Patent Publication Number: US-11644817-B2

Title: Control system for adaptive control of a thermal processing system

Description:
PRIORITY CLAIM 
     The present application claims the benefit of priority of U.S. Provisional Application Ser. No. 62/929,404, filed on Nov. 1, 2019, titled “Control System for Adaptive Control of a Thermal Processing System,” which is incorporated herein by reference. 
    
    
     FIELD 
     The present disclosure relates generally to thermal processing systems. 
     BACKGROUND 
     A thermal processing chamber as used herein refers to a device that heats workpieces, such as semiconductor wafers. Such devices can include a support plate for supporting one or more semiconductor wafers and an energy source for heating the semiconductor wafers, such as heating lamps, lasers, or other heat sources. During heat treatment, the semiconductor wafers can be heated under controlled conditions according to a preset temperature regime. 
     Many semiconductor heating processes require a wafer to be heated to high temperatures so that various chemical and physical transformations can take place as the wafer is fabricated into a device(s). During rapid thermal processing, for instance, semiconductor wafers can be heated by an array of lamps through the support plate to temperatures from about 300° C. to about 1,200° C., for times that are typically less than a few minutes. The range and duration of these temperatures is generally determined by a temperature setpoint profile. During these processes, a primary goal can be to adaptively control the thermal processing system to improve temperature setpoint profile tracking performance. 
     SUMMARY 
     Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or may be learned from the description, or may be learned through practice of the embodiments. 
     One example aspect of the present disclosure is directed to a thermal processing system. The system includes a processing chamber. The system includes a workpiece support operable to support a workpiece during thermal processing in the processing chamber. The system includes one or more heat sources operable to heat the workpiece in the processing chamber during thermal processing of the workpiece. The system includes one or more sensors configured to obtain data associated with a workpiece temperature. The system includes a control system configured to perform operations. The operations include, for instance, determining an actual workpiece temperature estimate based at least in part on the data associated with a workpiece temperature of a first workpiece during thermal processing of the workpiece; obtaining a simulated temperature estimate for the first workpiece using a system model, the system model providing the simulated temperature estimate based on one or more model parameters and one or more controller outputs; adjusting one or more controller parameters of a system controller based at least in part on a difference between the simulated temperature estimate obtained using the system model and a temperature setpoint; and controlling, by the system controller, one or more operating parameters of the thermal processing system based at least in part on the controller parameters to regulate a workpiece temperature of a second workpiece during thermal processing. 
     These and other features, aspects and advantages of various embodiments will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the related principles. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Detailed discussion of embodiments directed to one of ordinary skill in the art are set forth in the specification, which makes reference to the appended figures, in which: 
         FIG.  1    depicts an example rapid thermal processing (RTP) system having a support plate with spatially arranged low transmission zones according to example embodiments of the present disclosure; 
         FIG.  2    depicts an example schematic representation of a control routine operable to train a system model using a learning routine to determine workpiece temperature and other properties based on parameters of the thermal processing system according to example embodiments of the present disclosure; 
         FIG.  3    shows a schematic representation of a control routine operable to train a workpiece deformation submodel of a system model to emulate thermal processing system radiometric outputs according to example embodiments of the present disclosure; 
         FIG.  4    shows a schematic representation of a control routine operable to train a workpiece/chamber optical submodel of a system model to emulate thermal processing system optical outputs according to example embodiments of the present disclosure; 
         FIG.  5    shows a schematic representation of a control routine operable to train a workpiece/chamber thermal submodel of a system model to emulate thermal processing system thermal outputs according to example embodiments of the present disclosure; 
         FIG.  6    shows a schematic representation of a control routine operable to implement a control tuner to generate temperature setpoint tracking improvements for a thermal processing system during the processing of workpieces according to example embodiments of the present disclosure; 
         FIG.  7    depicts an example representation of results from applying one or more temperature setpoint tracking improvements to a thermal processing system according to example embodiments of the present disclosure; 
         FIG.  8    depicts a flow diagram of a process for adaptively controlling a thermal processing system according to example embodiments of the present disclosure; and 
         FIG.  9    depicts a flow diagram of a process for adaptively controlling a thermal processing system according to example embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Reference now will be made in detail to embodiments, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the embodiments, not limitation of the present disclosure. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments without departing from the scope or spirit of the present disclosure. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that aspects of the present disclosure cover such modifications and variations. 
     Example aspects of the present disclosure are directed to control systems for adaptively controlling the heat applied to workpieces in a thermal processing system. Workpieces can include, for instance, semiconductor workpieces, opto-electronic workpieces, flat panel displays, or other suitable workpieces. The workpiece materials can include, for instance, silicon, silicon germanium, glass, plastic, or other suitable material. In some embodiments, the workpieces can be semiconductor wafers. The heat can be applied to these workpieces based on a temperature setpoint profile. 
     A temperature setpoint profile can be configured to specify a plurality of different temperatures be applied to a workpiece over time to thermally treat the workpiece. For instance, the temperature setpoint profile can specify that a workpiece be heated at a first temperature for 10 seconds and ramped to and heated at a second temperature for less than 1 second. Both the range and number of temperatures of a temperature setpoint profile can be specified to occur at certain times. In some embodiments, the temperature setpoint profile can be specified or selected by a user or technician as part of a process recipe. 
     During processing, a control system for a thermal processing system can be operable to determine actual workpiece temperature estimate associated with a workpiece. An actual workpiece temperature estimate can be determined based on data from one or more sensors in the thermal processing system, including but not limited to radiometers, optical detectors, pyrometers, visible wavelength imaging systems, infrared imaging systems, or any other sensor(s) operable to estimate the temperature of a workpiece. 
     The control system can compare the actual workpiece temperature estimate with a temperature specified by the temperature setpoint profile to determine an error or difference between the actual workpiece temperature estimate and the temperature setpoint profile. The control system can adjust operating parameters of the thermal processing system (e.g., amount of heat emitted by the heat source, for instance, by controlling lamp power). According to example aspects of the present disclosure, the control system can implement control routines improve the performance of the thermal processing system by accurately tracking the temperature specified by a temperature setpoint profile relative to the actual temperature estimate of the workpiece during thermal processing to reduce error. 
     In some embodiments, the control system can access a system model. A system model can be trained to emulate the conditions and output of a thermal processing system. More specifically, a system model can be trained to provide a simulated temperature estimate associated with a workpiece, the simulated temperature estimate simulating an actual temperature estimate associated with the workpiece or an actual temperature output associated with the workpiece based on specified operating conditions of the thermal processing system. Furthermore, a system model can be trained to provide simulated workpiece properties associated with a workpiece, the simulated properties simulating actual temperature, optical, radiometric, emission, and other properties associated with a workpiece. 
     According to example aspects of the present disclosure, the system model can be a machine learned model. For example, the system model can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or combinations thereof. For example, the system model may be trained to provide a simulated temperature estimate by using a machine-learned neural network that is trained by a learning routine using data collected from the processing of test workpieces and/or actual workpieces. 
     In some embodiments, the system model can be trained using a system learning routine. An error value can be determined based at least in part on a difference between a simulated temperature estimate and an actual temperature workpiece estimate. A system learning routine can be a differentiable objective function operable to, when optimized, provide modifications for the system model that reduce the error value associated with a difference between a simulated workpiece temperature and an actual temperature output obtained from a test workpiece and/or the difference between simulated workpiece properties associated with a workpiece and actual temperature and optical properties associated with the workpiece. The system learning routine can receive one or more inputs, including but not limited to a difference between an actual system output and a simulated temperature estimate, and a difference between simulated workpiece properties and actual workpiece properties. 
     In some embodiments, a test workpiece can be used to obtain data associated with a workpiece temperature for training of the system model for a thermal processing system. Data associated with a workpiece temperature that is obtained using a test workpiece can be sufficiently accurate as to be considered an actual temperature output. A test workpiece can include one or more sensors operable to measure the heat being applied to the workpiece. For example, a test workpiece may include one or more thermocouples to measure the heat of the workpiece. The thermocouples can measure the heat at a precision sufficient to provide an actual temperature output. Furthermore, optical properties associated with the test workpiece can be known. Temperature and optical properties associated with the test workpiece can be measured and/or calculated in advance to assist in testing and/or calibrating thermal processing system performance. 
     In some embodiments, the system learning routine may receive a first temperature difference between an actual temperature output associated with a test workpiece and a simulated temperature estimate associated with the test workpiece. The system learning routine may also receive a first optical difference between an actual optical property associated with the test workpiece and an estimated optical property associated with the test workpiece. Based at least in part on the first temperature difference and/or the first optical difference, the system learning routine can generate one or more parameter modifications to modify one or more parameters associated with the system model. 
     For example, the system learning routine may produce a first simulated temperature estimate associated with a test workpiece that is significantly different than a first actual temperature output associated with the test workpiece. The system learning routine can generate one or more parameter modifications to modify one or more parameters associated with the system model. The control system can apply the one or more parameter modifications to the system model. The system model may then produce a second simulated temperature output associated with the test workpiece that is more accurate than the first simulated temperature output. 
     In some embodiments, the system model can include a plurality of submodels. A system model submodel can more effectively simulate one subsystem of the system. For example, the system model may include a workpiece deformation submodel to more accurately simulate the effect of workpiece deformation in the system. For another example, the system model may include a workpiece/chamber optical submodel to more accurately simulate workpiece optical properties. For yet another example, the system model may include a workpiece/chamber thermal submodel to more accurately simulate estimated workpiece temperatures. 
     In some embodiments, the control system can modify the parameters of the system model using an optimization method. An optimization method can be, but is not limited to, one or more of backpropagation, stochastic gradient descent, mini-batch gradient descent, or any other first or second order optimization method that can be applied to a machine learned model. For example, one or more modifications can be applied to the system model by utilizing a stochastic gradient descent method. For another example, the one or more modifications can be determined and applied using a backpropagation method in conjunction with a mini-batch gradient descent method. For yet another example, the one or more modifications can be determined and applied using a backpropagation method in conjunction with a different first order optimization method. 
     In some embodiments, the control system can include a system controller. The system controller can be configured to control one or more operating parameters of the thermal processing system. The operating parameters of the thermal processing system can include, but are not limited to, heat output, duration and/or intensity of heat, or any other adjustments to any components of the thermal processing system. For example, the system controller can control one or more operating parameters of the thermal processing system to adjust the output of heat from one or more components (e.g., lamps) of the thermal processing system. 
     In some embodiments, the control system can include a trusted control tuner. The trusted control tuner can adjust one or more system controller parameters of the system controller. Adjusting the one or more system controller parameters of the system controller can, at least in part, affect how the system controller controls the one or more operating parameters of the thermal processing system using system controller outputs. The one or more system controller parameters can be, for instance, gains used in a proportional integral controller, proportional derivative controller, and/or proportional integral derivative controller. 
     For example, the trusted control tuner may adjust one or more system controller parameters of the system controller. Based at least in part on the one or more system controller parameter adjustments, the system controller can adjust one or more operating parameters of the thermal processing system to adjust the output of heat from one or more components of the thermal processing system using system controller outputs. 
     In some embodiments, the control system may include a clone system controller operable to provide outputs indicative of controller parameters to the system model. The system model, based at least in part on the clone system controller outputs from the clone system controller, can provide a simulated workpiece temperature estimate. Initially, the clone system controller outputs can be clone controller outputs that mirror or clone the actual controller output of the control system of the thermal processing system. The clone system controller can include one or more clone controller parameters. The one or more clone controller parameters of the clone system controller can be, for instance, gains used in a proportional integral controller, proportional derivative controller, and/or proportional integral derivative controller. 
     In some embodiments, the one or more clone controller parameters can be adjusted by a clone control tuner. The clone control tuner can be operable to modify the one or more clone controller parameters of a clone system controller based on control tuner adjustments learned using a tuning learning routine. More specifically, a clone control tuner can be operable to provide one or more clone controller parameter adjustments to the clone system controller based at least in part on a difference between the simulated workpiece temperature estimate and a temperature setpoint. 
     In some embodiments, the clone control tuner can be a model (e.g., a machine learned model) that correlates errors to adjustments in controller parameters. For example, the clone control tuner can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or combinations thereof. For example, the clone control tuner may be trained to provide one or more clone controller parameter adjustments by using an artificial neural network. 
     In some embodiments, the clone control tuner can be trained by a tuning learning routine. A tuning learning routine can determine control tuner adjustments based on simulated temperature estimates of workpieces during thermal processing. In some embodiments, the tuning learning routine can implement a differentiable objective function operable to, when optimized, provide modifications for parameters of the clone control tuner that can reduce a difference between a simulated workpiece temperature estimate and the temperature setpoint. The tuning learning routine can receive one or more inputs, including but not limited to a difference between the simulated workpiece temperature estimate and the temperature setpoint. 
     For example, the tuning learning routine may receive a first temperature difference between a simulated workpiece temperature estimate associated with a workpiece and a temperature setpoint associated with the workpiece. Based at least in part on the first temperature difference, the tuning learning routine can generate and apply one or more control tuner adjustments to adjust one or more parameters of the clone control tuner. In conjunction, the clone control tuner can apply clone controller parameter adjustments to the clone system controller based at least in part on the control tuner adjustments of the tuning learning algorithm. These clone controller parameter adjustments can be operable to modify outputs provided by the clone system controller to the system model simulating performance of the thermal processing system, which can lead to a reduction in the difference between the workpiece temperature estimate and the temperature setpoint. 
     In some embodiments, the tuning learning routing can determine the control tuner adjustment using an optimization method. An optimization method can be, but is not limited to, one or more of backpropagation, stochastic gradient descent, mini-batch gradient descent, or any other first or second order optimization method. For example, one or more adjustments can be applied to the clone control tuner by utilizing a stochastic gradient descent method. For another example, the one or more adjustments can be determined and applied using a backpropagation method in conjunction with a mini-batch gradient descent method. For yet another example, the one or more adjustments can be determined and applied using a backpropagation method in conjunction with a different first order optimization method. 
     In some embodiments, the clone controller parameter adjustments generated by the clone control tuner can be recorded in a recipe type recorder. Each of the one or more clone controller parameter adjustments can be linked to one or more workpiece types or process recipes in the recipe type recorder. The recipe type recorder can be a database, data structure, record, etc. The recipe type recorder can track the performance of the simulated workpiece temperature estimate resulting from the clone controller parameter adjustments. 
     If the clone system controller controls the system model to produce a simulated workpiece temperature estimate that is more accurate than a previously-stored simulated workpiece temperature estimate stored in the recipe type recorder, a trigger condition can be satisfied. If the trigger condition is satisfied, the clone control tuner can provide the same clone controller parameter adjustments to the trusted control tuner. The trusted control tuner can generate one or more system controller parameter adjustments for the system controller based at least in part on the one or more clone controller parameter adjustments. 
     For example, the clone control tuner can produce one or more clone controller parameter adjustments that are determined to decrease the difference between the simulated workpiece temperature estimate and the temperature setpoint. The clone control tuner can provide these one or more clone controller parameter adjustments to the trusted control tuner. The trusted control tuner can generate one or more system controller parameter adjustments for the system controller based at least in part on the clone controller parameter adjustments. The system controller&#39;s controller parameters can be adjusted, resulting in a more accurate tracking of actual workpiece temperature with a temperature setpoint profile. 
     In some embodiments, the system will evaluate whether the one or more system controller parameter adjustments increases performance. If the actual workpiece temperature estimate is determined to increase performance, the trusted control tuner can retain the one or more system controller parameter adjustments. If the actual workpiece temperature estimate is determined to decrease performance, the control system can determine that an adjustment error has occurred. If an adjustment error has occurred, the trusted control tuner can discard the one or more system controller parameter adjustments and alert a system model watchdog. The system model watchdog can determine, based on the decreased performance, that the system model should be retrained. Alternatively, the system model watchdog may apply a system model modification to the system model, the system model modification operable to correct the adjustment error. 
     For example, the trusted control tuner may apply the one or more system controller parameter adjustments to the system controller. The actual workpiece temperature estimate may suffer from reduced performance in tracking the temperature setpoint. In response, the trusted control tuner can discard the one or more system controller parameter adjustments. Furthermore, the system model watchdog can determine the system model should be retrained and initiate retraining of the system model. 
     Aspects of the present disclosure can achieve a number of technical effects and benefits. For instance, aspects of the present disclosure can continuously reduce errors in temperature setpoint tracking of the system, leading to more efficient and productive thermal processing. In addition, aspects of the present disclosure can be used to generate more robust system models for a thermal processing system, leading to more accurate workpiece temperature estimates, etc. 
     Variations and modifications can be made to these example embodiments of the present disclosure. As used in the specification, the singular forms “a,” “and,” and “the” include plural referents unless the context clearly dictates otherwise. The use of “first,” “second,” “third,” and “fourth” are used as identifiers and are directed to an order of processing. Example aspects may be discussed with reference to a “substrate,” “wafer,” or “workpiece” for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that example aspects of the present disclosure can be used with any suitable workpiece. The use of the term “about” in conjunction with a numerical value refers to within 20% of the stated numerical value. 
     With reference now to the FIGS., example embodiments of the present disclosure will now be discussed in detail.  FIG.  1    depicts an example rapid thermal processing (RTP) system  100  having a support plate  120  with spatially arranged low transmission zones according to example embodiments of the present disclosure. As illustrated, the RTP system  100  includes a RTP chamber  105 , a workpiece  110 , a support plate  120 , heat sources  130  and  140  (e.g., lamps), air bearings  145 , a pyrometer  165 , a controller  175 , a door  180 , and a gas flow controller  185 . 
     The workpiece  110  to be processed is supported in the RTP chamber  105  (e.g., a quartz RTP chamber) by the support plate  120 . The support plate  120  is a workpiece support operable to support a workpiece  110  during thermal processing. The support plate  120  includes a rotatable base  135  and at least one support structure  115  extending from the rotatable base  135 . A support structure describes a structure contacting and supporting a workpiece during thermal processing. Examples of the support structure can include one or more support pins, a ring support, or any other suitable support that contacts and supports a workpiece. As shown in  FIG.  1   , the support structure  115  includes one or more support pins (only one shown). The support structure  115  and the rotatable base  135  can transmit heat from the heat sources  140  and to absorb heat from the workpiece  110 . In some embodiments, the support structure  115  and the rotatable base  135  can be made of quartz. The rotatable base  135  rotates the workpiece  110  at a defined rotation orientation and at a defined rotation speed, as further described below. 
     A guard ring (not shown) can be used to lessen edge effects of radiation from one or more edges of the workpiece  110 . An end plate  190  seals to the chamber  105 , and the door  180  allows entry of the workpiece  110  and, when closed, allows the chamber  105  to be sealed and a process gas  125  to be introduced into the chamber  105 . Two banks of heat sources operable to heat the workpiece in the processing chamber (e.g., lamps, or other suitable heat sources)  130  and  140  are shown on either side of the workpiece  110 . 
     A gas flow  150  can be an inert gas that does not react with the workpiece  110 , or the gas flow  150  can be a reactive gas such as oxygen or nitrogen that reacts with the material of the workpiece  110  (e.g. a semiconductor wafer, etc.) to form a layer of on the workpiece  110 . In some embodiments, an electrical current can be run through the atmosphere in the RTP system  100  to produce ions that are reactive with or at the surface, and to impart extra energy to the surface by bombarding the surface with energetic ions. 
     The controller  175  controls the rotatable base  135  to rotate the workpiece  110 . For example, the controller  175  generates an instruction that defines the rotation orientation and the rotation speed of the rotatable base  135  and controls the rotatable base  135  to rotate the workpiece  110  with the defined rotation orientation and the defined rotation speed. The rotatable base  135  is supported by the air bearings  145 . The gas flow  150  impinging on the rotatable base  135  causes the rotatable base  135  to rotate about an axis  155 . 
     The controller  175  is used to control the heat sources  130  and  140 . The controller  175  can be used to control the gas flow controller  185 , the door  180 , and/or the temperature measuring system, denoted here as the pyrometer  165 . The controller  175  can be modified dynamically to increase temperature setpoint tracking performance, which will be discussed in-depth in the following figures. 
     As used herein a controller, control system, or one or more components of a control system can include one or more processors and one or more memory devices. The one or more processors can be configured to execute computer-readable instructions stored in the one or more memory devices to perform operations, such as any of the operations for controlling a thermal processing system described herein. 
       FIG.  1    depicts an example thermal processing system  100  for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that aspects of the present disclosure can be used with other thermal processing systems for workpieces without deviating from the scope of the present disclosure. 
       FIG.  2    depicts an example schematic representation of a control routine  200  operable to train a system model using a learning routine to determine workpiece temperature and other properties based on parameters of the thermal processing system. 
     A system controller  200  (e.g., controller  175  in  FIG.  1   ) can be operable to provide controller outputs to control components of a thermal processing system  204  during processing of a test workpiece. The test workpiece can include one or more sensors operable to measure the temperature of the workpiece. For example, the test workpiece may include one or more thermocouples to measure the temperature of the workpiece. The one or more sensors of the test workpiece can record the temperature with sufficient accuracy to produce an actual system output  218 . Furthermore, the test workpiece can include actual workpiece properties  208 . Actual workpiece properties  208  can be measured and/or calculated in advance to assist in testing and/or calibrating thermal processing system performance. 
     The system controller  200  can be further operable to provide controller outputs to the system model  206 . The system model  206  can be trained to emulate the conditions and output of the thermal processing system with test workpiece  204 . More specifically, the system model  206  can be trained to provide a simulated temperature estimate  216  for the test workpiece  204  that simulates an actual system output  218 . Furthermore, the system model  206  can be trained to provide simulated workpiece properties  212  associated with the test workpiece of to simulate actual workpiece properties  208 . 
     In some embodiments, the system model  206  can be a machine learned model. For example, the system model  206  can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or combinations thereof. For example, the system model  206  may utilize a machine-learned neural network trained by a learning routine to provide a simulated temperature estimate  216 . 
     The system model  206  can be trained using a system learning routine  210 . The system learning routine  210  can be a differentiable objective function operable to, when optimized, provide parameter modifications  214  for the system model  206  that reduce the difference between the simulated temperature estimate  216  and the actual system output  218  and/or the difference between simulated workpiece properties  212  and actual workpiece properties  208 . The system learning routine  210  can receive one or more inputs, including but not limited to a difference between an actual system output  218  and a simulated temperature estimate  216 , and a difference between simulated workpiece properties  212  and actual workpiece properties  208 . 
     The control system  200  can apply the parameter modifications  214  to the system model  206  using an optimization method. An optimization method can be, but is not limited to, one or more of backpropagation, stochastic gradient descent, mini-batch gradient descent, or any other first or second order optimization method that can be applied to a machine learned model. For example, one or more modifications can be applied to the system model by utilizing a stochastic gradient descent method. For another example, the one or more modifications can be determined and applied using a backpropagation method in conjunction with a mini-batch gradient descent method. For yet another example, the one or more modifications can be determined and applied using a backpropagation method in conjunction with a different first order optimization method. 
     The system model  206  can include one or more submodels.  FIG.  3    depicts an example schematic representation of a control routine  300  operable to train a workpiece deformation submodel of a system model to emulate thermal processing system radiometric outputs according to example embodiments of the present disclosure. 
     Lamp power levels  302  can be determined based at least in part by a temperature set point  602 , as depicted in  FIG.  3   . The lamp power levels  302 , along with the known test workpiece temperature and optical properties  318 , are sent as inputs to the model parameter tuner  312 . The lamp power levels  302  are also sent as an input to the radiometer/chamber model  304 . 
     The radiometer/chamber physical model  304  can emulate the conditions and output of the radiometer of a thermal processing system based at least in part on the lamp power levels  302 . The radiometer/chamber model  304  includes a plurality of model parameters. The model parameters can be adjusted by the model parameter tuner  312  using model parameter adjustments  316 . 
     Similarly, the workpiece deformation model  306  can emulate the conditions and output of workpiece deformation during thermal processing. The workpiece deformation model also includes a plurality of model parameters operable to be adjusted by the model parameter tuner  312 . The workpiece deformation model  306 , in conjunction with radiometer/chamber model  304 , can produce estimated radiometer signals  320 . The estimated radiometer signals  320  are operable to estimate actual radiometer signals associated with the radiometer of the thermal processing system. 
     The model parameter tuner  312  can be trained to dynamically adjust the parameters of the radiometer/chamber model  304  and the workpiece deformation model  306  using model parameter adjustments  316 . The model parameter tuner  312  can be a machine learned model. For example, the model parameter tuner  312  can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or combinations thereof. For example, the model parameter tuner  312  may be an artificial neural network trained to dynamically adjust the parameters of the radiometer/chamber model  304  and the workpiece deformation model  306  using model parameter adjustments  316 . 
     The model parameter tuner  312  can be trained using a tuning learning routine  310 . The tuning learning routine  310  can be a differentiable objective function operable to, when optimized, provide tuning parameter adjustments  314  for the model parameter tuner that reduce the difference between the estimated radiometer signals  320  and the actual radiometer signals  308 . The tuning learning routine  310  can receive one or more inputs, including but not limited to a difference between the estimated radiometer signals  320  and the actual radiometer signals  308 . 
     The control routine  300  can apply the model parameter adjustments  316  to the radiometer/chamber model  304  and the workpiece deformation model  306  using an optimization method. An optimization method can be, but is not limited to, one or more of backpropagation, stochastic gradient descent, mini-batch gradient descent, or any other first or second order optimization method that can be applied to a machine learned model. For example, one or more adjustments can be applied to the radiometer/chamber model  304  and the workpiece deformation model  306  by utilizing a stochastic gradient descent method. For another example, the one or more adjustments can be determined and applied using a backpropagation method in conjunction with a mini-batch gradient descent method. For yet another example, the one or more adjustments can be determined and applied using a backpropagation method in conjunction with a different first order optimization method. 
       FIG.  4    depicts an example schematic representation of a control routine  400  operable to train a workpiece/chamber optical submodel of a system model to emulate thermal processing system optical outputs according to example embodiments of the present disclosure. 
     Actual radiometer signals  402  can be determined based at least in part by a temperature set point  602 , as depicted in  FIG.  4   . The actual radiometer signals  402 , along with the known test workpiece temperature and optical properties  418 , are sent as inputs to the model parameter tuner  412 . The actual radiometer signals  402  are also sent as an input to the workpiece/chamber optical model  404 . 
     The workpiece/chamber optical model  404  can emulate the conditions and output of the optical properties of a thermal processing system based at least in part on the actual radiometer signals  402 . The workpiece/chamber optical model  404  includes a plurality of model parameters. The model parameters can be adjusted by the model parameter tuner  412  using model parameter adjustments  416 . The workpiece/chamber optical model  404  can produce predicted workpiece optical properties  420 . The predicted workpiece optical properties  420  are operable to predict known workpiece optical properties  420  associated with a test workpiece of the thermal processing system. 
     The model parameter tuner  412  can be trained to dynamically adjust the parameters of the workpiece/chamber optical model  404  using model parameter adjustments  416 . The model parameter tuner  412  can be a machine learned model. For example, the model parameter tuner  412  can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or combinations thereof. For example, the model parameter tuner  412  may be an artificial neural network trained to dynamically adjust the parameters of the workpiece/chamber optical model  404  using model parameter adjustments  416 . 
     The model parameter tuner  412  can be trained using a tuning learning routine  410 . The tuning learning routine  410  can be a differentiable objective function operable to, when optimized, provide tuning parameter adjustments  414  for the model parameter tuner  412  that reduce the difference between the predicted workpiece optical properties  420  and the known workpiece optical properties  408 . The tuning learning routine  410  can receive one or more inputs, including but not limited to a difference between the predicted workpiece optical properties  420  and the known workpiece optical properties  408 . 
     The control routine  400  can apply the model parameter adjustments  416  to the workpiece/chamber optical model  404  using an optimization method. An optimization method can be, but is not limited to, one or more of backpropagation, stochastic gradient descent, mini-batch gradient descent, or any other first or second order optimization method that can be applied to a machine learned model. For example, one or more adjustments can be applied to the workpiece/chamber optical model  404  by utilizing a stochastic gradient descent method. For another example, the one or more adjustments can be determined and applied using a backpropagation method in conjunction with a mini-batch gradient descent method. For yet another example, the one or more adjustments can be determined and applied using a backpropagation method in conjunction with a different first order optimization method. 
       FIG.  5    depicts an example schematic representation of a control routine  500  operable to train a workpiece/chamber thermal submodel of a system model to emulate thermal processing system thermal outputs according to example embodiments of the present disclosure. 
     Lamp power levels  502  can be determined based at least in part by a temperature set point  602 , as depicted in  FIG.  5   . The lamp power levels  502 , along with the known test workpiece temperature and optical properties  518 , are sent as inputs to the model parameter tuner  512 . The lamp power levels  502  are also sent as an input to the workpiece/chamber thermal model  504 . 
     The workpiece/chamber thermal model  504  can emulate the conditions and output of the thermal properties of a thermal processing system based at least in part on the lamp power levels  502 . The workpiece/chamber thermal model  504  includes a plurality of model parameters. The model parameters can be adjusted by the model parameter tuner  512  using model parameter adjustments  516 . The workpiece/chamber thermal model  504  can produce estimated workpiece temperature  520 . The estimated workpiece temperature  520  is operable to predict a known workpiece temperature  520  associated with a test workpiece of the thermal processing system. 
     The model parameter tuner  512  can be trained to dynamically adjust the parameters of the workpiece/chamber thermal model  504  using model parameter adjustments  516 . The model parameter tuner  512  can be a machine learned model. For example, the model parameter tuner  512  can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or combinations thereof. For example, the model parameter tuner  512  may be an artificial neural network trained to dynamically adjust the parameters of the workpiece/chamber thermal model  504  using model parameter adjustments  516 . 
     The model parameter tuner  512  can be trained using a tuning learning routine  510 . The tuning learning routine  510  can be a differentiable objective function operable to, when optimized, provide tuning parameter adjustments  514  for the model parameter tuner  512  that reduce the difference between the estimated workpiece temperature  520  and the known workpiece temperature  508 . The tuning learning routine  510  can receive one or more inputs, including but not limited to a difference between the difference between the estimated workpiece temperature  520  and the known workpiece temperature  508 . 
     The control routine  500  can apply the model parameter adjustments  516  to the workpiece/chamber thermal model  504  using an optimization method. An optimization method can be, but is not limited to, one or more of backpropagation, stochastic gradient descent, mini-batch gradient descent, or any other first or second order optimization method that can be applied to a machine learned model. For example, one or more adjustments can be applied to the workpiece/chamber thermal model  504  by utilizing a stochastic gradient descent method. For another example, the one or more adjustments can be determined and applied using a backpropagation method in conjunction with a mini-batch gradient descent method. For yet another example, the one or more adjustments can be determined and applied using a backpropagation method in conjunction with a different first order optimization method. 
       FIG.  6    depicts an example schematic representation of a control routine  600  operable to implement a control tuner to generate temperature setpoint tracking improvements for a thermal processing system during the processing of workpieces according to example aspects of the present disclosure. The control routine  600  can determine improvements to controller performance based on actual processing of workpiece to improve temperature tracking with a temperature setpoint profile. 
     A temperature setpoint  602  can be a precise temperature value associated with a certain time during thermal processing of a workpiece as defined, for instance, by a temperature setpoint profile. The temperature setpoint  602  can be provided by a recipe type  634 . In some embodiments, the recipe type  634  can be determined and/or selected by a user or technician. The temperature setpoint  602  can also be recorded in the setpoint recorder  628 . A system controller  604  can control one or more operating parameters (e.g., heat source output, duration and/or intensity of heat, other adjustments to components of the thermal processing system, etc.) of thermal processing system  606  using system controller outputs  640 . The system controller outputs  640  can be based at least in part on the temperature setpoint  602 . The thermal processing system  606  can process the workpiece. Data from one or more sensors in the thermal processing system  606  can be processed to determine an actual workpiece temperature estimate associated with a workpiece of the thermal processing system  606 . The actual workpiece temperature estimate  608  can be based at least in part on the system controller outputs  640 . 
     For example, a temperature setpoint  602  may specify a temperature of 500 degrees Celsius. The system controller  604  can control the operating parameters of thermal processing system  606  using system controller outputs  640  to heat the workpiece in the thermal processing system  606  to 500 degrees Celsius. Data from sensors (e.g., a pyrometer) in the thermal processing system  606  can be processed to determine an actual workpiece temperature estimate  608  associated with an estimated temperature of the workpiece in the thermal processing system  606 . 
     A clone system controller  626  can output one or more operating parameters to a system model  616  using clone system controller outputs  618 . The clone system controller outputs  618  can be based at least in part on the temperature setpoint  602 . The clone system controller  626  can be a simulation of the system controller  604 . For instance, the clone system controller  626  can be operable to perform clone system controller outputs  618  that initially are substantially similar to system controller outputs  640 . 
     According to example embodiments of the present disclosure, the system model  616  can be a machine-learned model that is trained to simulate the thermal processing system  606 . The system model  616  can receive clone system controller outputs from the clone system controller  626  in the same manner that the thermal processing system  606  receives system controller outputs  640  from the system controller  604 . The system model  616  can be operable to model the conditions and output of the thermal processing system  606 . More specifically, the system model  616  can be trained to provide a simulated workpiece temperature estimate  610  associated with a first workpiece of thermal processing system  606 , the simulated workpiece temperature estimate  616  simulating an actual workpiece temperature estimate  608  associated with the first workpiece of thermal processing system  606 . 
     Furthermore, the system model  616  can be trained to provide predicted workpiece properties associated with a workpiece, including but not limited to simulated temperature and optical properties simulating actual temperature and optical properties associated with a workpiece. 
     In some embodiments, the system model  616  can be a machine learned model. For example, the system model can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or combinations thereof. For example, the system model  616  may be trained to provide a simulated workpiece temperature estimate by using an artificial neural network. 
     The simulated workpiece temperature estimate  610 , along with the actual workpiece temperature estimate  608 , can initially be provided to the system model watchdog  612 . The system model watchdog  612  can determine, based at least in part on a difference between the actual workpiece temperature estimate  608  and the simulated workpiece temperature estimate  610 , whether the system model  616  is sufficiently accurate. If the system model watchdog  612  determines that the system model  616  is not sufficiently accurate, the system model  616  can undergo a retraining phase substantially similar to the process previously depicted in  FIG.  2   . 
     If the system model watchdog  612  determines that the system model  616  is sufficiently accurate, the system model  616  can provide the simulated workpiece temperature estimate  610  to the tuning learning algorithm  620  and the clone control tuner  622 . 
     The clone control tuner  622  can be operable to generate one or more clone controller parameter adjustments  630  for the clone system controller  626 . More specifically, the clone control tuner  622  can be trained to provide one or more clone controller parameter adjustments  630  based at least in part on a difference between the simulated workpiece temperature estimate  610  and the temperature setpoint  602 . The clone control tuner  622  can be a machine learned model. For example, the clone control tuner  622  can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or combinations thereof. For example, the clone control tuner  622  may be trained to provide one or more clone controller parameter adjustments by using an artificial neural network. 
     The clone control tuner  622  can be trained using a tuning learning routine  620 . A tuning learning routine  620  can be a differentiable objective function operable to, when optimized, provide control tuner adjustments  636  for the clone control tuner  622  that can reduce a difference between the simulated workpiece temperature estimate  610  and the temperature setpoint  602 . Alternatively, or in combination, the clone control tuner  622  can provide clone controller parameter adjustments  630  based at least in part on a difference between predicted workpiece properties  632  and known workpiece properties associated with the recipe type  634  stored in recipe type recorder  624 . The tuning learning routine  620  can receive one or more inputs, including but not limited to a difference between the simulated workpiece temperature estimate  610  and the temperature setpoint  602 . 
     The control system  600  can adjust the parameters of the clone control tuner  622  using an optimization method. An optimization method can be, but is not limited to, one or more of backpropagation, stochastic gradient descent, mini-batch gradient descent, or any other first or second order optimization method that can be applied to a machine learned model. For example, one or more control tuner adjustments  636  can be applied to the clone control tuner  622  by utilizing a stochastic gradient descent method. For another example, the one or more adjustments can be determined and applied using a backpropagation method in conjunction with a mini-batch gradient descent method. For yet another example, the one or more adjustments can be determined and applied using a backpropagation method in conjunction with a different first order optimization method. 
     The clone controller parameter adjustments  636  generated by the clone control tuner  622  can be recorded in a recipe type recorder  634 . The recipe type recorder  644  can track the performance of the simulated workpiece temperature estimate  610  resulting from the clone controller parameter adjustments  630 . If the clone system controller  626  uses clone system controller outputs  618  to control the system model  616  to produce a simulated workpiece temperature estimate  610  that is more accurate than a simulated workpiece temperature estimate  610  previously stored in the recipe type recorder, the clone control tuner  622  can provide the same clone controller parameter adjustments  622  to a trusted control tuner  614 . 
     The trusted control tuner  614  can be operable to apply system controller parameter adjustments  638  to the system controller  604 . If the trusted control tuner  614  receives clone controller parameter adjustments  630  from the clone control tuner  622 , the trusted control tuner  614  can generate system controller parameter adjustments  638  that are substantially similar to the clone controller parameter adjustments  630 . The trusted control tuner  614  can apply the system controller parameter adjustments  638  to the system controller  604 , which can control the thermal processing system  606  based at least in part on the system controller parameter adjustments  638 . 
     For example, the clone control tuner  622  can produce one or more clone controller parameter adjustments  630  that are determined to decrease the difference between the simulated workpiece temperature estimate  610  and the temperature setpoint  602 . The clone control tuner  622  can then provide these one or more clone controller parameter adjustments  630  to the trusted control tuner  614 . The trusted control tuner  614  can generate one or more system controller parameter adjustments  638  that are substantially similar the one or more clone controller parameter adjustments  630 . The trusted control tuner  614  can apply the system controller parameter adjustments to the system controller  604 , adjusting the way that system controller  604  controls the thermal processing system  606  using system controller outputs  640 . 
     The control system  600  can evaluate whether the actual workpiece temperature estimate  608  is more accurate due to the one or more system controller parameter modifications  638 . If the actual workpiece temperature estimate  608  is determined to be more accurate, the trusted control tuner  614  will retain the one or more system controller parameter modifications  638 . If the actual workpiece temperature estimate  608  is determined to be less accurate, the trusted control tuner  614  will discard the one or more system controller parameter modifications  638  and alert the system model watchdog  612 . The system model watchdog  612  can determine, based on the decreased performance, that the system model  616  should be retrained. The system model  616  can be retrained in a manner substantially similar to the process depicted in  FIG.  2   . 
       FIG.  7    depicts an example representation  700  of results from applying one or more temperature setpoint tracking improvements to a thermal processing system according to example embodiments of the present disclosure. As illustrated, the representation  700  includes a temperature setpoint  702 , an actual workpiece temperature estimate  704 , an error example  706 , and a correction example  708 . 
     The representation  700  includes an error example  706  demonstrating a calculated difference between a temperature setpoint  702  and an actual workpiece temperature estimate  704  that exists before the system controller  604  has been adjusted by the trusted control tuner  614  of  FIG.  6   . The temperature setpoint  702  can be obtained from a recipe, and can call for specific temperatures in the thermal processing system to be applied at specific times. The actual workpiece temperature estimate  704  can be measured intermittently over time with the objective of tracking the temperature setpoint  702  as closely as possible. The accuracy of the actual workpiece temperature estimate  702  is based at least in part on the system controller outputs  640  of system controller  604 , as depicted in  FIG.  6   . 
     The correction example  708  demonstrates a calculated reduction in the difference previously depicted in error example  706  between the temperature setpoint  702  and the actual workpiece temperature estimate  704 . This reduction can be attributed, at least in part, to one or more system controller parameter adjustments  638  applied to the system controller  604  by trusted control tuner  614 , as depicted in  FIG.  6   . This correction process, as demonstrated by correction example  708 , is iterative, and can operate continuously during thermal processing to constantly improve the tracking of the actual workplace temperature estimate  704  to the temperate setpoint  602 . 
       FIG.  8    depicts a flow diagram of a process ( 800 ) for adaptively controlling a thermal processing system according to example embodiments of the present disclosure. The process ( 800 ) can be implemented using the control system  600 .  FIG.  8    depicts steps performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that various steps of any of the methods described herein can be omitted, expanded, performed simultaneously, rearranged, and/or modified in various ways without deviating from the scope of the present disclosure. In addition, various additional steps (not illustrated) can be performed without deviating from the scope of the present disclosure. 
     At ( 802 ), the process can include determining an actual workpiece temperature estimate based at least in part on the data associated with a workpiece temperature of a first workpiece during thermal processing of the workpiece. For example, in the embodiment of  FIG.  6   , the actual workpiece temperature estimate  608  can be determined based at least in part on system controller outputs  640  to the thermal processing system  606 , the actual workpiece temperature estimate  608  associated with a workpiece temperature of a first workpiece of the thermal processing system  606 . 
     At ( 804 ), the process can include obtaining a simulated temperature estimate for the first workpiece using a system model, the system model providing the simulated temperature estimate based on one or more model parameters and one or more controller outputs. For example, in the embodiment of  FIG.  6   , the simulated workpiece temperature estimate  610  can be determined based at least in part on clone system controller outputs  618  to the system model  616 , the simulated workpiece temperature estimate  610  associated with a workpiece temperature of a first workpiece of the thermal processing system  606 . 
     At ( 806 ), the process can include adjusting one or more controller parameters of a system controller based at least in part on a difference between the simulated temperature estimate obtained using the system model and the actual temperature estimate. For example, in the embodiment of  FIG.  6   , the trusted control tuner  614  can generate and implement system controller parameter adjustments  638  that are substantially similar to clone controller parameter adjustments  630 . The clone controller parameter adjustments  630  are generated at least in part on differences between the simulated workpiece temperature estimate  610  and the temperature setpoint  602 . 
     At ( 808 ), the process can include controlling, by the system controller, one or more operating parameters of the thermal processing system based at least in part on the controller parameters to regulate a workpiece temperature of a second workpiece during thermal processing. For example, in the embodiment of  FIG.  6   , the system controller  604  controls the operating parameters of thermal processing  606  using system controller outputs  640 . The system controller outputs  640  can be applied to the thermal processing system  606  for processing of a subsequent second workpiece of the thermal processing system  606 . 
       FIG.  9    depicts a flow diagram of a process ( 900 ) for adaptively controlling a thermal processing system according to example embodiments of the present disclosure. The process ( 900 ) can be implemented using the control system  200 .  FIG.  9    depicts steps performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that various steps of any of the methods described herein can be omitted, expanded, performed simultaneously, rearranged, and/or modified in various ways without deviating from the scope of the present disclosure. In addition, various additional steps (not illustrated) can be performed without deviating from the scope of the present disclosure. 
     At ( 902 ), the process can include obtaining an actual temperature output from one or more thermal sensors attached to a test workpiece. For example, in the embodiment of  FIG.  2   , a thermal processing system can include a test workpiece. The test workpiece can include one or more temperature sensors (e.g., one or more thermocouples) to measure the heat of the test workpiece. The thermocouples can measure the heat at a precision sufficient to provide an actual temperature output  218 . 
     At ( 904 ), the process can include obtaining a simulated temperature estimate for the test workpiece using the system model. For example, in the embodiment of  FIG.  2   , a system model  206  can produce a simulated temperature estimate  216  associated with the test workpiece of a thermal processing system  204 . 
     At ( 906 ), the process can include receiving one or more parameter modifications from a system learning routine associated with the system model, the one or more parameter modifications based at least in part on a difference between the simulated temperature estimate and the actual temperature output. For example, in the embodiment of  FIG.  2   , a system learning routine  210  can receive as an input a difference between a simulated temperature estimate  216  and an actual system output  218 . The system learning routine  210  can generate one or more parameter modifications  214  based at least in part on the difference between the simulated temperature estimate  216  and the actual system output  218 . 
     At ( 908 ), the process can include applying the one or more parameter modifications to the system model. For example, in the embodiment of  FIG.  2   , the control system  200  can apply the parameter modifications  214  received from the system learning routine  210  to the system model  206 , modifying the parameters of system model  206 . 
     While the present subject matter has been described in detail with respect to specific example embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the scope of the present disclosure is by way of example rather than by way of limitation, and the subject disclosure does not preclude inclusion of such modifications, variations and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.