or processes. In various forms, one or more of the programming instructions are embodied in and are configured to implement the methods, processes, and systems described herein for measuring and processing the sensor data, and predicting the power distribution data 126, the component performance data 128, and / or the assembly performance data 130, as will be described further below.

[0050] In various forms, the memory 122 further includes a data storage device 132 that stores predefined data such as models, measured data, and / or predicted data associated with the heater component 102, the assembly 106 having the heater component 102, the resistive element 104, the configured zones, and / or the conditions associated with the processing environment of the heater component 102 (e.g., the processing chamber). As will be discussed herein, the stored data is used by the thermal performance evaluation system 100 to generate the power distribution data 126, the component performance data 128, and / or the assembly performance data 130.

[0051] As such, the thermal performance evaluation system 100 is configured to provide enhanced performance information at early stages in the fabrication process without the need for the physical processing environment.

[0052] Referring now to FIG. 3 and with continued reference to FIGS. 1 , 2A, 2B, and 2C, a dataflow diagram illustrates various forms of elements of the control module 114 of the thermal performance evaluation system 100 in accordance with various embodiments. That is, suitable software and / or hardware components of the control module 114 (e.g., the processor 120 and the memory 122) are utilized to provide the performance information that is used in conjunction with the heater component 102 and / or assembly 106. In various forms, inputs to the control module 114 may be received from the input device 116, received from the communication device 124, and / or determined, modeled, or pre-processed by other sub-modules (not shown) within the control module 114.

[0053] In various forms, the instructions of the thermal performance evaluation system 100 may be organized by function or system into sub-modules and datastores of the control module 114. For example, as shown in FIG. 3, the control module 114 can be configured to include a thermal measurement module 202, a power distribution determination module 204, a simulation module 206, a performance evaluation module 208, a performance history management module 209, a component model datastore 210, an assembly model datastore 212, a performance data datastore 214, and a thermal image data datastore 215.

[0054] In various forms, the thermal measurement module 202 controls power to the heater component 102 by generating control data 216 to the actuator 103 and, in response, receives sensor data 218 from the sensor 112. The thermal measurement module 202 processes the sensor data 218 and generates thermal image data 220 based thereon.

[0055] For example, the thermal measurement module 202 controls the power to the heater component 102 via the control data 216 for a defined period of time (e.g., relatively short, less than sixty seconds, or some other value) and according to a constant target power to effectively charge the heater component 102 with a heat energy infusion (e.g. of less than seventy degrees Celsius). In various forms, the infrared camera captures a high resolution and high frequency temperature response of the surface of the heater component 102. The thermal measurement module 202 analyzes the video of the sensor data 218 and identifies a frame that best illustrates the heat energy infusion. The thermal measurement module 202 then generates the thermal image data 220 based on the identified frame.

[0056] When the heater component 102 includes multiple zones (as shown in FIG. 2A), the thermal measurement module 202 controls the power to each zone for the defined period of time such that the sum of the constant power to each zone is equal to the target constant power. In various forms, the power to each zone can be adjusted for any number of iterations until an optimum heat energy fusion is identified in a frame. The thermal measurement module 202 then generates the thermal image data 220 based on the identified frame.

[0057] In various forms, the power distribution determination module 204 receives the thermal image data 220. The power distribution determination module 204 determines a distribution of the total power throughout the heater component 102 based thereon and generates power distribution data 222.

[0058] For example, the power distribution determination module 204 retrieves model data 224 from the component model datastore 210 corresponding to the heater component 102 that was sensed. The model data 224 includes, for example, a finite element analysis (FEA) model defining the geometry of the component. The power distribution determination module 204 segments or pixelates the FEA model into a desired number of pixels (e.g., fifty by fifty or some other value). The power distribution determination module 204 maps the thermal image data 220 to the pixels of the outer surface of the component in the FEA model to generate a signature temperature target.

[0059] The power distribution determination module 204 then adds uniform power to each pixel in the FEA model (totaling the target test power) and computes a temperature at the pixel based thereon. The power distribution determination module 204 then iteratively adjusts the power assigned to each of the pixels (totaling the target test power) and computes the temperature at the pixel until the resulting modeled surface temperature is substantially similar to the signature temperature target. The power distribution determination module 204 generates the power distribution data 222 based on the final pixelated power model.

[0060] In various forms, the simulation module 206 receives the power distribution data 222. The simulation module 206 predicts a thermal image of a modeled component in a simulated environment, a modeled assembly, and / or a modeled assembly in a simulated environment based on the power distribution data 222. For example, the simulation module 206 retrieves model data 226 from the component model datastore 210 corresponding to the heater component 102 or modeldata 226 from the model datastore 212 corresponding to an assembly including the heater component 102.

[0061] In various forms, the model data 226, and / or the model data 228 includes parameters defining the conditions of the processing environment that will affect the component or assembly during fabrication. The conditions are generally referred to as the processing conditions and can include, but are not limited to, chamber pressure, chamber wall temperature, gas flow species, gas flow volume flow rate, gas flow distribution, wafer coupling means, etc. In various forms, the processing conditions can be defined for any number of different processing environments and thus, any number of models can be stored in the model datastore 212.

[0062] The simulation module 206 segments or pixelates the portion of the model corresponding to the heater component 102 according to the same desired number of pixels (e.g., fifty by fifty or some other values) used by the power distribution determination module 204. The simulation module 206 determines a total power and distributes power to the pixels based on the power distribution data 222. The simulation module 206 computes a steady state temperature for each pixel based on the power associated with the pixel, and the parameters defining the heater component 102, and / or the processing conditions to produce predicted temperature data.

[0063] The simulation module 206 computes a standard deviation of the predicted temperature data from all of the pixels and compares the standard deviation to a predefined threshold. If the standard deviation does not meet the threshold, the simulation module 206 adjusts the total power to the component (e.g., by increasing or decreasing) and re-distributes the power to the pixels based on the power distribution data 222. When adjusting the power to each pixel, the relative power ratios amongst the pixels (determined from the power distribution data 222) are maintained while the cumulative power is increased or decreased. Once the threshold standard deviation has been met, the thermal image data and the associated power to achieve the thermal image data is provided as predicted thermal data for evaluation. When the component is evaluated, the simulation module 206 provides the predicted thermal data as component performance data 230. When the assembly is evaluated, the simulation module 206 provides the predicted thermal data as assembly performance data 232. In various forms, the component performance data 230 and / or the assembly performance data 232 is saved in the performance data datastore 214 to establish adatabase of performance data for future evaluations. In various forms, the thermal image data 220 and / or the power distribution data 222 used to generate the component performance data 230 and / or the assembly performance data 232 may be associated with the respective performance data and stored in the performance data datastore 214 for future evaluations.

[0064] In various forms, the performance history management module 209 receives as input the thermal image data 220. The performance history management module 209 determines whether the thermal image data 220 matches (or is substantially similar to) any previously stored thermal image data 220. When the received thermal image data 220 matches (or is substantially similar to) any previously stored thermal image data, previously stored performance data associated with the thermal image data is retrieved from the performance data datastore 214 and supplied to the performance evaluation module 208 as assembly performance data 234 or component performance data 236. As can be appreciated, in various forms, the functions of the performance history management module 209 can be performed instead of or in addition to the functions of the power distribution determination module 204 and / or the simulation module 206 to produce the predicted performance data for use by the performance evaluation module 208.

[0065] In various forms, the performance evaluation module 208 receives the predicted performance data 230, 232, 234 and / or 236 and evaluates the predicted performance data 230, 232, 234 and / or 236 with one or more predefined quality standards to provide a recommendation of quality of the heater component 102. For example, the performance data evaluation module 208 generates recommendation data 234 indicating whether the heater component 102 meets or does not meet a quality standard or whether the heater component 102 should be kept, modified, or discarded.

[0066] Referring now to FIGS. 4-11 , and with continued reference to FIGS. 1-3, flowcharts illustrate processes 300-1000 that can be performed by, for example, the modules of the control module 114 of the thermal performance evaluation system 100 in accordance with the present disclosure. As can be appreciated in light of the disclosure, the order of operation within the processes is not limited to the sequential execution as illustrated in FIGS. 4-11 but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. In various embodiments, the various processes 300-1000 can bescheduled to run independently or in a defined order based on one or more predetermined events associated with the manufacture processes associated with the heater component 102.

[0067] With reference now to FIG. 4, a process 300 for measuring temperature data of the heater component 102 is shown in accordance with various forms. In one example, the process 300 may begin at 302. Power is controlled to the heater component 102 using a known and constant power input at 304; and the sensor data 218 is obtained for a period of time (e.g., less than sixty seconds), such that the component is effectively charged with a heat energy fusion. By charging the component for a short period of time and measuring the component, the temperature of the component does not exceed ~70C. This protects any oxidation-sensitive component from undergoing degradation (molybdenum, for example) during this process.

[0068] Once the sensor data 218 has been captured for the period of time at 308, post processing of the sensor data 218 is performed to provide a high resolution thermal image of the resulting surface temperature of the heater component 102 at 310. The resulting thermal image is stored as thermal image data 220 along with the associated total heat energy used to generate the thermal image at 312. Thereafter, the process 300 may end at 314.

[0069] With reference now to FIG. 5, a process 400 for measuring temperature data of the heater component 102 having multiple zones is shown in accordance with various forms. In one example, the process 400 may begin at 402. Power is controlled to each of the zones by dividing the total constant power between the zones for a period of time (e.g., less than sixty seconds) at 404 and the sensor data 218 is obtained at 406 for a period of time such that the component is effectively charged with a heat energy fusion.

[0070] The sensor data 218 is performed to provide a high resolution thermal image of the resulting surface temperature of the heater component 102 at 410. If a thermal image with optimum thermal image is not identified at 412, the process 400 may adjust the power allocated to each zone, while keeping the total target power the same, at 414 and control the power to the zones at 404.

[0071] The process 400 iterates until an optimum thermal image is identified at 412. The resulting thermal image is stored as thermal image data 220along with the associated total heat energy used to generate the thermal image at 416. Thereafter, the process 400 may end at 418.

[0072] With reference now to FIG. 6, a process 500 for determining a power distribution of the heater component 102 based on the stored thermal image data 220 is shown in accordance with various forms. In one example, the process 500 may begin at 502. The thermal image data 220 measured from the heater component 102 is received at 504 and a first model of the component is obtained at 506. For example, an FEA model of the heater component 102 that features a heating layer embedded within a ceramic body is obtained.

[0073] The first model is divided or pixelated into a number of cells or pixels at 508. For example, the heating layer within the FEA model is pixelated into individual heat-generating cells, such as, for example, a fifty by fifty array of cells in which a subset comprises the entire circular heating layer.

[0074] The measured thermal image data 220 is mapped to the cells or pixels of the first model at 510 to produce measured model data. For example, the measured surface temperature map is imported onto the uppermost surface of the FEA model to represent the measured model data.

[0075] The associated power used in the initial measurement is then uniformly distributed to the individual pixels of the model to produce a first predicted power distribution at 512. Thermal data is then computed for each pixel of the first model based on the predicted power distribution to produce predicted model data at 514.

[0076] The predicted model data is compared with the measured model data at 516. When the predicted model data is not within a threshold range of the measured model data at 516, the power assigned to each of the pixels (totaling the target test power) is modified at 518. For example, the power of each pixel is iteratively changed until the predicted model data is within a threshold range of the measured model data for that pixel.

[0077] Once the predicted model data is within the threshold range of the measured model data at 516, the predicted power distribution is saved as the final power distribution data 222 at 520 and provided to other modules and / or entities for further analysis at 522. Thereafter, the process 500 may end at 524.

[0078] With reference now to FIG. 7, a process 600 for evaluating the heater component 102 or the assembly 106 with or without processing conditions andbased on the power distribution data 222 is shown in accordance with various forms. In one example, the process 600 may begin at 602. The predicted power distribution data 222 is received at 604. A second FEA model is obtained at 606. The second model includes a model of the heater component 102 or a model of the assembly 106 with or without processing conditions associated with the processing environment. For example, if the initial measurement was performed on a full assembly pedestal, then the second model remains the same but with the constraints of the processing conditions of the processing environment. In another example, if the initial measurement was performed on the heater plate, then the second model will feature the heater plate but fully integrated with a wafer plate, shaft, chamber mount and terminations as a full assembly pedestal with or without the constraints of the processing conditions of the processing environment.

[0079] In various forms, each of the pixels of the heater component 102 from the first model, and thus the associated power distribution data, is mapped to the heater component 102 in the second model at 608.

[0080] Thereafter, total power is controlled to the component in the second model and distributed to the pixels based on the power distribution data at 610. A steady state temperature is then determined for each pixel based on the distributed power and the defined parameters at 612.

[0081] A standard deviation of the temperature data is computed and compared to a threshold at 614. If the standard deviation does not meet the threshold at 616, the total power is adjusted (increased or decreased) to the component and distributed to the pixels based on the power distribution at 618. When adjusting the power to each pixel, the relative power ratios, determined from the power distribution data, amongst the pixels are maintained while the cumulative power is increased or decreased.

[0082] The steady state temperature of the pixels and the standard deviation of the temperatures are iteratively computed until the threshold standard deviation has been reached at 616. Once the standard deviation has been reached at 616, the resulting power distribution and temperature data is stored as performance data at 620. The performance data is provided for quality analysis of the heater component 102 at 622. Thereafter, the process 600 may end at 624.

[0083] With reference now to FIG. 8, a process 700 for evaluating the heater component 102 having multi-zones is shown in accordance with various forms.In one example, the process 700 may begin at 702. The predicted power distribution data 222 is received at 704. The pixels of the predicted power distribution are evaluated using nearest neighbor or other clustering technique in order to define one or more sub-groups of the pixels at 706. For example, the sub-groups can correspond to the exacting dimensions of the individual zones of thermal control within the component. In other words, for a two-zone component, the pixels that are within the geometric boundaries of each control zone become a sub-group. All pixels are assigned to a sub-group that defines each of the two zones of control.

[0084] A second FEA model is obtained at 708. The second model includes a model of the heater component 102 or a model of the assembly 106 with or without processing conditions. In various forms, each of the pixels of the heater component 102 from the first model, and thus the associated power distribution data, is mapped to the heater component 102 in the second model at 710.

[0085] Thereafter, total power is controlled separately to each of the identified sub-groups of the component in the second model and distributed to the pixels within the sub-groups based on the power distribution data at 712. A steady state temperature is then determined for each pixel based on the distributed power, and the defined parameters at 714.

[0086] A standard deviation of the temperature data within each subgroup is computed at 716 and compared to a threshold at 718. If the standard deviations do not meet the threshold at 718, the total power is adjusted (e.g., increased or decreased) at 720 and distributed to the sub-groups and the pixels based on the power distribution data 222 at 712. When adjusting the power to each pixel within the sub-group, the relative power ratios within the sub-group (determined from the power distribution data 222) are maintained while the cumulative power is increased or decreased.

[0087] The steady state temperature of the pixels and the standard deviation of the temperatures is iteratively computed for each sub-group until the threshold standard deviation has been reached at 718. Once the threshold has been reached at 718, the resulting power distribution and temperature data is stored as performance data for the heater component at 722. The performance data is provided for further quality analysis of the heater component 102 at 724. Thereafter, the process 700 may end 726.

[0088] With reference now to Fig. 9, a process 800 for evaluating the heater component 102 is shown in accordance with various forms. In one example, the process 800 may begin at 802. The performance data is received at 804. Performance metrics associated with heater component 102 are received at 806. The performance data is compared with the performance metrics at 808.

[0089] If the performance data meets the performance metrics at 810, a recommendation to keep the heater component 102 is generated at 812, and the performance data is stored with the power distribution data at 814. Thereafter, the process 800 may end at 816.

[0090] If, however, the performance data does not meet the performance metrics at 810, a recommendation to discard the heater component 102 is generated at 818, and the performance data is stored with the power distribution data at 820. Thereafter, the process 800 may end at 816.

[0091] With reference now to Fig. 10, a process 900 for evaluating the performance history data is shown in accordance with various forms. In one example, the process 900 may begin at 902. The image data is compared with stored thermal image data in the thermal image datastore at 904. If no match of the thermal image is saved at 904, the process 900 may end at 912. If a match of the thermal image is found in the datastore, the confidence level of the match is compared to a threshold at 906. When the confidence level of the match does not exceed the threshold at 906, the process 900 may end at 912. If the confidence level of the match exceeds the threshold at 906, the performance data associated with the stored thermal image data is retrieved from the performance data datastore and provided for evaluation at 910. Thereafter, the process 900 may end at 912. As can be appreciated, the process 900 may performed as an alternative to processes of FIGS. 5-8 or in addition to in order to provide the performance data for evaluation.

[0092] With reference now to Fig. 11 , a process 1000 for evaluating and / or updating the modeled processing conditions is shown in accordance with various forms. In one example, the process 1000 may begin at 1005. The simulation of the component is performed at 1010 to produce the performance data. The performance data is evaluated at 1012. If the performance data does not meet the metrics at 1012, the process 1000 may end at 1028. If the performance data meets the metrics at 1012, the simulation of the assembly with the component is performed at 1014. If the power distribution data from the first simulation of the component doesnot match (or is not substantially similar to) the power distribution data of the assembly at 1016, the performance data from the second simulation of the assembly is evaluated at 1018.

[0093] If the performance data does not meet the metrics at 1018, the process 1000 may end at 1028. If the performance data meets the metrics at 1012, test data including thermal image data associated with the component in an actual processing environment is retrieved at 1020. The test thermal image data and the predicted thermal image data are compared at 1022. If the test thermal image data matches (or is substantially similar to) the predicted thermal image data at 1022, the processing conditions are stored in the model for use in future simulations at 1026. If the test thermal image data does not match (or is not substantially similar to) the predicted thermal image data at 1022, the parameters of the processing environment are tuned, and the model is updated at 1024. Thereafter, the process 1000 may continue at 1010 with the retuned model or may end at 1028.

[0094] In accordance with the disclosure, novel methods and systems are provided for predicting the thermal performance of a heater component and a completely manufactured pedestal assembly in the targeted process environment by virtue of a fast, low temperature transient response test.

[0095] Unless otherwise expressly indicated herein, all numerical values indicating mechanical / thermal properties, compositional percentages, dimensions and / or tolerances, or other characteristics are to be understood as modified by the word “about” or "approximately" in describing the scope of the present disclosure. This modification is desired for various reasons including industrial practice, material, manufacturing, and assembly tolerances, and testing capability.

[0096] As used herein, the phrase at least one of A, B, and C should be construed to mean a logical (A OR B OR C), using a non-exclusive logical OR, and should not be construed to mean “at least one of A, at least one of B, and at least one of C.”

[0097] In this application, the term “controller” and / or “module” may refer to, be part of, or include: an Application Specific Integrated Circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by theprocessor circuit; other suitable hardware components (e.g., op amp circuit integrator as part of the heat flux data module) that provide the described functionality; or a combination of some or all of the above, such as in a system-on-chip.

[0098] The term memory is a subset of the term computer-readable medium. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave); the term computer-readable medium may therefore be considered tangible and non-transitory. Non-limiting examples of a non-transitory, tangible computer-readable medium are nonvolatile memory circuits (such as a flash memory circuit, an erasable programmable read-only memory circuit, or a mask readonly circuit), volatile memory circuits (such as a static random access memory circuit or a dynamic random access memory circuit), magnetic storage media (such as an analog or digital magnetic tape or a hard disk drive), and optical storage media (such as a CD, a DVD, or a Blu-ray Disc).

[0099] The apparatuses and methods described in this application may be partially or fully implemented by a special purpose computer created by configuring a general-purpose computer to execute one or more particular functions embodied in computer programs. The functional blocks, flowchart components, and other elements described above serve as software specifications, which can be translated into the computer programs by the routine work of a skilled technician or programmer.

[0100] The description of the disclosure is merely exemplary in nature and, thus, variations that do not depart from the substance of the disclosure are intended to be within the scope of the disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the disclosure.

Claims

CLAIMSWhat is claimed is:

1. A method for evaluating a performance of a heater component, the method comprising: controlling power to the heater component; receiving sensor data measured, by a sensor, of the heater component while the power is controlled to the heater component; generating first thermal image data of the heater component based on the sensor data; determining a power distribution of the heater component based on a first model of the heater component and the first thermal image data; computing second thermal image data of the heater component based on a second model of at least one of the heater component and an assembly comprising the heater component, and the power distribution; and evaluating a performance of the heater component based on the second thermal image data.

2. The method of claim 1 , wherein the controlling power to the heater component comprises controlling a defined power for a period of time.

3. The method of claim 1 , wherein the generating the first thermal image data comprises: segmenting the first model into a plurality of pixels; and mapping the sensor data to the plurality of pixels of the first model to produce measured thermal image data, wherein the determining the power distribution is based on the measured thermal image data.

4. The method of claim 3, wherein the determining the power distribution comprises: selectively distributing the power to each of the plurality of pixels of the first model; andcomputing thermal image data for each pixel of the plurality of pixels of the first model based on the distributed power to produce predicted thermal image data, wherein the determining the power distribution is further based on a comparison of the measured thermal image data and the predicted thermal image data.

5. The method of claim 4, wherein the selectively distributing the power comprises: distributing a uniform power to each pixel of the plurality of pixels of the first model; computing the predicted thermal image data based on the uniform power; comparing the measured thermal image data to the predicted thermal image data; and selectively increasing or decreasing power of one or more pixels of the plurality of pixels of the first model based on the comparing.

6. The method of claim 1 , wherein the computing the second thermal image comprises: segmenting the heater component of the second model into a plurality of pixels; mapping the power distribution to the plurality of pixels of the second model; and selectively increasing and decreasing the power to each of the pixels to produce the second thermal image based on the mapped power distribution.

7. The method of claim 6 further comprising computing a standard deviation from thermal data of the second thermal image; comparing the standard deviation to a threshold; and iterating the selectively increasing and decreasing the power to each of the pixels based on the comparing.

8. The method of claim 1 , wherein the sensor is an infrared sensor that measures a top surface of the heater component.

9. The method of claim 1, wherein at least one of the first model and the second model is a finite element analysis model.

10. The method of claim 1, wherein the second model comprises parameters defining a geometry of the heater component and parameters associated with processing conditions of a processing environment of the heater component.11 . The method of claim 1 , further comprising generating a recommendation of quality of the heater component based on the evaluating.

12. A system for evaluating a performance of a heater component, comprising: one or more processors; and a computer-readable storage medium storing instructions which, when executed by the one or more processors, cause the one or more processors to: control power to the heater component; receive sensor data measured, by a sensor, of the heater component while the power is controlled to the heater component; generate first thermal image data of the heater component based on the sensor data; determine a power distribution of the heater component based on a first model of the heater component and the first thermal image data; compute second thermal image data of the heater component based on a second model of at least one of the heater component and an assembly comprising the heater component, and the power distribution; and evaluate a performance of the heater component based on the second thermal image data.

13. The system of claim 12, wherein the one or more processors are configured to control power to the heater component by controlling a defined power for a period of time.

14. The system of claim 12, wherein the one or more processors are configured to generate the first thermal image data by: segmenting the first model into a plurality of pixels; and mapping the sensor data to the plurality of pixels of the first model to produce measured thermal data, wherein the determining the power distribution is based on the measured thermal data.

15. The system of claim 14, wherein the one or more processors are configured to determine the power distribution by: selectively distributing the power to each of the plurality of pixels of the first model; and computing thermal image data for each pixel of the plurality of pixels of the first model based on the distributed power to produce predicted thermal image data, wherein the determining the power distribution is further based on a comparison of the measured thermal image data and the predicted thermal image data.

16. The system of claim 15, wherein the one or more processors are configured to selectively distribute the power by: distributing a uniform power to each pixel of the plurality of pixels of the first model; computing the predicted thermal data based on the uniform power; comparing the measured thermal data to the predicted thermal data; and selectively increasing or decreasing power of one or more pixels of the plurality of pixels of the first model based on the comparing.

17. The system of claim 12, wherein the one or more processors are configured to compute the second thermal image by: segmenting the heater component of the second model into a plurality of pixels; mapping the power distribution to the plurality of pixels of the second model; andselectively increasing and decreasing the power to each of the pixels to produce the second thermal image data based on the mapped power distribution.

18. The system of claim 17, wherein the one or more processors are further configured to compute a standard deviation from thermal data of the second thermal image data; comparing the standard deviation to a threshold; and iterating the selectively increasing and decreasing the power to each of the pixels based on the comparing.

19. The system of claim 12, wherein the sensor is an infrared sensor that measures a top surface of the heater component.

20. The system of claim 12, wherein at least one of the first model and the second model is a finite element analysis model.

21. The system of claim 12, wherein the second model comprises parameters defining a geometry of the heater component and parameters associated with processing conditions of a processing environment of the heater component.

22. The system of claim 12, wherein the processor is further configured to generate a recommendation of quality of the heater component based on the evaluating.

23. The system of claim 12, wherein the heater component comprises two or more resistive elements arranged in respective zones.

24. A computer-readable storage device storing instructions which, when executed by one or more processors, cause the one or more processors to: control power to a heater component; receive sensor data measured, by a sensor, of the heater component while the power is controlled to the heater component; generate first thermal image data of the heater component based on thesensor data; determine a power distribution of the heater component based on a first model of the heater component and the first thermal image data; compute second thermal image data of the heater component based on a second model of at least one of the heater component and an assembly comprising the heater component, and the power distribution; and evaluate a performance of the heater component based on the second thermal image data.

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