Etch feedback for control of upstream process

Abstract

To provide a substrate processing system for performing feedback control of an upstream process based on sensor measurements generated during an etch process.SOLUTION: A substrate processing system 300 comprises an etch processing chamber 303 for performing an etch process on a substrate and an optical sensor including a spectrometer to generate one or more optical measurements of a film on the substrate during and / or after the etch process. The system further comprises a server operatively connected to the etch chamber, where the computing device is to: receive the one or more optical measurements of the film; determine, for each optical measurement of the one or more optical measurements, a film thickness of the film; determine an etch rate of the film based on the one or more optical measurements using the determined film thickness of each optical measurement of the one or more optical measurements; and determine a process parameter value of at least one process parameter for a previously performed process that was performed on the substrate based on the etch rate.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to feedback control of upstream processes based on sensor measurements generated during an etch process. [Background technology]

[0002] To develop a manufacturing process sequence for forming components on a substrate, an engineer will perform one or more design of experiments (DoE) to determine process parameter values ​​to use for each process in the manufacturing process sequence. For DoE, typically, multiple different process parameter values ​​are tested by processing a substrate with different process parameter values ​​for each manufacturing process. This includes performing destructive measurements on the substrate that destroy the substrate. Once the process parameter values ​​are determined, the substrate is processed according to the manufacturing process sequence, and predetermined process parameter values ​​determined based on the results of the DoE are used for each process in the manufacturing process sequence. Engineers expect the processed substrate to have similar properties to the substrate processed during DoE. However, there is often a variation between the film properties determined during DoE and the film properties of the film on the product substrate. Furthermore, each process chamber may be slightly different from other process chambers and may produce films with different film properties. Also, process chambers may age, so the films produced by those process chambers may age even when using the same process recipe.

[0003]

[0003] Sometimes, non-production substrates (also called dummy wafers) are processed using predetermined process parameter values ​​for a process, and the processed substrates can be measured using the same destructive measurement techniques used during DoE to determine whether the designed process is performing as expected. However, processing the non-production substrates takes time, during which the process chamber is not being used to process product substrates. In addition, performing destructive measurements also takes time and manpower. Summary of the Invention

[0004] Some of the embodiments described herein are directed to a substrate processing system including an etch chamber and a computing device operably connected to the etch chamber. The etch chamber is configured to perform an etch process on the substrate and includes an optical sensor for generating one or more optical measurements of a film on the substrate at least one of during or after the etch process. The computing device receives the one or more optical measurements of the film. The computing device determines a film thickness of the film for each optical measurement of the one or more optical measurements. The computing device determines an etch rate of the film based on the one or more optical measurements using the determined film thickness of each optical measurement of the one or more optical measurements. The computing device determines a process parameter value of at least one process parameter for a previously performed process performed on the substrate based on the etch rate. The determined process parameter value of the at least one process parameter may be used to adjust the at least one process parameter for a future substrate.

[0005]

[0005] In an additional or related embodiment, a method includes performing an etching process to etch a film on a substrate, the film having been previously formed on the substrate using a deposition process; generating one or more optical measurements of the film on the substrate at least one of during or after the etching process; determining a film thickness of the film for each optical measurement of the one or more optical measurements; determining an etch rate of the film based on the one or more optical measurements using the determined film thickness for each optical measurement of the one or more optical measurements; and determining a process parameter value of at least one process parameter for the deposition process performed on the substrate based on the etch rate.

[0006] In some embodiments, a method includes receiving or generating a training data set including a plurality of data items, each data item of the plurality of data items including a process parameter value for at least one process parameter used in a manufacturing process to form a film on a substrate, a film quality value for a film formed by the manufacturing process performed using the process parameter value for the at least one process parameter, and an etch rate for an etch process used to etch the film. The method further includes training a model based on the training data set to take the etch rate as an input and output at least one of a predicted film quality value or a predicted process parameter value for the at least one process parameter based on the etch rate.

[0007]

[0007] Numerous other features are provided in accordance with these and other aspects of the present disclosure. Other features and aspects of the present disclosure will become more fully apparent from the following detailed description, the claims, and the accompanying drawings.

[0008]

[0008] The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals indicate like elements. It should be noted that various references to "an" or "one" embodiment in this disclosure are not necessarily to the same embodiment, and such references mean at least one. [Brief description of the drawings]

[0009] [Figure 1A] 1 is a top schematic diagram of a first example manufacturing system according to an embodiment. [Figure 1B]

[0010] 1 is a top schematic view of a second manufacturing system according to an embodiment. [Diagram 2]

[0011] 1 illustrates a schematic side view of a portion of a substrate including a poly plug, a DRAM bitline stack, and a hardmask layer, according to an embodiment. [Diagram 3]

[0012] FIG. 2 illustrates a simplified side view of an etch chamber and an optical sensor that performs in-line optical measurements related to the etch process, according to an embodiment. [Figure 4A]

[0013] 1 is a flow chart for a method of performing feedback control of an upstream process based on optical measurements generated during and / or after an etch process, according to an embodiment. [Figure 4B]

[0014] 1 is a flowchart for a method of determining process parameter values ​​for one or more process parameters of an upstream process based on optical measurements generated during and / or after an etch process, according to an embodiment. [Diagram 5]

[0015] 1 is a flow chart for a method of performing a design of experiments (DoE) associated with a manufacturing process for forming a film on a substrate and a downstream etching process for etching the film, according to an embodiment. [Figure 6]

[0016] 1 is a flowchart for a method of training a model to determine a film quality value of a film and / or process parameter values ​​of one or more process parameters of an upstream process that formed the film based on an etch rate of the film measured during an etch process, according to an embodiment. [Figure 7]

[0017] 1 illustrates a diagrammatic representation of a machine, in the exemplary form of a computing device, within which a set of instructions for causing the machine to perform any one or more of the methodologies discussed herein may be executed. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010]

[0018] The embodiments described herein relate to a method of using an etch rate of a film determined from an etch process to determine one or more properties of the film and / or to determine process parameter values ​​of one or more process parameters of an upstream manufacturing process performed to form the film. Additionally, the embodiments relate to a manufacturing system performing the methods described herein. The embodiments further relate to a method of training a model to correlate an etch rate of a film to one or more properties of the film and / or to process parameter values ​​of one or more upstream manufacturing processes performed to form the film. The model can be, for example, a machine learning model or a regression model. Examples of models that can be trained include linear regression models, Gaussian regression models, and neural networks such as convolutional neural networks.

[0011]

[0019] Traditionally, a one-time DoE is performed to determine recipe set points for process parameters of a manufacturing process (e.g., a deposition process). Once recipe set points are set for a recipe, each process chamber that runs the recipe uses the determined process parameter set points, and it is assumed that the film quality determined during DoE is achieved for the manufacturing process. However, there is often variation between process chambers and / or the process parameters of the process chambers drift over time. Such variation and / or drift causes these process chambers to achieve process parameter values ​​that are different from the values ​​actually set in the process recipe. For example, a process recipe for a manufacturing process includes a target temperature of up to 200° C., but a first process chamber, when set to 200° C., may actually reach an actual temperature of 205° C. Additionally, a second process chamber, when set to 200° C., may actually reach an actual temperature of 196° C. Such deviations from the predetermined process parameter values ​​of the process recipe may cause one or more properties of a film deposited using the manufacturing process to change from the target properties. For example, increasing the temperature may increase the grain size of the film, and decreasing the temperature may decrease the grain size of the film, which may become a layer in the final device. Such changes in the film's properties may have deleterious effects on the final device.

[0012]

[0020] Measurement techniques such as X-ray diffraction (XRD), transmission electron microscopy (TEM), atomic force microscopy (AFM), etc., that can be used to measure film properties indicative of film quality, are destructive measurement techniques that destroy the substrate on which the measurements are performed. Furthermore, such measurement techniques are typically very time consuming. Thus, measurements that provide information about film quality are not performed on product-containing substrates (e.g., product wafers). Thus, as described above, it is generally assumed that films formed using process recipes have target film qualities (e.g., target film properties).

[0013]

[0021] One technique that may be used to address the problem of process chamber variability and drift is to periodically run the manufacturing process on a dummy or non-production substrate in the process chamber. The substrate with the film is then tested using destructive metrology techniques such as XRD, TEM, AFM, etc. to ensure that the film has the target film quality. However, while the manufacturing process is being performed by the process chamber on the dummy substrate, the process chamber cannot process product substrates. In addition, a delay occurs between processing the substrate using the manufacturing process and when the results of the metrology measurement are available. During this delay, the manufacturer may choose to keep the product substrate running on the process chamber (at the risk of forming an inferior film) or may choose to wait for the results of the metrology measurement (causing further delays in the manufacturing line). Either option increases manufacturing costs.

[0014]

[0022] The embodiments provide a system and method for monitoring the film quality of a film using an optical sensor in the etching process chamber. Using the optical sensor in the etching chamber, optical measurements of the film are taken during and / or after the etching process (e.g., in-line). The optical measurements are correlated to the film quality of the film and / or to process parameter values ​​of one or more process parameters of the upstream manufacturing process used to form the film. Measurements can be taken for each substrate being etched, providing measurement data at the substrate by substrate (e.g., wafer by wafer) level. The measurements are performed without damaging the product substrates and much faster than traditional measurements that have been used to evaluate the quality of the film. Furthermore, measurements of film quality can be performed in situ during etching without increasing the manufacturing time of the device or reducing the time that the process chamber is available to process product substrates. If a problem is found in the film of a substrate, the process chamber that performed the upstream manufacturing process can be adjusted and / or scheduled for maintenance with few or no other product substrates being processed under the same conditions that caused the problem. Thus, the etch chamber, in embodiments, becomes an in-line metrology tool to provide feedback control of the upstream film formation (eg, deposition) process.

[0015]

[0023] In an embodiment, the etch chamber can be used as an in-line metrology tool to provide feedback control of DRAM bitline deposition (e.g., ruthenium (Ru) bitline deposition). The Ru bitline resistance can depend on a tradeoff between grain size and average surface roughness. By process centring the Ru deposition conditions (e.g., crystalline and / or grain characteristics), the grain size can be monitored and controlled in embodiments to maintain minimal run-to-run variation in electrical resistance. The grain size can be correlated to the deposition temperature for Ru deposition and further correlated to the etch rate. These correlations can be made to generate a trained model that takes the etch rate as an input and outputs a film quality value (e.g., representative of grain size and / or electrical resistance) and / or the Ru deposition temperature. Thus, the measured Ru etch rate can be used to perform feedback control of grain size by adjusting the Ru deposition temperature.

[0016]

[0024] In one embodiment, the etch chamber is configured to perform an etch process on the substrate and includes an optical sensor that generates one or more optical measurements of a film on the substrate at least one of during or after the etch process. A computing device receives the one or more optical measurements of the film and determines, for each optical measurement of the one or more optical measurements, a film thickness of the film. The computing device uses the determined film thickness of each optical measurement of the one or more optical measurements to determine an etch rate of the film based on the one or more optical measurements. The computing device determines a film quality value of the film and / or a process parameter value of at least one process parameter of a previously performed process performed on the substrate based on the etch rate. The determined process parameter value of the at least one process parameter may be used to adjust the at least one process parameter for future substrates and / or to schedule maintenance of the process chamber.

[0017]

[0025] Turning now to the figures, FIG. 1A is a diagram of a cluster tool 100 (also referred to as a system or manufacturing system) configured for substrate fabrication, e.g., post poly plug fabrication, in accordance with at least some embodiments of the present disclosure. The cluster tool 100 includes one or more vacuum transfer chambers (VTMs) 101, 102, a factory interface 104, a number of processing chambers / modules 106, 108, 110, 112, 114, 116, 118, and a process controller 120 (controller). A server computing device 145 may also be connected to the cluster tool 100 (e.g., to the controller 120 of the cluster tool 100). In an embodiment having two or more VTMs, such as that shown in FIG. 1A, one or more pass-through chambers (referred to as vias) may be provided to facilitate vacuum transfer from one VTM to another. In an embodiment consistent with that shown in FIG. 1A, two pass-through chambers may be provided (e.g., pass-through chamber 140 and pass-through chamber 142).

[0018]

[0026] The factory interface 104 includes a loading port 122 configured to receive one or more substrates, for example from a Front Opening Unified Pod (FOUP) or other suitable substrate-containing box or carrier, to be processed using the cluster tool 100. The loading port 122 can include one or more loading areas 124a-124c, which can be used to load one or more substrates. Three loading areas are shown, although more or fewer loading areas can be used.

[0019]

[0027] The factory interface 104 includes an atmospheric transfer module (ATM) 126 that is used to transfer substrates loaded into the loading port 122. More specifically, the ATM 126 includes one or more robotic arms 128 (shown in dotted lines) that are configured to transfer substrates from the loading areas 124a-124c to the ATM 126 through doors 135 (shown in dotted lines, also referred to as slit valves) that connect the ATM 126 to the loading port 122. There is typically one door at each loading port (124a-124c) to allow substrate transfer from the respective loading port to the ATM 126. The robotic arms 128 are also configured to transfer substrates from the ATM 126 to the load locks 130a, 130b through doors 132 (shown in dotted lines, one for each load lock) that connect the ATM 126 to the air locks 130a, 130b. The number of load locks may be more or less than two, but for purposes of illustration only, two load locks (130a and 130b) are shown, each having a door for connection to the ATM 126. The load locks 130a-b may or may not be batch load locks.

[0020]

[0028] The load locks 130a, 130b can be maintained at either atmospheric or vacuum pressure environments under the control of the controller 120 and serve as intermediate or temporary holding spaces for substrates being transferred to / from the VTMs 101, 102. The VTM 101 includes a robot arm 138 (shown in dotted lines) configured to transfer substrates from the load locks 130a, 130b to one or more of the multiple processing chambers 106, 108 (also referred to as process chambers) or to one or more of the pass-through chambers 140, 142 (also referred to as vias) without breaking vacuum, i.e., while maintaining the vacuum pressure environment inside the VTM 102 and the multiple processing chambers 106, 108 and pass-through chambers 140, 142. The VTM 102 includes a robot arm 138 (dotted line) configured to transfer substrates from airlocks 130a, 130b to one or more of the multiple processing chambers 106, 108, 110, 112, 114, 116 and 118 without breaking vacuum, i.e., while maintaining the vacuum pressure environment inside the VTM 102 and the multiple processing chambers 106, 108, 110, 112, 114, 116 and 118.

[0021]

[0029] A door 134, e.g., a slit valve door, connects each of the load locks 130a, 130b to the VTM 101. Similarly, a door 136, e.g., a slit valve door, connects each processing module to the VTM (e.g., either VTM 101 or VTM 102) to which the respective processing module is coupled. The multiple processing chambers 106, 108, 110, 112, 114, 116, and 118 are configured to perform one or more processes. Examples of processes that may be performed by one or more of the processing chambers 106, 108, 110, 112, 114, 116, and 118 include a cleaning process (e.g., a pre-cleaning process to remove surface oxides from a substrate), an annealing process, a deposition process (e.g., for deposition of a cap layer, a hard mask layer, a barrier layer, a bitline metal layer, a barrier metal layer, etc.), an etching process, etc. Examples of deposition processes that may be performed by one or more of the process chambers include physical vapor deposition (PVD), chemical vapor deposition (CVD), atomic layer deposition (ALD), etc. Examples of etching processes that may be performed by one or more of the process chambers include plasma etch processes. In one exemplary embodiment, the process chambers 106, 108, 110, 112, 114, 116, and 118 are configured to perform processes typically associated with a post-poly plug fabrication sequence and / or a dynamic random access memory (DRAM) bit line stack fabrication sequence.

[0022]

[0030] The controller 120 (e.g., a tool and equipment controller) may control various aspects of the cluster tool 100, such as gas pressures in the processing chambers, individual gas flows, spatial flow ratios, plasma power in the various process chambers, temperatures of various chamber parts, radio frequency (RF) or electrical conditions of the processing chambers, etc. The controller 120 may receive signals from and send commands to any of the components of the cluster tool 100, such as the robot arms 128, 138, the process chambers 106, 108, 110, 112, 114, 116, and 118, the load locks 130a-b, the slit valve doors, one or more sensors, and / or other processing components of the cluster tool 100. In this manner, the controller 120 may control the starting and stopping of processing, adjust deposition rates, the type or mix of deposition compositions, etch rates, etc. The controller 120 may also receive and process measurement data (e.g., optical measurement data) from various sensors (e.g., optical sensors).

[0023]

[0031] In various embodiments, the controller 120 may be and / or include a computing device such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, etc. The controller 120 includes (or is) one or more processing devices, which may be a general-purpose processing device such as a microprocessor, a central processing unit, etc. More specifically, the processing device may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. The processing device 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 processor (DSP), a network processor, etc. The controller 120 may include a data storage device (e.g., one or more disk drives and / or solid state drives), a main memory, a static memory, a network interface, and / or other components. A processor of the controller 120 may execute instructions to perform any one or more of the methodologies and / or embodiments described herein. The instructions may be stored in a computer-readable storage medium, which may include a main memory, a static memory, a secondary memory, and / or a processor during execution of the instructions.

[0024]

[0032] In one embodiment, the controller 120 includes a feedback engine 121. The feedback engine may be implemented in hardware, firmware, software, or a combination thereof. The feedback engine 121 is optionally configured to receive and process optical measurement data, including results of reflectance measurements performed by an optical sensor such as a spectrometer. The feedback engine 121 may calculate the optical measurement data (e.g., reflectance measurement signals) during etching of the substrate to determine one or more thickness values ​​of the substrate. The feedback engine 121 may further determine an etch rate based on timestamps of when the optical measurements were made and the change in thickness between those optical measurements.

[0025]

[0033] In one embodiment, the feedback engine 121 includes a correlation model 123 that correlates the etch rate to a film quality value. The film quality value can be a value related to one or more properties of the film. Examples of properties that can be correlated to the etch rate are grain size (e.g., grain size in a Z-profile), grain orientation, crystal structure, thickness, and average surface roughness (Ra). During DoE, optimal film properties can be determined and a particular combination of film property values ​​can be assigned the highest film quality value. Other combinations of film quality values ​​can be assigned additional lower film quality values. Alternatively, a particular combination of film property values ​​that indicates a highest quality film can be assigned an intermediate film quality value. Other combinations of film properties that have poorer film quality can be assigned film quality values ​​above and below the intermediate film quality value. The correlation model 123 can additionally or alternatively correlate the etch rate and / or film quality value to process parameter values ​​of process parameters (process parameter values ​​of multiple process parameters) used during a preceding upstream manufacturing process used to form the film etched during the etch process.

[0026]

[0034] The feedback engine 121 may input the determined etch rate into the correlation model 123 and receive as an output a film quality value of the film being etched and / or one or more process parameter values ​​of one or more process parameters of a preceding upstream manufacturing process used to form the film under etch. The feedback engine 121 may compare the one or more determined process parameter values ​​to one or more target process parameter values ​​associated with a process recipe of the manufacturing process used to form the film. If a difference is identified between the one or more determined process parameter values ​​and the one or more target process parameter values, the feedback engine may generate a process parameter value offset for the process chamber that processed the substrate to form the film. The process chamber will then apply the process parameter offset to the target process parameter value of the process recipe when performing the manufacturing process of the process recipe on a subsequent substrate being processed. This improves the accuracy and / or precision of the properties of the film formed using the process recipe. Additionally or alternatively, the feedback engine 121 may schedule maintenance of the process chamber in response to determining that the difference exceeds a difference threshold.

[0027]

[0035] In one example, a first one of the process chambers 106, 108, 110, 112, 114, 116, and 118 may be a deposition chamber and a second one of the process chambers 106, 108, 110, 112, 114, 116, and 118 may be an etch chamber. The deposition chamber may execute a process recipe for depositing a film on a substrate. This may include using target process parameter values ​​for one or more process parameters, such as temperature, pressure, plasma power, etc., during the deposition process. The etch chamber may then process the substrate with the film after the film is deposited on the substrate by the deposition chamber. The etch chamber may etch the film and generate optical measurements of the film during the etch process and / or at the beginning and end of the etch process. The controller 120 may receive the optical measurements. The feedback engine 121 may calculate the etch rate at one or more depths / thicknesses of the film and / or calculate an overall etch rate of the film. The feedback engine 121 may determine that the etch rate is higher or lower than the target etch rate (which is the etch rate that correlates with the highest possible film quality value) and may determine a process parameter value for one or a combination of process parameters that correlates with the determined etch rate. The controller 120 may then adjust the deposition chamber settings (e.g., via generating one or more process parameter offsets for the deposition chamber). When the deposition chamber processes a future substrate using the process recipe to deposit a film on the future substrate, the determined adjustment or offset is applied to the one or more process parameter values ​​set in the process recipe. The resulting film then has film properties that more closely resemble the ideal film properties (e.g., the film has a film quality value that is higher or closer to the ideal film quality value).

[0028]

[0036] The controller 120 may be operatively connected to a server 145. The server 145 may be or may include a computing device that acts as a factory floor server that interfaces with some or all tools in a fabrication facility. The server 145 may send instructions to a controller of one or more cluster tools, such as the cluster tool 100. For example, the server 145 may receive signals from and send commands to the controller 120 of the cluster tool 100.

[0029]

[0037] In various embodiments, server 145 may be and / or include a computing device such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, etc. Server 145 may include (or be) one or more processing devices, which may be a general-purpose processing device such as a microprocessor, a central processing unit, etc. More specifically, the processing device may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. The processing device 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 processor (DSP), a network processor, etc. Server 145 may include data storage (e.g., one or more disk drives and / or solid state drives), a main memory, a static memory, a network interface, and / or other components. A processor of the server 145 may execute instructions to perform any one or more of the methodologies and / or embodiments described herein. The instructions may be stored in a computer-readable storage medium, which may include a main memory, a static memory, a secondary memory, and / or a processor during execution of the instructions.

[0030]

[0038] In some embodiments, server 145 includes feedback engine 121 and correlation model 123. Server 145 may include feedback engine 121 and correlation model 123 in addition to or instead of controller 120 including feedback engine 121 and correlation model 123. In some embodiments, controller 120 and / or server 145 correspond to computing device 700 of FIG.

[0031]

[0039] In some examples, one or more processes may be performed on a substrate in a first cluster tool (e.g., cluster tool 100) to form one or more films on the substrate, and one or more etching processes may be performed on the substrate in another cluster tool (e.g., optionally after performing a lithography process on the substrate). In such examples, optical measurements performed on the etching process in the process chamber of the second cluster tool may be used to adjust parameter values ​​for one or more process parameters used by the process chamber of the first cluster tool for future deposition processes. In such embodiments, server 145 may communicate with controllers of both cluster tools to adjust the etching feedback control of the process chamber of the first cluster tool based on the measurements performed in the second cluster tool.

[0032]

[0040] 1B is an illustration of a cluster tool 150 configured for substrate fabrication, such as post-poly plug fabrication, in accordance with at least some embodiments of the present disclosure. Cluster tool 150 includes a vacuum transfer chamber (VTM) 160, a factory interface 164, a number of processing chambers / modules 152, 154, 156, and a controller 170. A server computing device 145 may also be connected to cluster tool 150 (e.g., to controller 170 of cluster tool 150).

[0033]

[0041] The factory interface 164 includes one or more loading ports configured to receive one or more substrates, for example from front-opening unified pods (FOUPs) 166a, 166b or other suitable substrate-containing boxes or carriers, to be processed using the cluster tool 150.

[0034]

[0042] The factory interface 164 includes an atmospheric transfer module (ATM) that is used to transfer substrates loaded into the loading port. More specifically, the ATM includes one or more robotic arms configured to transfer substrates from a loading area to the ATM through which the ATM connects to the loading port. The robotic arms are also configured to transfer substrates from the ATM to the load locks 158a-b through doors that connect the ATM to the load locks 158a-b. The load locks 158a-b can be maintained at either an atmospheric or vacuum pressure environment under the control of the controller 170 and serve as intermediate or temporary holding spaces for substrates being transferred to / from the VTM 160. The VTM 160 includes a robotic arm 162 configured to transfer substrates from the load locks 158a-b to one or more of the multiple processing chambers 152, 154, 156 without breaking vacuum, i.e., while maintaining a vacuum pressure environment inside the VTM 160 and the multiple processing chambers 152, 154, 156.

[0035]

[0043] The controller 170 (e.g., a tool and equipment controller) may control various aspects of the cluster tool 150, such as gas pressures in the processing chambers, individual gas flows, spatial flow ratios, temperatures of various chamber parts, radio frequency (RF) or electrical conditions of the processing chambers, etc. The controller 170 may receive signals from and send commands to any of the components of the cluster tool 150, such as the robot arm 162, the process chambers 152, 154, 156, the load locks 158a-b, the slit valve doors, one or more sensors, and / or other processing components of the cluster tool 150. In this manner, the controller 170 may control the starting and stopping of processing, adjust deposition rates, the type or mix of deposition compositions, etch rates, etc. The controller 170 may further receive and process measurement data (e.g., optical measurement data) from various sensors (e.g., optical sensors in an etching process chamber, which may correspond to the process chamber 152 and / or the process chamber 154). Controller 170 may be substantially similar to controller 120 of FIG. 1A and may include a feedback engine 121 (which may include, for example, correlation model 123).

[0036]

[0044] The controller 170 is operatively connected to the server 145, which may also be operatively connected to the controller 120 of FIG. 1A.

[0037]

[0045] In one example, one or more processes are performed on the substrate by the various process chambers 106, 116, 118, 114, 110, 112, 108 of the cluster tool 100 to form one or more films on the substrate. The substrate may then be removed from the cluster tool 100 and placed in a lithography tool to pattern a mask layer on the substrate. The substrate may then be placed in the cluster tool 150. One or more etch processes are then performed on the substrate by one or more of the process chambers 152, 154, 156 of the cluster tool 150 to etch the one or more films. The controller 170 and / or the feedback engine 121 of the server 145 may receive the optical metrology data before, during, and / or after the one or more etch processes. The correlation model 123 may then be used to determine a film quality value for the film and / or to determine a process parameter value for a process parameter (or process parameter values ​​for multiple process parameters) of a manufacturing process performed by one of the process chambers of the cluster tool 100 to form the film. The feedback engine 121 may determine an offset or adjustment to apply to the process chamber that performed the previous manufacturing process. The server 145 may send the offset or adjustment to the controller 120. The controller 120 may store the offset or adjustment for the process chamber and cause the process chamber to use the offset or adjustment for future runs of the process used to form the film.

[0038]

[0046] In one embodiment, the process chambers of cluster tool 100 are configured to perform one or more DRAM bit line stack processes (e.g., for post-poly plug fabrication). Alternatively, cluster tool 100 may be configured to perform other processes. Additionally, in one embodiment, the process chambers of cluster tool 150 are configured to perform one or more etching processes to etch one or more films of the DRAM bit line stacks formed in the process chambers of cluster tool 100. In one embodiment, prior to performing one or more etching processes, a lithography process is performed to pattern a hard mask layer deposited over the DRAM bit line stacks.

[0039]

[0047] 2 shows a schematic side view of a portion of a substrate 200 including a poly plug 202, a DRAM bitline stack 201 (including a barrier metal 204, a barrier layer 206, and a bitline metal layer 208), and a hardmask layer 210. The poly plug 202 may have been formed outside of the cluster tool 100. The DRAM bitline stack 201 may have been formed inside of the cluster tool 100 without breaking vacuum between the deposition of the various layers of the DRAM bitline stack 201. Selective portions of the hardmask layer 210 may have been hardened using a lithography process by a lithography tool.

[0040]

[0048] In one example, the processing chamber 106 may have been used to perform one or more pre-cleaning processes to remove contaminants that may be present on the substrate 200, such as native oxidation that may be present on the substrate 200. The processing chamber 108 may have performed a barrier metal deposition process on the substrate 200 (e.g., to deposit a barrier metal 204 on the cleaned substrate 200 and the poly plug 202). The barrier metal may be, for example, one of titanium (Ti), tantalum (Ta). The processing chamber 110 may have performed a barrier layer deposition process on the substrate 200 (e.g., to deposit a barrier layer 206 on the barrier metal 204). The barrier layer 206 may be, for example, one of titanium nitride (TiN), tantalum nitride (TaN), or tungsten nitride (WN). The processing chamber 112 may have performed a bitline metal deposition process on the substrate 200 (e.g., to deposit a bitline metal layer 208 on the barrier layer 206). The bitline metal may be, for example, one of tungsten (W), molybdenum (Mo), ruthenium (Ru), iridium (Ir), or rhodium (Rh). The processing chamber 114 may have performed a hardmask deposition process on the substrate 200 (e.g., to deposit a hardmask layer 210 on the bitline metal layer 208). The hardmask may be, for example, one of silicon nitride (SiN), silicon oxide (SiO), and silicon carbide (SiC). In some embodiments, an annealing process may be performed on the substrate 200 after deposition of the barrier layer 206 and before depositing the bitline metal layer 208 on the barrier layer 206. The annealing process may be any suitable annealing process, such as a rapid thermal processing (RTP) anneal. Alternatively or in combination, an annealing process may have been performed on the substrate 200 after deposition of the bitline metal layer 208 and before depositing the hardmask layer 210 on the bitline metal layer 208. The annealing process may be performed on a substrate 200 having a bitline metal layer 208 deposited thereon, as described above.In some embodiments where an annealing process is performed, a separate processing chamber may have deposited an optional capping layer 209 deposited on the bitline metal layer 208 prior to the formation of the hardmask layer 210. In some embodiments, the bitline metal is a metal such as ruthenium (Ru), which is a grain growth material.

[0041]

[0049] In one embodiment, the cluster tool 100 advantageously allows a user to perform multiple DRAM bitline processes on poly plugs using a single machine (e.g., a single cluster tool 100) that is configured to maintain a vacuum pressure environment throughout the DRAM bitline process. Thus, the possibility of oxidation occurring on the substrate during fabrication of the post substrate 200 is reduced, if not eliminated. Additionally, in an embodiment, because a vacuum pressure environment is maintained throughout the DRAM bitline formation process, the selection of bitline metal material is not limited by the grain growth characteristics of the metal.

[0042]

[0050] After the DRAM bitline stack 201 and hardmask layer 210 are formed, the cluster tool 150 may perform one or more etch processes to etch one or more layers of the DRAM bitline stack 201. During one or more of the etch processes, optical measurements may be generated to determine the instantaneous thickness of the layer or layers being etched. For example, during the etching of the bitline metal layer 208, optical measurements may be generated at times T0, T1, T2 through T N Optical measurements may be made at time T0, Z0 thickness may be measured, at time T1, Z1 thickness may be measured, and at time T2, Z2 thickness may be measured, and repeated until the etch process is complete.

[0043]

[0051] Once two or more thickness measurements are generated, the times at which the thickness measurements were made and the measured thicknesses may be used to calculate an etch rate for a portion of the film between the two thicknesses. A thickness delta between the thickness values ​​may be determined, and a time delta between the time values ​​may be determined. The etch rate may then be the thickness delta divided by the time delta. For example, a first etch rate for a portion of the bitline metal layer 208 between Z0 and Z1 may be calculated, a second etch rate for a portion of the bitline metal layer 208 between Z1 and Z2 may be calculated, and so on. Each of the instantaneous etch rates associated with a region of the bitline metal layer 208 may be input into a correlation model of the feedback engine to determine a film quality value associated with that region of the bitline metal layer 208, as well as one or more process parameter values ​​for one or more process parameters used during the formation of the bitline metal layer 208. For example, the feedback engine may determine that the temperature of the substrate 200 during the beginning of deposition of the bitline metal layer 208 was too high, and the temperature of the substrate near the end of deposition of the bitline metal layer 208 was too low. This information can be used to automatically determine one or more adjustments to make to the process recipe for future process runs. For example, a first temperature offset to lower the temperature below the target temperature specified in the process recipe for the start of deposition of the bitline metal layer and a second temperature offset to raise the temperature above the target temperature specified in the process recipe for the end of deposition can be generated and stored for later application.

[0044]

[0052] In addition to or instead of using the optical measurements and feedback engine to detect film quality and / or process parameter values ​​of process parameters used during the formation of the bitline metal layer 208, the optical measurements and feedback engine may be used to detect film quality and / or process parameter values ​​of process parameters used during the formation of other types of layers. Examples of other types of layers may include barrier layers, barrier metals, etc. In one embodiment, the feedback engine may determine film quality for any film whose film quality correlates to an etch rate. Similarly, in one embodiment, the feedback engine may determine process parameter values ​​for process parameters used during a deposition process whose process parameter values ​​correlate to an etch rate. A bitline metal layer (e.g., a Ru layer, etc.) is one example of a film whose film quality and process parameter values ​​used during deposition correlate to an etch rate.

[0045]

[0053] 3 shows a simplified side view of a system 300 for monitoring an etch processing chamber 303 according to one embodiment of the present disclosure. The system 300 may include, for example, an etch processing chamber 303. The processing chamber 303 may include other components including walls, a lid, a showerhead, a nozzle, a door, and / or a window 320. The window 320 may be, for example, a transparent crystal. The transparent crystal may be made of a transparent ceramic material or may be made of a durable transparent material such as sapphire, diamond, quartz, silicon carbide, or a combination thereof.

[0046]

[0054] In an embodiment, system 300 further includes a light source 301 (e.g., a broadband light source or other source of electromagnetic radiation), an optical combiner 304 (e.g., a collimator or mirror), a spectrometer 325, a controller 170, and optionally a server 145. The light source 301 and the spectrometer 325 may be optically coupled to the optical combiner 304 through one or more fiber optic cables 332.

[0047]

[0055] In various embodiments, the optical coupler 304 may be adapted to collimate or otherwise transmit light in two directions along the optical path. The first direction may include light from the light source 301 that is collimated and transmitted through the window 320 into the process chamber 303. The second direction may be reflected light that is reflected from the substrate 304, passes through the window 320 again, and returns into the optical coupler 304. The reflected light may be collected into the fiber optic cable 332 and thus directed to the spectrometer 325 in the second direction along the optical path. Additionally, the fiber optic cable 332 may be coupled between the spectrometer 325 and the light source 301 for efficient transmission of light from the light source 301, to the transparent crystal 120, and back to the spectrometer 325.

[0048]

[0056] In an embodiment, the light source emits light in a spectrum of approximately 200-800 nm, and the spectrometer 325 also has a wavelength range of 200-800 nm. The spectrometer 325 may be adapted to detect the spectrum of reflected light received from the optical coupler 304, for example, light that reflects off a substrate in the chamber 303, returns through the window 320, and is collected by the optical coupler 304 into the fiber optic cable 332.

[0049]

[0057] The controller 170 may be coupled to both the light source 301 , the spectrometer 325 , and the process chamber 303 .

[0050]

[0058] In one embodiment, the controller 170 may instruct the light source 301 to receive a light spectrum from the spectrometer 325 after it has been turned on. The controller 170 may also leave the light source off and receive a second spectrum from the spectrometer 325 when the light source 301 is off. The processing device 130 may subtract the second spectrum from the first spectrum to determine an instantaneous reflectometry signal. The controller 170 may then mathematically fit the reflectometry signal to one or more thin film models to determine one or more optical thin film properties of the thin film being measured.

[0051]

[0059] In some embodiments, the one or more optical thin film properties include a thickness of the film, a refractive index (n), and / or an extinction coefficient (k) value. The refractive index is the ratio of the speed of light in a vacuum to the speed of light in the film. The extinction coefficient is a measure of how much light is absorbed in the film. The controller 170 may use the n and k values ​​to determine the composition of the film. The controller 170 may be further configured to analyze the data of the one or more properties of the film. The controller 170 may then use a feedback engine to determine a film quality value and / or a process parameter value, as described herein above. Alternatively, the server 145 may use a feedback engine to determine a film quality value and / or a process parameter value, as described herein above.

[0052]

[0060] FIG. 4A is a flow chart for a method 400 of performing feedback control of an upstream process based on optical measurements generated during and / or after an etching process, according to an embodiment. Method 400 may be performed using components described with reference to FIGS. 1A-3, as will become apparent. For example, method 400 may be performed by controller 120, controller 170, and / or server 145, in an embodiment. At least some steps of method 400 may be performed by processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed on a processing device to perform a hardware simulation), or a combination thereof. Although shown in a particular sequence or order, unless otherwise specified, the order of operations may be changed. Thus, the illustrated embodiment should be understood as an example only, and the illustrated processes may be performed in a different order, and some processes may be performed in parallel. Additionally, in various embodiments, one or more processes may be omitted. Thus, not all processes are performed in all embodiments. Other process flows are possible.

[0053]

[0061] In step 410 of method 400, a processing chamber performs a fabrication process on a substrate to form a film on the substrate. The processing chamber may be controlled by a controller (e.g., controller 120 of FIG. 1A), which may cause the processing chamber to perform the fabrication process. The substrate may or may not have one or more films already formed thereon. Thus, in some embodiments, the film formed in step 410 is formed over one or more other films on the substrate.

[0054]

[0062] In some embodiments, the processing chamber is a deposition chamber and the manufacturing process is a deposition process, such as a CVD process, a PVD process, an ALD process, etc. The manufacturing process may alternatively be an epitaxial growth process, an oxide growth (i.e., oxidation) process, or other type of process for forming a film. The manufacturing process is performed according to a process recipe using target process parameter values ​​for one or more process parameters. The process recipe may include different target process parameter values ​​at different time offsets in the manufacturing process. For example, the process recipe may include a first temperature, a first pressure, and a first plasma power at the beginning of the process, a second temperature, a second pressure, and / or a second plasma power at the middle of the process, and a third temperature, a third pressure, and / or a third plasma power near the end of the process. Some target process parameter values ​​do not change during the manufacturing process, while other target process parameter values ​​may change periodically throughout the manufacturing process. In some cases, some or all of the process parameter values ​​are constant throughout the manufacturing process.

[0055]

[0063] The manufacturing process may have been designed based on DoE that was performed to determine optimal process parameter values ​​and achieve a film having target properties. The DoE may have tested multiple different process parameter values ​​for each of the process parameters in the process recipe and, based on the test results, generated a process recipe with a particular combination of process parameter values ​​for the process parameters in the recipe.

[0056]

[0064] After the method 410 is performed, the substrate may be transferred from the processing chamber (e.g., by a transfer chamber robot). The substrate may be sequentially transferred into one or more additional process chambers (e.g., by a transfer chamber robot). The additional process chambers may be connected to the same transfer chamber or cluster tool as the processing chamber that performed step 410. Alternatively, or in addition, the one or more additional process chambers may not be connected to the transfer chamber and / or cluster tool as the process chamber that performed step 410. In some cases, one or more additional manufacturing processes (e.g., one or more deposition processes) are performed on the substrate. For example, one or more deposition processes may be performed on the substrate to form one or more additional films (e.g., mask layers, etc.) on the substrate. Thus, a film stack including multiple layers of films may be formed. One example of such a film stack is the bit line stack of a DRAM shown in FIG. 2. However, in other embodiments, different film stacks may be formed, or a single film may be formed.

[0057]

[0065] In some embodiments, the substrate may be transferred to a lithography tool, which may expose the substrate to a pattern. The substrate may include a mask layer (e.g., a hard mask layer) over the film formed in step 410. The lithography tool may transfer the pattern to the mask layer by hardening the photoresist layer (e.g., by exposing portions of the photoresist layer to ultraviolet or other light).

[0058]

[0066] In step 415, the substrate is transferred to an etching chamber. The etching chamber may be connected to the same transfer chamber and / or cluster tool as the process chamber that performed step 410. Alternatively, the etching chamber may not be connected to the transfer chamber and / or cluster tool to which the process chamber that performed step 410 is connected. For example, the etching chamber may be connected to a different cluster tool and / or a different transfer chamber from the process chamber that performed step 410. In step 415, the etching chamber performs an etching process to etch the film on the substrate. The etching process may additionally etch one or more additional films and / or layers on the substrate, such as a mask layer on the film (and optionally on one or more other films). The etching chamber may be controlled by a controller (e.g., controller 170 of FIG. 1B), which may cause the etching chamber to perform the etching process.

[0059]

[0067] In step 420, one or more optical sensors in the etch chamber generate one or more optical measurements of the film on the substrate. The optical measurements may be, for example, spectroscopic and / or reflectometric measurements and may provide information usable to determine the depth or thickness of the film. The optical measurements may be generated, for example, using the optical sensors shown in FIG. 3 in some embodiments. The optical measurements may be generated periodically before, during, and / or after the etch process. For example, optical measurements may be generated at the beginning of the etch process to determine the initial thickness of the film, and then periodically (e.g., every 5 seconds, 10 seconds, 15 seconds, or other intervals) until the etch process is complete. When the etch process is complete, the film may have a target final thickness. This target final thickness may be 0 nm thick in some cases (e.g., the film may be completely etched away in one or more areas of the substrate).

[0060]

[0068] In step 425, processing logic may process the optical measurements (e.g., using reflectometric and / or spectroscopic measurements) to determine the depth or thickness of the film. For each optical measurement, a different film thickness may be determined.

[0061]

[0069] In step 430, processing logic may determine one or more etch rates for the film (etch rates associated with particular depths into the film). In one embodiment, an etch rate is determined for the entire etch process based on a starting thickness, an ending thickness, a starting time, and an ending time. In one embodiment, one or more instantaneous etch rates are determined for the film. Each instantaneous etch rate may be determined using two or more thickness measurements and the associated times at which the two or more thickness measurements were made. Thus, different etch rates may be determined for different regions or depths of the film.

[0062]

[0070] In step 435, processing logic determines a process parameter value for at least one process parameter for the manufacturing process used to form the film based on the at least one determined etch rate. In one embodiment, processing logic determines a process parameter value for a single process parameter that most closely correlates to the quality of the film. For example, the process parameter value may be a temperature value. In one embodiment, processing logic determines a combination of process parameter values ​​for two or more process parameters that correlate to the film quality. For example, the process parameter value may include a temperature value, a pressure value, and / or a plasma power value. The one or more process parameter values ​​may be determined by inputting the determined etch rate into a trained model that has been trained to receive the etch rate as an input and output one or more process parameter values. The model may be, for example, a trained regression model or a trained neural network. Each of the determined etch rates may be input into the model to determine one or more process parameter values ​​for the manufacturing process performed to form the film. For example, the substrate / film temperature during deposition of the film can be determined when the film was 10 nm thick, when the film was 20 nm thick, when the film was 30 nm thick, etc.

[0063]

[0071] In step 440, processing logic compares the determined process parameter value or values ​​to one or more target process parameter values ​​for at least one process parameter. Based on the comparison, processing logic may determine whether the determined process parameter value deviates from the process parameter value and may determine a delta or amount of any difference between the determined process parameter value and the target process parameter value. The target process parameter value may be a process parameter set point of a process recipe used to form the film. For example, the process recipe may have a target temperature, a target pressure, and / or a target plasma power. In addition, the target temperature, the target pressure, and / or the target plasma power (and / or other process parameter values) may vary at different points in the recipe (e.g., at different time offsets in the recipe). In many cases, the process parameter values ​​actually achieved by a process chamber during execution of a process recipe do not exactly correspond to the process parameter set points of the process recipe. However, heretofore, there has been no commercially viable method for determining whether this is the case. The embodiments provide techniques for rapidly determining whether process parameter values ​​for a film formation process (e.g., a deposition process) deviate from the process recipe values ​​(and how far the determined process parameter values ​​deviate from the target process parameter values) based on the etch rate measured in a downstream etch process. The determined process parameter values ​​may represent true process parameter values ​​achieved during the film formation process, as opposed to process parameter values ​​that should have been achieved (as described in the process recipe). Thus, in step 440, the processing logic may determine, for example, whether the temperature was too high or too low, the pressure was too high or too low, and / or the plasma power was too high or too low.

[0064]

[0072] In step 445, processing logic determines whether the determined process parameter value differs from the target process parameter value. If the target process parameter value and the determined process parameter value do not differ, the method continues to step 455 where processing logic determines that no adjustments to the process parameters for the manufacturing process as performed by the process chamber are made. If the target process parameter value and the determined process parameter value differ, the method continues to step 450.

[0065]

[0073] In step 450, processing logic determines whether the determined difference between the one or more determined process parameter values ​​and the one or more target process parameter values ​​meets or exceeds a difference threshold. If the difference meets or exceeds the difference threshold, the method continues to step 460. If the difference is less than the difference threshold, the method proceeds to step 455, where processing logic determines that no adjustments will be made to the process parameters for the manufacturing process as performed by the process chamber and / or that no maintenance is scheduled for the process chamber.

[0066]

[0074] At step 460, processing logic may determine an adjustment to at least one process parameter associated with the fabrication process and a process chamber performing the fabrication process to form a film on the substrate that has been etched and the etch rate determined. At step 465, processing logic may adjust at least one process parameter for the process chamber / process recipe combination (e.g., for the process chamber when performing the process recipe) according to the determined adjustment.

[0067]

[0075] The adjustment may be in the form of an offset that may be associated with the process recipe and the process chamber. The target process parameter value of the process recipe may not be changed in an embodiment. Rather, in one embodiment, the processing logic generates a process parameter value offset. The process parameter value offset may be stored and applied to the process recipe when the process recipe is executed by the process chamber. The process parameter offset may be a correction offset equal to the delta or difference determined in step 440. For example, if the target temperature was 600° C. and the determined temperature was 612° C., the determined offset may be −12° C. Thus, the next time the process chamber executes the process recipe to form a film on a new substrate, it will use a temperature of 600−12=588° C. From this, the actual temperature will be 600° C. This is because the process chamber was shown to operate at a high temperature and output a temperature of 612° C. when set to 600° C.

[0068]

[0076] It should be noted that multiple process parameter offsets may be generated for a single process recipe / process chamber combination. For example, a temperature offset of +2° C. may be generated between seconds 1-20 of the process recipe, a temperature offset of +1° C. may be generated between seconds 21-100 of the process recipe, and no temperature offset may be generated during the remainder of the process recipe. This combination of offsets may be generated, for example, for a process chamber that heats up slower than anticipated during the process recipe design. In another example, a temperature offset of +5° C., a pressure offset of −1 mTorr, and a plasma power offset of +10 mWatts may be determined.

[0069]

[0077] In some embodiments, if the difference exceeds a difference threshold in step 450, maintenance is scheduled for the process chamber.

[0070]

[0078] FIG. 4B is a flow chart for a method 470 of determining process parameter values ​​for one or more process parameters of an upstream process based on optical measurements generated during and / or after an etching process, according to an embodiment. Method 470 may be performed using components described with reference to FIGS. 1A-3, as will become apparent. For example, method 400 may be performed by controller 120, controller 170, and / or server 145, in an embodiment. In one embodiment, method 470 is performed at step 435 of method 400. Although shown in a particular sequence or order, unless otherwise specified, the order of operations can be changed. Thus, the illustrated embodiment should be understood as an example only, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, in various embodiments, one or more processes can be omitted. Thus, not all processes are performed in all embodiments. Other process flows are possible.

[0071]

[0079] At step 472 of method 470, processing logic receives an optical measurement of the film. The optical measurement may have been generated during the etching process at a first time (e.g., time 0). At step 474, processing logic determines a film thickness of the film at the first time based on the optical measurement (e.g., using reflectometry). At step 476, processing logic generates an additional optical measurement of the film on the substrate during the etching process at a later time (e.g., a second time, such as time 10 seconds). At step 478, processing logic determines an additional film thickness of the film at the later time based on the additional optical measurement.

[0072]

[0080] In step 480, processing logic determines an etch rate of the film based on the thicknesses determined at the two points in time. For example, processing logic may determine a change in thickness by subtracting the additional film thickness from the initial film thickness, and may determine a time difference by subtracting the later time from the first time. Processing logic may divide the change in thickness by the time difference to determine an instantaneous etch rate of the film between the initial thickness and the additional thickness.

[0073]

[0081] In step 482, processing logic determines a film quality value for the film at that thickness / depth based on the determined etch rate. In one embodiment, processing logic inputs the etch rate into a trained model, which outputs a film quality value. The film quality value can be a value that correlates with one or more film properties related to the success and / or failure of an end product. Some examples of film properties that correlate with film quality values ​​are grain size (e.g., grain size in Z-direction or vertical profile), grain orientation, and average surface roughness. The film quality value can be used to estimate the quality of an end product that will include the film as one layer of the end product (e.g., a DRAM memory unit).

[0074]

[0082] In step 484, processing logic determines a process parameter value for at least one process parameter for the fabrication process performed to form the film based on the film quality value and / or the etch rate. In one embodiment, the film quality value is input to another model that outputs one or more process parameter values. In one embodiment, the trained model into which the etch rate is input outputs both the film quality value and the one or more process parameter values. The one or more process parameter values ​​may be correlated to both the film quality value and the etch rate. The determined film quality value and the one or more determined process parameter values ​​are associated with a particular region of the film or a film thickness range between the film thickness determined in step 474 and the film thickness determined in step 478.

[0075]

[0083] In step 486, the processing logic determines whether the etching process is complete. If the etching process is not complete, the method returns to block 476 where an additional optical measurement is generated. This additional optical measurement is used to determine another thickness, which may be used with the last determined thickness to determine a new etch rate for the new thickness range of the film. A new film quality value and / or one or more new process parameter values ​​may then be determined for the new thickness range. This process is repeated, and new film quality values / process parameter values ​​may be determined for each thickness range or region of the film until the etching process is complete. Depending on the film properties, the etch rate may change as more of the film is etched (e.g., using a directional or anisotropic etch, or using an isotropic etch) and as deeper regions of the film are exposed. For example, the grain size may be large at one depth / thickness of the film and small at another depth / thickness of the film. Increasing the temperature during the deposition process may result in larger grain sizes, less dense, and etching may proceed more quickly. Also, decreasing the temperature during the deposition process may result in smaller grain sizes and higher density, which may cause etching to proceed less quickly. Thus, when measurements are performed at different depths of the film, the film may have different properties and different instantaneous etch rates may be determined. This may correspond to different film quality values ​​and / or process parameter values ​​(e.g., temperature).

[0076]

[0084] In step 488, processing logic optionally determines an overall film quality value for the film. The overall film quality value may be, for example, an average or median of the film quality values ​​determined at different thickness ranges of the film. The overall film quality value may be used to determine the probability that the final product will fail the test.

[0077]

[0085] FIG. 5 is a flow chart for a method 500 of performing a design of experiments (DoE) associated with a manufacturing process for forming a film on a substrate and a downstream etching process for etching the film, according to an embodiment. Although shown in a particular sequence or order, the order of operations can be changed unless otherwise specified. Thus, the illustrated embodiment should be understood as an example only, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, in various embodiments, one or more processes can be omitted. Thus, not all processes are performed in all embodiments. Other process flows are possible.

[0078]

[0086] In step 505 of method 500, multiple versions of a manufacturing process (e.g., a deposition process) are performed. Each version of the manufacturing process uses a different combination of process parameter values, resulting in a film with different film properties. In one embodiment, each version of the manufacturing process uses a different temperature, while other process parameters remain fixed for the different versions of the manufacturing process. In one embodiment, the different versions of the manufacturing process have different settings for temperature, pressure, and / or plasma power (e.g., of the plasma used during deposition, such as plasma PVD or plasma CVD). In some implementations, an optimal value for a process parameter may be known a priori, and that optimal value, as well as one or more additional values ​​of the process parameter above and below the optimal value, may be tested. For example, for a manufacturing process with a known optimal temperature of 450° C., versions of the manufacturing process using the following temperatures may be tested: 435° C., 438° C., 441° C., 444° C., 447° C., 450° C., 453° C., 456° C., 459° C., 462° C., and 465° C. In one example, about 220 substrates are processed at a range of temperatures. For example, for a manufacturing process with a known optimum temperature of 450° C., 20 wafers each may be processed at each of the temperatures listed above.

[0079]

[0087] In step 510, one of the versions of the manufacturing process is selected. In step 515, one or more destructive metrology measurements are performed on representative substrates manufactured using the selected version of the manufacturing process to determine one or more film properties of the films on the representative substrates. Examples of destructive metrology measurements that may be performed include electron backscatter diffraction (EBSD) and XRD. The destructive measurements may be performed to determine crystalline properties and / or grain properties, such as grain size, grain orientation, and / or average surface roughness (Ra). In one embodiment, EBSD and / or XRD measurements are performed to determine the grain size profile in the Z direction (vertical direction) of the film.

[0080]

[0088] In step 520, a film quality rating value is determined for the film based on the destructive metrology measurements. In one embodiment, optimal crystal and / or grain characteristics may be known. Films with optimal crystal and / or grain characteristics may be assigned a particular film quality value (e.g., a film quality value of 0 or a film quality value of 100). Films may be assigned a film quality value based on the amount of deviation between the film's optimal crystal and / or grain characteristics and the measured crystal and / or grain characteristics. In one embodiment, films with optimal crystal and / or grain characteristics have the highest film quality value. For example, if the film's grain size is larger or smaller than the optimal grain size, the film may be assigned a value less than the maximum value based on the amount of difference between the optimal grain size and the film's grain size. In one embodiment, films with optimal crystal and / or grain characteristics are associated with intermediate film quality values. For example, 0 may be the optimal film quality value. In the example of Z-direction grain size, films with grain sizes larger than the optimal grain size may be assigned a positive film quality value. Similarly, films having grain sizes smaller than the optimum can be assigned a negative film quality value, where the larger the value, the greater the deviation from the optimum.

[0081]

[0089] In step 525, an etch process is performed on a representative substrate manufactured using the selected version of the manufacturing process. In step 530, an etch rate is determined for the etch process (e.g., based on optical measurements from an optical sensor, where optical measurements are taken before, during and / or after the etch process). In one embodiment, the etch rate is determined as described above. In one embodiment, the etch rate is calculated according to the following formula: ER(n)=(Z n-1 -Z n ) * frequency where ER is the etching rate and Z n is the specific thickness measurement, and Z n-1 is the previous thickness measurement and frequency is the frequency at which the thickness measurement is made.

[0082]

[0090] Data items are generated for the selected version of the fabrication process in step 535. The data items can be training data items including process parameter values ​​or values ​​of the fabrication process (e.g., temperatures used in the fabrication process), determined film quality ratings, and determined etch rates.

[0083]

[0091] At step 540, a determination is made as to whether there are any remaining versions of the manufacturing process that have not yet been tested (and for which data items have not yet been generated). If untested versions of the manufacturing process remain, the method returns to step 510 and a new version of the manufacturing process is selected and tested. If all of the versions of the manufacturing process have been tested, the method continues to step 545.

[0084]

[0092] In step 545, a training data set is generated. The training data set includes data items generated for each of the versions of the manufacturing process. In some cases, a film quality assessment for each version of the manufacturing process (and associated each version of the film) after destructive metrology has been performed on all versions of the film. This allows for a comparison of differences between film properties for each version of the film before assigning a film quality value.

[0085]

[0093] FIG. 6 is a flow chart for a method 600 of training a model to determine a film quality value of a film and / or a process parameter value of one or more process parameters of an upstream process that formed the film based on an etch rate of the film measured during an etch process, according to an embodiment. Method 600 may be performed using components described with reference to FIGS. 1A-3, as will become apparent. For example, method 600 may be performed by controller 120, controller 170, and / or server 145, in an embodiment. At least some steps of method 400 may be performed by processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed on a processing device to perform a hardware simulation), or a combination thereof. Although shown in a particular sequence or order, unless otherwise specified, the order of operations may be changed. Thus, the illustrated embodiment should be understood as an example only, and the illustrated processes may be performed in a different order, and some processes may be performed in parallel. Additionally, in various embodiments, one or more processes may be omitted. Thus, not all processes are performed in all embodiments. Other process flows are possible.

[0086]

[0094] At step 605 of method 600, processing logic receives a training data set (e.g., as may have been generated according to method 500). The training data set includes a number of data items, each data item including one or more process parameter values, film quality ratings, and etch rates for a version of a manufacturing process.

[0087]

[0095] In step 610, processing logic trains the model to receive the etch rate as input and output at least one of a predicted film quality value or a predicted process parameter value of the process parameter (or a combination of predicted process parameter values ​​of multiple process parameters).

[0088]

[0096] In one embodiment, the model is a machine learning model, such as a regression model trained using regression. Examples of regression models are regression models trained using linear regression or Gaussian regression. In one embodiment, in step 615, the processing logic performs linear regression or Gaussian regression using the training data set to train the model. A regression model predicts a value of Y given known values ​​of X variables. The regression model may be trained using regression analysis, which may include interpolation and / or extrapolation. In one embodiment, the parameters of the regression model are estimated using least squares. Alternatively, Bayesian linear regression, percentage regression, leas absolute deviation, non-parametric regression, scenario optimization and / or distance metric learning may be performed to train the regression model.

[0089]

[0097] In one embodiment, the model is a machine learning model, such as an artificial neural network (also referred to simply as a neural network). The artificial neural network may be, for example, a Convolutional Neural Network (CNN). In one embodiment, at step 620, processing logic performs supervised machine learning to train the neural network.

[0090]

[0098] Artificial neural networks generally include a feature representation component with a classifier or recurrent layer that maps features to a target output space. For example, a convolutional neural network (CNN) hosts multiple layers of convolution filters. Lower layers may perform pooling and address nonlinearities. On top of the lower layers, a multi-layer perceptron is typically added to map the features of the upper layers extracted by the convolutional layers to a decision (e.g., a classification output). Neural networks can be deep networks with multiple hidden layers, or shallow networks with zero or a few (e.g., 1-2) hidden layers. Deep learning is a class of machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Neural networks can learn in a supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) manner. Some neural networks (e.g., deep neural networks) include a hierarchy of layers, with different layers learning different levels of representations corresponding to different levels of abstraction. In deep learning, each level learns to transform its input data into a more abstract and complex representation.

[0091]

[0099] Training a neural network can be accomplished in a supervised learning manner that involves feeding a training dataset of labeled inputs through the network, observing its outputs, defining an error (by measuring the difference between the output and the labeled values), and using techniques such as deep gradient descent and backpropagation to adjust the weights of the network across all its layers and nodes such that the error is minimized. In many applications, repeating this process across many labeled inputs in the training dataset results in a network that can generate the correct output when presented with inputs that differ from those contained in the training dataset. In high-dimensional settings, such as large images, this generalization is achieved once a sufficiently large and diverse training dataset is available.

[0092]

[0100] In an embodiment, the input is a feature vector including the etch rate, and the labels are one or more process parameter values ​​and / or film quality values. In one embodiment, a neural network is trained to receive the etch rate as an input and output both a film quality value and a process parameter value (or a set of two or more process parameter values). In one embodiment, a neural network is trained to receive the etch rate as an input and output a film quality value. The film quality value may then be input to another trained model (e.g., another trained machine learning model), which may output a process parameter value for the process parameter or process parameter values ​​for multiple process parameters. In one embodiment, a neural network is trained to receive the etch rate as an input and output a process parameter value for the process parameter or process parameter values ​​for multiple process parameters.

[0093]

[0101] In step 625, the trained model is deployed. The trained model may be deployed, for example, to one or more process chambers and / or controllers of the cluster tool. Additionally or alternatively, the trained model may be deployed to a server connected to one or more controllers (e.g., controllers of one or more process chambers and / or controllers of one or more cluster tools). Deploying the trained model may include storing the trained model in a feedback engine of the controller and / or server. Once the trained model is deployed, the controller and / or server may use the trained model to perform feedback control of one or more manufacturing processes performed to form the film based on a measured etch rate measured during etching of the film.

[0094]

[0102] FIG. 7 illustrates a diagrammatic representation of a machine in the exemplary form of a computing device 700 upon which a set of instructions may be executed to cause the machine to perform any one or more of the methodologies discussed herein. In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a local area network (LAN), an intranet, an extranet, or the Internet. The machine may operate as 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 machine may be a personal computer (PC), a tablet computer, 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 non-sequential) that specify actions to be taken by the machine. Furthermore, although only a single machine is illustrated, the term "machine" shall also be construed to include any collection of machines (e.g., computers) that individually or in concert execute a set (or sets) of instructions to perform any one or more of the methodologies discussed herein.

[0095]

[0103] The example computing device 700 includes a processing unit 702, a main memory 704 (e.g., read only memory (ROM), flash memory, dynamic random access memory (DRAM) (such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM)), a static memory 706 (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory (e.g., data storage device 718), which communicate with each other via a bus 730.

[0096]

[0104] Processing unit 702 represents one or more general-purpose processors, such as a microprocessor, a central processing unit, etc. More specifically, processing unit 702 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. Processing unit 702 may also be one or more special purpose processing units, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. Processing unit 702 is configured to execute processing logic (instructions 722) for performing the processes and steps discussed herein.

[0097]

[0105] The computing device 700 may further include a network interface device 708. The computing device 700 may also include a video display unit 710 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 712 (e.g., a keyboard), a cursor control device 714 (e.g., a mouse), and a signal generating device 716 (e.g., a speaker).

[0098]

[0106] The data storage device 718 may include a machine-readable storage medium (or more specifically, a computer-readable storage medium) 728 on which is stored one or more sets of instructions 722 embodying any one or more of the methodologies or functions described herein. The instructions 722 may also reside, completely or at least partially, within the main memory 704 and / or within the processing unit 702 during execution thereof by the computer system 700. The main memory 704 and the processing unit 702 also constitute computer-readable storage media.

[0099]

[0107] The computer readable storage medium 728 may also be used to store a software library including the feedback engine 121 and / or methods for invoking the feedback engine 121. Although the computer readable storage medium 728 is shown in an exemplary embodiment to be a single medium, the term "computer 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) that store one or more sets of instructions. The term "computer readable storage medium" should also be interpreted to include any medium that can store or encode a set of instructions for execution by a machine and cause the machine to perform any one or more of the methodologies described herein. Thus, the term "computer readable storage medium" should be interpreted to include, but is not limited to, non-transitory computer readable media, such as solid-state memory, optical media, and magnetic media.

[0100]

[0108] The modules, components, and other features described herein (e.g., in connection with FIGS. 1A-3) may be implemented as discrete hardware components or integrated into the functionality of hardware components such as ASICS, FPGAs, DSPs, or similar devices. In addition, modules may be implemented as firmware or functional circuitry within a hardware device. Furthermore, modules may be implemented as any combination of hardware devices and software components, or may be implemented solely in software.

[0101]

[0109] Some portions of the detailed description have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their invention to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a target result. These steps require physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared and otherwise manipulated. It has proven convenient, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0102]

[0110] It should be noted, however, that all of these and similar terms should be associated with the appropriate physical quantities and are merely convenient labels applied to such quantities. As will become apparent from the following description, unless otherwise specifically stated, throughout this specification discussions utilizing terms such as "receiving," "identifying," "determining," "selecting," "providing," "storing," and the like should be understood to refer to the operations and processes of a computer system or similar electronic computing device. This computer system or similar electronic computing device manipulates and converts data represented as physical quantities (electronic quantities) in the computer system's registers and memory to other data similarly represented as physical quantities in the computer system's memory or registers, or other storage, transmission, or display device for such information.

[0103]

[0111] An embodiment of the present invention also relates to an apparatus for carrying out the processes herein. This apparatus may be specially constructed for the purposes discussed, or may comprise a general-purpose computer system that is selectively programmed by a computer program stored in the computer system. Such a computer program may be stored in a computer readable storage medium, such as any type of disk, including, but not limited to, a floppy disk, an optical disk, a CD-ROM, and a magneto-optical disk, a read-only memory (ROM), a random access memory (RAM), an EPROM, an EEPROM, a magnetic disk storage medium, an optical storage medium, a flash memory device, any other type of machine-accessible storage medium, or any type of medium suitable for storing electronic instructions, each coupled to a computer system bus.

[0104]

[0112] The foregoing description specifies numerous specific details, such as examples of specific systems, components, methods, etc., to provide a good understanding of some embodiments of the present disclosure. However, it will be apparent to one skilled in the art that at least some embodiments of the present disclosure can be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in simple block diagram form to avoid unnecessarily obscuring the present disclosure. Thus, the specific details described are merely exemplary. Certain implementations may differ from these example details and still be considered to be within the scope of the present disclosure.

[0105]

[0113] Throughout this specification, reference to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. In addition, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". When the term "about" or "approximately" is used herein, it is intended to mean that the nominal value presented is accurate to within ±10%.

[0106]

[0114] Although the steps of the methods herein are illustrated and described in a particular order, the order of the steps of each method may be changed such that certain steps are performed in reverse order, and certain steps are performed at least partially simultaneously with other steps. In alternative embodiments, the orders of the separate steps or sub-steps may be intermittent and / or alternating.

[0107]

[0115] It should be understood that the above description is intended to be illustrative, and not limiting. Many other embodiments will become apparent to those skilled in the art upon reading and understanding the above description. The scope of the disclosure should therefore be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. an etch chamber configured to perform an etch process on a substrate, the etch chamber comprising an optical sensor for generating one or more optical measurements of a film on the substrate at least one of during or after the etch process; a computing device operably connected to the etch chamber, receiving the one or more optical measurements of the film; determining a thickness of the film for each optical measurement of the one or more optical measurements; determining an etch rate of the film based on the one or more optical measurements using the determined film thickness for each optical measurement of the one or more optical measurements; determining a process parameter value for at least one process parameter for a previously performed process performed on the substrate based on the etch rate; A computing device that performs the above steps. A substrate processing system comprising:

2. to determine the process parameter value of the at least one process parameter for the previously performed process performed on the substrate based on the etch rate, the computing device determining a film quality value for the film based on the etch rate; and determining the process parameter value for the at least one process parameter based on the film quality value; The substrate processing system of claim 1 , further comprising:

3. The substrate processing system of claim 2 , wherein the film quality value is a value related to at least one of grain size, grain orientation, and average surface roughness of the film.

4. The computing device comprises: determining a difference between the determined process parameter value and a target process parameter value of the at least one process parameter; determining a process chamber that performed the previously performed process on the substrate; determining an adjustment to the at least one process parameter for future substrates processed by the process chamber based on the difference; and The substrate processing system of claim 1 , further comprising:

5. 2. The substrate processing system of claim 1, wherein, for the computing device to determine the process parameter value for the at least one process parameter, the etch rate is input into a trained machine learning model trained to output a process parameter value based on the input etch rate, and the trained machine learning model outputs the process parameter value for the at least one process parameter.

6. The substrate processing system of claim 1 , wherein the optical sensor comprises a spectrometer configured to measure the film thickness using reflectometry.

7. The one or more optical measurements of the film include a plurality of optical measurements of the film, and the computing device: periodically determining an instantaneous etch rate for a region of the film based on a film thickness value and a timestamp associated with two or more optical measurements of the plurality of optical measurements. The substrate processing system of claim 1 , further comprising:

8. The substrate processing system of claim 1 , wherein the previously performed process comprises a deposition process and the at least one process parameter for the previously performed process comprises a temperature.

9. 9. The substrate processing system of claim 8, wherein the at least one process parameter for the previously performed process further comprises at least one of a pressure or a plasma power.

10. performing an etching process to etch a film on a substrate, the film having been previously formed on the substrate using a deposition process; generating one or more optical measurements of the film on the substrate at least one of during or after the etching process; determining a thickness of the film for each optical measurement of the one or more optical measurements; determining an etch rate of the film based on the one or more optical measurements using the determined film thickness for each optical measurement of the one or more optical measurements; determining a process parameter value for at least one process parameter for the deposition process performed on the substrate based on the etch rate; A method comprising:

11. determining the process parameter value for the at least one process parameter for the previously performed process performed on the substrate based on the etch rate; determining a film quality value for the film based on the etch rate; and determining the process parameter value for the at least one process parameter based on the film quality value; The method of claim 10, comprising:

12. The method of claim 11 , wherein the film quality value is a value related to at least one of grain size, grain orientation, or average surface roughness of the film.

13. determining a difference between the determined process parameter value and a target process parameter value of the at least one process parameter; determining a process chamber that performed the previously performed process on the substrate; determining an adjustment to the at least one process parameter for future substrates processed by the process chamber based on the difference; and The method of claim 10 further comprising:

14. 11. The method of claim 10, wherein determining the process parameter value for the at least one process parameter comprises inputting the etch rate into a trained machine learning model trained to output a process parameter value based on an input etch rate, the trained machine learning model outputting the process parameter value for the at least one process parameter.

15. The one or more optical measurements of the film include a plurality of optical measurements of the film, the method comprising: periodically determining an instantaneous etch rate for a region of the film based on a film thickness value and a timestamp associated with two or more optical measurements of the plurality of optical measurements. The method of claim 1 further comprising:

16. The method of claim 1 , wherein the previously performed process comprises a deposition process and the at least one process parameter for the previously performed process comprises a temperature.

17. 17. The method of claim 16, wherein the at least one process parameter for the previously performed process further comprises at least one of pressure or plasma power.

18. receiving or generating a training data set comprising a plurality of data items, each data item of the plurality of data items comprising a process parameter value for at least one process parameter used in a manufacturing process to form a film on a substrate, a film quality value of the film formed by the manufacturing process performed using the process parameter value for the at least one process parameter, and an etch rate of an etching process used to etch the film; training a model based on the training data set, the model receiving as an input an etch rate and outputting at least one of a predicted film quality value or a predicted process parameter value for the at least one process parameter based on the etch rate; A method comprising:

19. performing a plurality of versions of the manufacturing process to form a respective film on each of a plurality of substrates, each version of the manufacturing process from the plurality of versions using a different combination of process parameter values; for each version of the manufacturing process, performing a destructive metrology measurement on a first representative substrate associated with the version of the manufacturing process to determine at least one of a grain characteristic or a crystal characteristic; determining, for each version of the manufacturing process, a film quality value based on at least one of the grain characteristic or the crystal characteristic; for each version of the manufacturing process, performing an etching process on the first representative substrate or a second representative substrate associated with the version of the manufacturing process to determine an etch rate of the etching process; Associating, for each version of the manufacturing process, the etch rate, the film quality value, and at least one of a temperature, a pressure, or a plasma power; The method of claim 18 , further comprising generating the training data set by:

20. 20. The method of claim 19, wherein the etch rate of the etching process is determined based on optical measurements from an optical sensor during the etching process.

21. 20. The method of claim 19, wherein the manufacturing process is a deposition process, the at least one process parameter includes at least one of a temperature, a pressure, or a plasma power, and each version of the deposition process from the plurality of versions uses a different combination of at least one of the temperature, the pressure, or the plasma power to form the respective film on a respective substrate of the plurality of substrates.

22. The method of claim 18 , wherein the model comprises at least one of a linear regression model, a Gaussian regression model, or a neural network.

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