Scanning radical sensor available for model training

Abstract

To solve a problem in which, as semiconductor devices continue to shrink to smaller feature sizes, the complexity of semiconductor wafer processing is increasing, a particular process may involve many different processing parameters (i.e., knobs) that can be individually controlled to provide the desired results on the wafer, and furthermore, once the final processing recipe is generated, chamber drift can occur during the many iterations of the process on different wafers, causing the wafer results to vary.SOLUTION: In embodiments, a plasma processing tool having an extendable probe is described. In embodiments, the plasma processing tool includes a chamber and a pedestal for supporting a substrate. In embodiments, there is an edge ring around the periphery of the pedestal. Further, a sensor is provided at the end of the probe. In embodiments, the probe is configured to extend above the pedestal.SELECTED DRAWING: Figure 1A

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Patent Application No. 17 / 545,618, filed December 8, 2021, the entire contents of which are incorporated herein by reference.

[0002] Embodiments relate to the field of semiconductor manufacturing, and in particular to methods and apparatus for measuring radicals in plasma processes and using the recorded information to build a digital twin plasma model.

[0003] 2. Description of Related Art As semiconductor devices continue to shrink to smaller feature sizes, the complexity of semiconductor wafer processing is increasing. A particular process may involve many different processing parameters (i.e., knobs) that can be individually controlled to provide a desired result on the wafer. For example, the desired result on the wafer may refer to a feature profile, a layer thickness, a layer's chemical composition, etc. As the number of knobs increases, the theoretical process space available for tuning and optimizing the process becomes much larger.

[0004] Furthermore, once a final process recipe is generated, chamber drift can occur during many iterations of the process on different wafers, resulting in varying wafer results. Chamber drift can be the result of erosion of consumable parts of the chamber, degradation of components (e.g., sensors, lamps, etc.), deposition of by-product films on surfaces, etc. Therefore, even after an extensive recipe generation process, further adjustments are necessary. Summary of the Invention

[0005] In embodiments, a plasma processing tool having an extendable probe is described. In embodiments, the plasma processing tool includes a chamber and a pedestal for supporting a substrate. In embodiments, there is an edge ring around the periphery of the pedestal. Further, a sensor is provided at the end of the probe. In embodiments, the probe is configured to extend above the pedestal.

[0006] In embodiments, methods disclosed herein include a method for training a plasma behavior model. In embodiments, the method includes performing a scan with a sensor in a plasma chamber to generate a two-dimensional radical map during a plasma process. The method may further include acquiring plasma parameters of the plasma process and combining the two-dimensional radical map with the plasma parameters to train the plasma behavior model.

[0007] In embodiments, the processing tool disclosed herein may further comprise a chamber and a pedestal for supporting the substrate. The processing tool may further comprise an edge ring around the outer periphery of the pedestal. In embodiments, a sensor is provided at the end of the probe. In embodiments, the probe is configured to extend above the pedestal. The tool may further comprise a plasma behavior model. In embodiments, the plasma behavior model is trained by a process including scanning the pedestal with the sensor and acquiring plasma parameters of the plasma process to generate a two-dimensional radical map during the plasma process. In embodiments, the method may further include combining the two-dimensional radical map with the plasma parameters to train the plasma behavior model. [Brief explanation of the drawings]

[0008] [Figure 1A] FIG. 1 is a perspective view of a portion of a chamber having an extendable probe with a sensor at the end of the probe above a substrate, according to an embodiment. [Figure 1B] FIG. 1 is a perspective view of a portion of a chamber having multiple extendable probes with sensors, according to an embodiment. [Figure 1C] FIG. 1 is a perspective view of a portion of a chamber having a displaceable extendable probe to sweep across the surface of a substrate, according to an embodiment. [Figure 2] 1 is a schematic of a sensor that may be used in the chamber described above, according to an embodiment. [Figure 3] 3 is a graph of the temperature of the catalytic wire of the sensor of FIG. 2 at different plasma settings, according to an embodiment. [Figure 4] FIG. 1 is a block diagram of a plasma processing tool architecture having a plasma behavior model as part of the processing tool's digital twin, according to an embodiment. [Figure 5] FIG. 1 is a flow diagram illustrating a process for training a plasma behavior model, according to an embodiment. [Figure 6] FIG. 1 is a block diagram of an exemplary computer system that may be used in conjunction with a processing tool, according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] The systems described herein include methods and apparatus for measuring radicals in plasma processes and using the recorded information to build a digital twin plasma model. In the following description, numerous specific details are set forth to provide a thorough understanding of the embodiments. It will be apparent to those skilled in the art that the embodiments may be practiced without these specific details. In other instances, well-known aspects have not been described in detail so as not to unnecessarily obscure the embodiments. Furthermore, it should be understood that the various embodiments illustrated in the accompanying figures are illustrative representations and are not necessarily drawn to scale.

[0010] As discussed above, a semiconductor processing environment includes many different knobs that can be adjusted to obtain a desired result on a substrate, such as a semiconductor wafer. To further complicate matters, process drift increases the difficulty of processing substrates while meeting desired uniformity specifications. Accordingly, embodiments disclosed herein include the use of a digital twin to assist in the processing of substrates. The digital twin can be used to monitor process drift and modify one or more process knobs to counteract drift conditions in the chamber. The digital twin can also be used to identify when planned maintenance (PM) is required to correct process drift.

[0011] In embodiments, the digital twin may include a statistical model and a physical model. In embodiments involving plasma processes, a plasma behavior model may also be included as part of the digital twin. In embodiments, the plasma behavior model may be trained by using one or more sensors in the chamber to provide a radical and / or ion map. For example, a Pirani-type sensor may be used to determine the radical concentration in the chamber. In one embodiment, the sensor is at the end of a telescopic probe. Such an embodiment allows for a one-dimensional map of radical concentration across the width of the substrate. In another embodiment, multiple telescopic probes are used to provide a two-dimensional map of radical concentration on the substrate. In yet another embodiment, a telescopic sensor is used that can be swept across the surface of the substrate to provide a two-dimensional map of radical concentration.

[0012] 1A, a perspective view of a portion of a plasma chamber 160 is shown, according to an embodiment. In the embodiment, a substrate 161 is supported within the chamber 160. For example, the substrate 161 may be a semiconductor substrate such as a silicon wafer. An edge ring 163 may surround the outer periphery of the substrate 161. A chamber wall 164 may surround the outer periphery of the edge ring 163.

[0013] In an embodiment, probe 162 may be attached to edge ring 163. Probe 163 may be configured to extend above the surface of substrate 161. Sensor 110 may be provided at the end of probe 162 above substrate 161. While probe 162 is shown attached to edge ring 163, it should be understood that probe 162 may be coupled to any surface within plasma chamber 160.

[0014] In certain embodiments, the sensor 110 comprises a catalytic wire. The catalytic wire may be a platinum wire or a nickel wire in some embodiments. In embodiments, the probe 162 may further comprise a second catalytic wire (not shown) coated with a non-catalytic layer, such as SiO2 or Al2O3. The coated second catalytic wire may alternatively be provided on another probe (not shown in FIG. 1A). As described in more detail below, the catalytic wire may be used to measure the concentration of radical species within the chamber 160. For example, the catalytic wire may initiate recombination of radical species. The recombination increases the temperature of the catalytic wire. The increase in temperature may be correlated with the resistance of the catalytic wire, which can be measured. Other embodiments may include the sensor 110 for measuring the ion energy distribution within the chamber 160. While the sensor 110 is shown having a catalytic wire, it should be understood that the sensor may be any sensor suitable for measuring parameters of the plasma within the chamber 160. For example, in some other embodiments, the sensor 110 may be an optical sensor.

[0015] 1A, the probe 162 may be a telescoping probe 162. That is, the probe 162 may extend across the substrate 161, or the probe may be fully retracted such that no part of the probe 162 or the sensor 110 is above the substrate 161. In such an embodiment, the sensor 110 may be used to provide a one-dimensional map of radical concentration and / or ion energy distribution. That is, a reading of the sensor 110 may be provided as the sensor 110 moves linearly across the surface of the substrate 161. In certain embodiments, the probe 162 extends across the diameter of the substrate (i.e., the sensor 110 passes over the center point of the substrate 161).

[0016] In embodiments, the probe 162 may be coupled to an external computing system that stores data and / or controls the sensor. One or more wires at the end of the probe attached to the edge ring 163 may pass through a vacuum feedthrough through the chamber wall 164 or through an O-ring (not shown) between the chamber lid (not shown) and the chamber wall 164. In other embodiments, wireless communication may be used to couple the sensor 110 to an external computing device.

[0017] 1B, a perspective view of a plasma chamber 160 according to a further embodiment is shown. The plasma chamber 160 may be similar to the plasma chamber 160 of FIG. 1A, but with the addition of additional probes 162 and sensors 110. For example, three probes 162A-162C are shown in FIG. 1B. However, it should be understood that any number of probes 162 may be used to provide the desired spatial resolution of the radical density and / or ion energy distribution. That is, each of the probes 162 may provide a line scan across the substrate 161, and multiple line scans may be combined to provide a two-dimensional map of radicals and / or ions within the plasma chamber 160 during plasma processing.

[0018] 1C , a perspective view of a plasma chamber 160 is shown, according to a further embodiment. In addition to being an extendable probe 162, the probe 162 can be scanned across the surface of the substrate 161, as indicated by the dashed arrow. For example, in some embodiments, the probe 162 can be swept across the surface of the substrate 161, similar to a wiper. The sweeping motion can be combined with the extension and retraction of the probe 162 to cover the entire substrate 161. Such an embodiment can enable the generation of an entire two-dimensional map of radical density and / or ion energy distribution using a single probe 162 and sensor 110.

[0019] In yet another embodiment, the multiple sensors 110 may be provided on a displaceable structure. In use, the displaceable structure allows the multiple sensors 110 to be moved over the surface of the substrate 161 (or over a dummy substrate, or over a substrate holder without a substrate). In an embodiment, the multiple sensors 110 may be positioned to provide a one- or two-dimensional map of radical density and / or ion energy distribution. When not in use, the structure may be moved so that no components are provided on the substrate 161. In this way, the sensors 110 may provide a one- or two-dimensional map without a scanning process.

[0020] Referring now to FIG. 2, a schematic diagram of a sensor 200 is shown, according to an embodiment. The sensor 200 may be used as the sensor 110 at the end of the probe 162 of FIGS. 1A-1C, described in more detail above. In an embodiment, the sensor 200 includes a Wheatstone bridge configuration. That is, a set of four resistors 210, 212, 214, and 216 may be electrically coupled to each other in a ring configuration. In an embodiment, the first resistor 210 and the second resistor 212 may be formed by catalytic wire. For example, the catalytic wire may be a material that aids in the recombination of radical ions. For example, for hydrogen and oxygen radical ions, the first catalytic wire may include platinum or nickel. Of course, other types of catalytic wire may be included for different plasma species.

[0021] In an embodiment, the first resistor 210 and the second resistor 212 may be substantially similar to each other. The difference between the first resistor 210 and the second resistor 212 is that the second resistor 212 is coated with a non-catalytic material 215. For example, the second resistor 212 may be coated with a material 215 including silicon and oxygen (e.g., SiO) or aluminum and oxygen (e.g., AlO). In an embodiment, the coating 215 is deposited on the second resistor 212 using any suitable deposition process. In a particular embodiment, the coating 215 is provided on the second resistor 212 using an atomic layer deposition (ALD) process. Because the second resistor 212 is coated with the coating 215, radical recombination on the second resistor is prevented. Therefore, the second resistor 212 may be used as a reference value to which the temperature of the first resistor 210 is compared.

[0022] In an embodiment, the catalytic wire is heated to a certain temperature. The voltage required to do this is monitored using a Wheatstone bridge configuration. The change in voltage correlates with the temperature change of the catalytic wire induced by the recombination of radical ions. In an embodiment, there is a linear relationship between the temperature and resistance of the catalytic wire. Therefore, the change in resistance can be measured to detect the temperature change. The higher the temperature, the higher the radical concentration.

[0023] Referring now to FIG. 3 , a graph of the temperature of the first catalytic wire 210 over time is shown, according to an embodiment. Up until about 625 seconds, the plasma is argon plasma only. Therefore, there is no heating due to radical ion recombination. For example, the temperature of the first catalytic wire 210 may be about 100° C. At about 625 seconds, a process gas, such as oxygen and hydrogen, may be added to the chamber. The process gas is ionized to form radical ion species. As shown in the first step 321, the temperature of the first catalytic wire 210 increases. Increasing the power from 1 KW in the first step 321 to 2 KW in the second step 322 increases the temperature. Further increasing the power to 3 KW in the third step 323 increases the temperature even further. Therefore, changes in the temperature of the catalytic wire 210 (and therefore changes in the resistance of the catalytic wire) may be correlated with changes in radical ion flux. In embodiments, the catalytic wire 210 is configured to provide rapid changes in temperature. This is made possible by having a low-mass wire. Therefore, changes to the radical ion flux can be detected quickly.

[0024] Sensors such as those described in FIG. 2 can be used to generate and train a plasma behavior model. The plasma behavior model can be one component of a digital twin of a processing tool. A digital twin can refer to a model of a physical chamber that exhibits results that substantially match those of the physical chamber. For example, for a particular set of inputs provided to the physical chamber and the digital twin, both the physical chamber and the digital twin output substantially the same substrate results. Due to the similarity between the digital twin and the physical chamber, the digital twin can be used to determine process optimization, drift detection, planned maintenance schedules, etc.

[0025] 4, a schematic of a processing tool 400 is shown, according to an embodiment. As shown, a data model server 420 may be integrated with the processing tool 400. For example, the data model server 420 may be communicatively coupled to a front-end server 460 by a network connection, as indicated by the arrow. However, in other embodiments, the data model server 420 may be external to the processing tool 400. For example, the data model server 420 may be communicatively coupled to the processing tool 400 via an external network, etc.

[0026] In some embodiments, the data model server 420 may be referred to as a digital twin of the processing tool. That is, the components of the data model server 420 may represent a virtual copy of the physical processing tool. Thus, inputs to the data model server 420 result in outputs that match the outputs exhibited by the physical processing tool.

[0027] In an embodiment, data model server 420 may include a physical model 427, a statistical model 425, and a plasma model 428. Statistical model 425, physical model 427, and plasma model 428 may be communicatively coupled to a database 430 for storing input data (e.g., sensor data, model data, measurement data, etc.) used to build and / or update statistical model 425, physical model 427, and plasma model 428.

[0028] In embodiments, the statistical model 425 may be generated by implementing a physical design of experiments (DoE) and may use interpolation to provide an expanded process space model. In embodiments, the physical model 427 may be generated using real-world physics and chemistry relationships. For example, physical and chemical equations for various interactions within a processing chamber may be used to build a physical model. In embodiments, the plasma model 428 may be generated using a sensor that detects radical ion concentration on the substrate. The sensor may be similar to the sensors described in more detail above. In some embodiments, the plasma model 428 may be generated using a one-dimensional radical and / or ion concentration map or a two-dimensional radical and / or ion concentration map.

[0029] In an embodiment, processing tool 400 may include a front-end server 460, a tool control server 450, and tool hardware 440. Front-end server 460 may include a user interface 465 for data model server 420. User interface 465 provides an interface for process engineers to utilize data modeling to perform various operations, such as recipe drift monitoring.

[0030] The tool control server 450 may include a smart monitoring and control block 455. The smart monitoring and control block 455 may include modules that provide diagnostics and other monitoring of the processing tool 400. The modules may include, but are not limited to, health checks, sensor drift, defect recovery, and leak detection. The smart monitoring and control block 455 may receive data as input from various sensors implemented in the tool hardware. The sensors may include standard sensors 447 typically present in semiconductor manufacturing tools 400 to enable operation of the tool 400. For example, the sensors 447 may include control loop sensors. The control loop sensors may include sensors that are part of a feedback loop to control a set of process parameters used to process substrates. The sensors may also include monitoring sensors 445 that are added to the tool 400. The monitoring sensors 445 may include sensors that are outside of the feedback loop. That is, the output from the monitoring sensors 445 is not directly used to control the process in the chamber.

[0031] Monitoring sensors 445 provide additional information necessary for building highly detailed data models. For example, monitoring sensors may include physical sensors and / or virtual sensors. Virtual sensors may utilize data acquired from two or more physical sensors and use calculations to provide additional sensor data not typically available from physical sensors alone. Monitoring sensors 445 may include any type of sensor, such as, but not limited to, pressure sensors, temperature sensors, gas flow sensors, and gas concentration sensors. In embodiments, monitoring sensors 445 may also include sensors for detecting radical and / or ion concentrations, as well as the sensors described in more detail above. In embodiments, smart monitoring and control block 455 may provide data used by data model server 420. In other embodiments, output data from various monitoring sensors 445 may be provided directly to data model server 420.

[0032] 5, a flow diagram illustrating a process 590 for generating a plasma behavior model similar to plasma model 428 of FIG. 4 is shown, according to an embodiment. In an embodiment, plasma model 428 is generated using one or more sensors to detect plasma characteristics within the chamber during plasma processing. For example, the sensors may be configured to read radical and / or ion concentrations above a substrate in one or two dimensions during plasma processing. The radical and / or ion maps may be used in combination with other plasma characteristics to generate the plasma behavior model.

[0033] In embodiments, process 590 begins with step 591, which includes scanning with a sensor in the chamber during the plasma process to generate a radical and / or ion map. In some embodiments, the sensor is at the end of a probe. In some embodiments, the probe can be a telescopic probe to provide a one-dimensional map. That is, the probe can scan the sensor linearly across the width of the substrate. In some embodiments, a line scan can pass the sensor over the center point of the substrate. In other embodiments, multiple telescopic probes, each with its own sensor, can be used to provide a two-dimensional map. In yet other embodiments, a single telescopic probe can scan (e.g., in a wiper pattern) to provide a two-dimensional map of radicals and / or ions. In yet other embodiments, a structure with multiple sensors can be used to form a one-dimensional or two-dimensional map of radicals and / or ions without a scanning process. In such embodiments, the structure can be stored from above the substrate when not in use.

[0034] In embodiments, the sensor or sensors in step 591 may be similar to those described in more detail above. For example, the sensors may include Pirani-type gauges. In such embodiments, a pair of catalytic wires are connected as part of a Wheatstone bridge. A first catalytic wire is exposed and a second catalytic wire is surrounded by a non-catalytic coating to provide a reference signal against which the first catalytic wire is compared.

[0035] In embodiments, process 590 may continue with step 592, which includes obtaining plasma parameters of the plasma process. In embodiments, the plasma parameters may include one or more of RF power, microwave power, VSWR, reflected power, and match settings. In some embodiments, data regarding temperature, pressure, flow rate, temperature, etc. may be collected to form two-dimensional maps and / or other distributions of such characteristics. The plasma parameters may be sensed using control loop sensors, monitor sensors, or the settings of various knobs that are controlled to provide the desired plasma results.

[0036] In embodiments, process 590 may continue with step 593, which includes combining the radical map and / or ion map with plasma parameters to train a plasma behavior model. In embodiments, combining the radical map and / or ion map with plasma parameters may include associating the plasma parameters with values ​​of the radical map and / or ion map. Thus, knowledge of plasma parameters in subsequent processing steps may be used to generate the radical map and / or ion map without the need to use radical and / or ion sensors.

[0037] In embodiments, training of the plasma behavior model may be implemented using DoE. For example, a few standard DoE wafer sets may provide sufficient initial data to initiate a learning model. Such models have been shown to converge to a process solution more quickly and closely than human iterative testing. After initial training, radical and / or ion sensors on the end of the probe may be periodically used to obtain data to retrain and / or refine the plasma behavior model. For example, training may be performed once per lot, after a period of inactivity in the chamber, at scheduled time intervals (e.g., once per hour, once per day, etc.), or after a PM event.

[0038] In embodiments, the plasma process is a plasma etching or plasma deposition process. In other embodiments, the plasma process is a plasma surface treatment. For example, the plasma process can be a nitridation, oxidation, or a wide variety of surface treatments useful in semiconductor manufacturing process flows.

[0039] Referring now to FIG. 6 , a block diagram of an exemplary computer system 600 of a processing tool is shown, according to an embodiment. In an embodiment, the computer system 600 is coupled to the processing tool and controls processing at the processing tool. The computer system 600 may be connected to (e.g., networked with) other machines in a local area network (LAN), an intranet, an extranet, or the Internet. The computer system 600 may operate in the role of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The computer system 600 may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web appliance, a server, a network router, switch, or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify operations to be performed by the machine. Furthermore, although only a single machine is shown as computer system 600, the term "machine" should also be interpreted to include any collection of machines (e.g., computers) that individually or jointly execute a set (or sets) of instructions to perform any one or more of the methods described herein.

[0040] The computer system 600 may include a computer program product, or software 622, having a non-transitory machine-readable medium having instructions stored thereon, which may be used to program the computer system 600 (or other electronic devices) to perform processes according to embodiments. A machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). For example, machine-readable (e.g., computer-readable) media includes machine-readable storage media (e.g., read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.), machine-readable transmission media (electrical, optical, acoustic, or other forms of propagated signals (e.g., infrared signals, digital signals, etc.)), etc.

[0041] In an embodiment, computer system 600 includes a system processor 602, a main memory 604 (e.g., dynamic random access memory (DRAM) such as read only memory (ROM), flash memory, synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM)), etc.), a static memory 606 (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory 618 (e.g., a data storage device), which communicate with each other via a bus 630.

[0042] System processor 602 represents one or more general-purpose processing devices, such as a microsystem processor, a central processing unit, or the like. More specifically, the system processor may be a complex instruction set computing (CISC) microsystem processor, a reduced instruction set computing (RISC) microsystem processor, a very long instruction word (VLIW) microsystem processor, a system processor implementing other instruction sets, or a system processor implementing a combination of instruction sets. System processor 602 may also be one or more special-purpose processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal system processor (DSP), a network system processor, or the like. System processor 602 is configured to execute processing logic 626 for performing the operations described herein.

[0043] Computer system 600 may further include a system network interface device 608 for communicating with other devices or machines. Computer system 600 may also include a video display device 610 (e.g., a liquid crystal display (LCD), a light emitting diode display (LED), or a cathode ray tube (CRT)), an alphanumeric input device 612 (e.g., a keyboard), a cursor control device 614 (e.g., a mouse), and a signal generation device 616 (e.g., a speaker).

[0044] The secondary memory 618 may include a machine-accessible storage medium 632 (or, more specifically, a computer-readable storage medium) having stored thereon one or more sets of instructions (e.g., software 622) that embody any one or more of the methods or functions described herein. This software 622 may also reside, completely or at least partially, within the main memory 604 and / or the system processor 602 while being executed by the computer system 600, with the main memory 604 and the system processor 602 also constituting machine-readable storage media. The software 622 may further be transmitted or received over the network 620 via the system network interface device 608. In embodiments, the network interface device 608 may operate using RF, optical, acoustic, or inductive coupling.

[0045] Although the exemplary embodiment depicts machine-accessible storage medium 632 as a single medium, the term "machine-readable storage medium" should be interpreted to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) on which one or more sets of instructions are stored. The term "machine-readable storage medium" should also be interpreted to include any medium capable of storing or encoding a set of instructions for execution by a machine and causing the machine to perform any one or more of the methods. Thus, the term "machine-readable storage medium" should be interpreted to include, but not limited to, solid-state memory, optical media, and magnetic media.

[0046] In the foregoing specification, certain exemplary embodiments have been described. It will be apparent that various modifications may be made to such embodiments without departing from the scope of the following claims. The specification and drawings are, therefore, to be regarded in an illustrative rather than a restrictive sense.

Claims

1. 1. A plasma processing tool comprising: a chamber; a pedestal for supporting the substrate; an edge ring around the outer periphery of the pedestal; The sensor at the end of the probe wherein the probe is configured to extend above the pedestal.

2. The plasma processing tool of claim 1 , wherein an end of the probe is coupled to the edge ring.

3. The plasma processing tool of claim 1 , wherein the probe is a telescoping probe.

4. The plasma processing tool of claim 1 , wherein the probe is configured to scan to provide a two-dimensional map of the parameter sensed by the sensor.

5. The plasma processing tool of claim 4 , wherein the sensed parameter is radical concentration.

6. The plasma processing tool of claim 4 , wherein the two-dimensional map is used to train a digital twin of the plasma processing tool.

7. 7. The plasma processing tool of claim 6, wherein the digital twin of the plasma processing tool is used to control process parameters within the plasma processing tool.

8. The plasma processing tool of claim 1 , wherein the sensor is a Pirani gauge sensor.

9. The Pirani gauge sensor a first catalytic wire; a second catalytic wire covered with a non-catalytic material; The plasma processing tool of claim 8 , comprising:

10. 10. The plasma processing tool of claim 9, wherein the first catalytic wire and the second catalytic wire comprise platinum or nickel, and the non-catalytic material comprises silicon and oxygen, or aluminum and oxygen.

11. The plasma processing tool of claim 1 , further comprising a plurality of sensors on the end of a plurality of probes, each of the plurality of probes configured to extend above the pedestal.

12. 1. A method for training a plasma behavior model, comprising: scanning with a sensor within the plasma chamber to generate a two-dimensional radical map during the plasma process; acquiring plasma parameters of the plasma process; combining the two-dimensional radical map with the plasma parameters to train the plasma behavior model; A method comprising:

13. The method of claim 12 , wherein the sensor scans over a support for holding a substrate.

14. The method of claim 12 , wherein the plasma parameters include one or more of RF power, microwave power, VSWR, reflected power, and match setting.

15. The method of claim 12 , wherein the plasma behavior model is used to predict and / or control process uniformity.

16. The method of claim 15 , wherein the process uniformity comprises layer thickness uniformity and / or element dose uniformity.

17. The method of claim 12 , wherein the plasma behavior model is used for an oxidation process, a nitridation process, or any other surface treatment of a substrate.

18. 1. A semiconductor processing tool comprising: a chamber; a pedestal for supporting the substrate; an edge ring around the outer periphery of the pedestal; a sensor at the end of the probe, the probe configured to extend above the pedestal; and 1. A plasma behavior model, the plasma behavior model comprising: scanning the sensor across the pedestal to generate a two-dimensional radical map during a plasma process; acquiring plasma parameters of the plasma process; combining the two-dimensional radical map with the plasma parameters to train the plasma behavior model; A plasma behavior model is trained by a process including 1. A semiconductor processing tool comprising:

19. The semiconductor processing tool of claim 18 , wherein the probe is a telescoping probe.

20. 20. The semiconductor processing tool of claim 18, wherein the sensor comprises a first catalytic wire and a second catalytic wire, the second catalytic wire being coated with a non-catalytic material.

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