WO2024005047A1 - 基板処理装置の制御方法及び基板処理システム - Google Patents

基板処理装置の制御方法及び基板処理システム Download PDF

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Publication number
WO2024005047A1
WO2024005047A1 PCT/JP2023/023911 JP2023023911W WO2024005047A1 WO 2024005047 A1 WO2024005047 A1 WO 2024005047A1 JP 2023023911 W JP2023023911 W JP 2023023911W WO 2024005047 A1 WO2024005047 A1 WO 2024005047A1
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Prior art keywords
processed
substrate processing
processing apparatus
recess
shape
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PCT/JP2023/023911
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English (en)
French (fr)
Japanese (ja)
Inventor
翔 熊倉
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Tokyo Electron Ltd
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Tokyo Electron Ltd
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Priority to JP2024530905A priority Critical patent/JPWO2024005047A1/ja
Priority to CN202380062542.9A priority patent/CN119836681A/zh
Priority to KR1020257001985A priority patent/KR20250028381A/ko
Priority to TW112124527A priority patent/TW202410181A/zh
Publication of WO2024005047A1 publication Critical patent/WO2024005047A1/ja
Priority to US19/002,147 priority patent/US20250125130A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
    • H01J37/32—Gas-filled discharge tubes
    • H01J37/32917—Plasma diagnostics
    • H01J37/32926—Software, data control or modelling
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10P—GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10
    • H10P50/00—Etching of wafers, substrates or parts of devices
    • H10P50/20—Dry etching; Plasma etching; Reactive-ion etching
    • H10P50/24—Dry etching; Plasma etching; Reactive-ion etching of semiconductor materials
    • H10P50/242—Dry etching; Plasma etching; Reactive-ion etching of semiconductor materials of Group IV materials
    • H10P50/244—Dry etching; Plasma etching; Reactive-ion etching of semiconductor materials of Group IV materials comprising alternated and repeated etching and passivation steps
    • C—CHEMISTRY; METALLURGY
    • C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C16/00—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes
    • C23C16/04—Coating on selected surface areas, e.g. using masks
    • C23C16/045—Coating cavities or hollow spaces, e.g. interior of tubes; Infiltration of porous substrates
    • C—CHEMISTRY; METALLURGY
    • C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C16/00—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes
    • C23C16/44—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes characterised by the method of coating
    • C23C16/455—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes characterised by the method of coating characterised by the method used for introducing gases into reaction chamber or for modifying gas flows in reaction chamber
    • C23C16/45523—Pulsed gas flow or change of composition over time
    • C23C16/45525—Atomic layer deposition [ALD]
    • C23C16/45527—Atomic layer deposition [ALD] characterized by the ALD cycle, e.g. different flows or temperatures during half-reactions, unusual pulsing sequence, use of precursor mixtures or auxiliary reactants or activations
    • C23C16/45536—Use of plasma, radiation or electromagnetic fields
    • C—CHEMISTRY; METALLURGY
    • C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C16/00—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes
    • C23C16/44—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes characterised by the method of coating
    • C23C16/455—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes characterised by the method of coating characterised by the method used for introducing gases into reaction chamber or for modifying gas flows in reaction chamber
    • C23C16/45523—Pulsed gas flow or change of composition over time
    • C23C16/45525—Atomic layer deposition [ALD]
    • C23C16/45553—Atomic layer deposition [ALD] characterized by the use of precursors specially adapted for ALD
    • C—CHEMISTRY; METALLURGY
    • C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C16/00—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes
    • C23C16/44—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes characterised by the method of coating
    • C23C16/52—Controlling or regulating the coating process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/092—Reinforcement learning
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
    • H01J37/32—Gas-filled discharge tubes
    • H01J37/32009—Arrangements for generation of plasma specially adapted for examination or treatment of objects, e.g. plasma sources
    • H01J37/32082—Radio frequency generated discharge
    • H01J37/32091—Radio frequency generated discharge the radio frequency energy being capacitively coupled to the plasma
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
    • H01J37/32—Gas-filled discharge tubes
    • H01J37/32917—Plasma diagnostics
    • H01J37/32935—Monitoring and controlling tubes by information coming from the object and/or discharge
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10P—GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10
    • H10P14/00—Formation of materials, e.g. in the shape of layers or pillars
    • H10P14/60—Formation of materials, e.g. in the shape of layers or pillars of insulating materials
    • H10P14/63—Formation of materials, e.g. in the shape of layers or pillars of insulating materials characterised by the formation processes
    • H10P14/6326—Deposition processes
    • H10P14/6328—Deposition from the gas or vapour phase
    • H10P14/6334—Deposition from the gas or vapour phase using decomposition or reaction of gaseous or vapour phase compounds, i.e. chemical vapour deposition
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10P—GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10
    • H10P72/00—Handling or holding of wafers, substrates or devices during manufacture or treatment thereof
    • H10P72/06—Apparatus for monitoring, sorting, marking, testing or measuring
    • H10P72/0604—Process monitoring, e.g. flow or thickness monitoring
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10P—GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10
    • H10P72/00—Handling or holding of wafers, substrates or devices during manufacture or treatment thereof
    • H10P72/06—Apparatus for monitoring, sorting, marking, testing or measuring
    • H10P72/0612—Production flow monitoring, e.g. for increasing throughput
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J2237/00—Discharge tubes exposing object to beam, e.g. for analysis treatment, etching, imaging
    • H01J2237/32—Processing objects by plasma generation
    • H01J2237/33—Processing objects by plasma generation characterised by the type of processing
    • H01J2237/332—Coating
    • H01J2237/3321—CVD [Chemical Vapor Deposition]
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J2237/00—Discharge tubes exposing object to beam, e.g. for analysis treatment, etching, imaging
    • H01J2237/32—Processing objects by plasma generation
    • H01J2237/33—Processing objects by plasma generation characterised by the type of processing
    • H01J2237/334—Etching
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10P—GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10
    • H10P50/00—Etching of wafers, substrates or parts of devices
    • H10P50/20—Dry etching; Plasma etching; Reactive-ion etching
    • H10P50/24—Dry etching; Plasma etching; Reactive-ion etching of semiconductor materials
    • H10P50/242—Dry etching; Plasma etching; Reactive-ion etching of semiconductor materials of Group IV materials

Definitions

  • the present disclosure relates to a method for controlling a substrate processing apparatus and a substrate processing system.
  • the aspect ratio of patterns formed in the manufacturing process of semiconductor devices is also increasing.
  • channel holes are formed in a direction that penetrates a large number of metal wiring layers. If a 64-layer memory cell is formed, the aspect ratio of the channel hole will be as high as 45.
  • Various methods have been proposed to form high aspect ratio patterns with high precision. For example, a method has been proposed in which lateral etching is suppressed by repeatedly performing etching and film formation on an opening formed in a dielectric material of a substrate. Furthermore, a method has been proposed in which a protective film is formed to prevent lateral etching of a dielectric layer by combining etching and film formation (for example, see Patent Document 1).
  • the present disclosure provides a control method for a substrate processing apparatus and a substrate processing system that can efficiently guide a good opening shape.
  • a method for controlling a substrate processing apparatus includes a) partially etching an object to be processed to form a recess in the object to be processed, and b) forming a recess formed in the object to be processed. c) further etching the recess and the object on which the protective film is formed; d) repeating b) and c); and e) repeating the steps b) and c). monitoring the object to be processed obtained in at least one of steps a) to d); f) conducting a virtual experiment simulating steps a) to d); and g) monitoring results of the object.
  • a good opening shape can be efficiently derived.
  • FIG. 1 is a diagram for explaining an example configuration of a substrate processing system.
  • FIG. 2 is a diagram for explaining a configuration example of a capacitively coupled plasma processing apparatus. It is a flowchart explaining the flow of general ALD.
  • FIG. 2 is an explanatory diagram illustrating a first method of subconformal ALD.
  • FIG. 3 is an explanatory diagram illustrating a second method of subconformal ALD. It is a flowchart explaining the flow of ALD in this embodiment. It is a flowchart explaining the flow of ALD in this embodiment.
  • FIG. 2 is an explanatory diagram illustrating a configuration example of an etching simulator. 3 is a flowchart illustrating a model update procedure.
  • FIG. 1 is a diagram for explaining an example configuration of a substrate processing system.
  • FIG. 2 is a diagram for explaining a configuration example of a capacitively coupled plasma processing apparatus. It is a flowchart explaining the flow of general ALD.
  • FIG. 2 is an explan
  • FIG. 3 is an explanatory diagram illustrating the thickness of a protective film formed by the substrate processing method according to the embodiment. It is a schematic diagram of a reinforcement learning algorithm.
  • FIG. 3 is a schematic diagram showing a first configuration example of a plasma processing system in Embodiment 3.
  • FIG. 3 is a schematic diagram showing a second configuration example of a plasma processing system in Embodiment 3.
  • FIG. 1 is a diagram for explaining a configuration example of a substrate processing system.
  • the substrate processing system PS includes tables BA1 to BA1, containers RC1 to RC4, a loader module LM, an aligner AN, load lock modules LL1 and LL2, process modules PM1 to PM6, a transfer module TF, and a control device MC.
  • the number of units, containers, and load lock modules in the substrate processing system PS may be any number greater than or equal to one.
  • the number of process modules in the substrate processing system PS may be any number greater than or equal to two.
  • the stands BA1 to BA4 are arranged along one edge of the loader module LM.
  • Containers RC1 to RC4 are mounted on stands BA1 to BA4, respectively.
  • Each of the containers RC1 to RC4 is, for example, a container called a FOUP (Front Opening Unified Pod).
  • Each of the containers RC1 to RC4 is configured to accommodate a substrate W therein.
  • the loader module LM has a chamber. The pressure within the chamber of the loader module LM is set to atmospheric pressure.
  • the loader module LM has a transport device TU1.
  • the transport device TU1 is, for example, an articulated robot, and is controlled by a control device MC.
  • the transport device TU1 is configured to transport the substrate W through the chamber of the loader module LM.
  • the transport device TU1 is arranged between each of the containers RC1 to RC4 and the aligner AN, between the aligner AN and each of the load lock modules LL1 to LL2, and between each of the load lock modules LL1 to LL2 and each of the containers RC1 to RC4.
  • the substrate W can be transported between them.
  • Aligner AN is connected to loader module LM.
  • the aligner AN is configured to adjust the position of the substrate W (position calibration).
  • Each of the load lock module LL1 and the load lock module LL2 is provided between the loader module LM and the transport module TF.
  • Each of load lock module LL1 and load lock module LL2 provides a preliminary vacuum chamber.
  • the transfer module TF is connected to each of the load lock module LL1 and the load lock module LL2 via gate valves.
  • the transfer module TF has a transfer chamber TC that can be depressurized.
  • the transport module TF has a transport device TU2.
  • the transport device TU2 is, for example, an articulated robot, and is controlled by the control device MC.
  • the transport device TU2 is configured to transport the substrate W through the transport chamber TC.
  • the transport device TU2 can transport the substrate W between each of the load lock modules LL1 to LL2 and each of the process modules PM1 to PM6, and between any two process modules among the process modules PM1 to PM6. .
  • Each of the process modules PM1 to PM6 is a processing device configured to perform dedicated substrate processing.
  • One of the process modules PM1 to PM6 is a film forming apparatus.
  • This film forming apparatus is used to form a protective film PF in a film forming process described below.
  • This film forming apparatus is a plasma processing apparatus having a configuration for generating plasma when plasma is generated in the film forming process, and forms the protective film PF without generating plasma in the film forming process. In some cases, it is not necessary to have a configuration for generating plasma.
  • Another process module among the process modules PM1 to PM6 is an etching device. The etching apparatus is used to form a pattern on the surface of a target object in an etching process described below.
  • control device MC is configured to control each part of the substrate processing system PS.
  • the control device MC can control the operation of the etching device, for example, to form a recess in the object to be processed and to form a pattern on the surface of the object to be processed. Further, the control device MC can control the film forming apparatus in order to form a protective film on the side wall of the formed recess.
  • the substrate processing system PS includes an observation device OC.
  • the observation device OC can be installed at any location within the substrate processing system PS. In one example, the observation device OC is installed in the observation module OM adjacent to the loader module LM.
  • the substrate W can be moved between the observation module OM and the process modules PM1 to PM6 by the transport device TU1 and the transport device TU2. After the substrate W is accommodated in the observation module OM by the transport device TU1 and the substrate W is aligned in the observation module OM, the observation device OC measures the groove width of a pattern such as a mask on the substrate W, Send the measurement results to the control device MC.
  • the observation device OC can measure the groove width of a pattern such as a mask formed in a plurality of regions on the surface of the substrate W.
  • a pattern such as a mask formed in a plurality of regions on the surface of the substrate W.
  • an optical observation device, a gravimeter, an ultrasonic microscope, etc. can be used.
  • FIG. 2 is a diagram for explaining a configuration example of a capacitively coupled plasma processing apparatus.
  • the plasma processing system includes a capacitively coupled plasma processing apparatus 1 and a control section 2.
  • the capacitively coupled plasma processing apparatus 1 includes a plasma processing chamber 10, a gas supply section 20, a power supply 30, and an exhaust system 40. Further, the plasma processing apparatus 1 includes a substrate support section 11 and a gas introduction section.
  • the gas inlet is configured to introduce at least one processing gas into the plasma processing chamber 10 .
  • the gas introduction section includes a shower head 13.
  • Substrate support 11 is arranged within plasma processing chamber 10 .
  • the shower head 13 is arranged above the substrate support section 11 . In one embodiment, showerhead 13 forms at least a portion of the ceiling of plasma processing chamber 10 .
  • the plasma processing chamber 10 has a plasma processing space 10s defined by a shower head 13, a side wall 10a of the plasma processing chamber 10, and a substrate support 11.
  • the plasma processing chamber 10 has at least one gas supply port for supplying at least one processing gas to the plasma processing space 10s, and at least one gas exhaust port for discharging gas from the plasma processing space.
  • Plasma processing chamber 10 is grounded.
  • the shower head 13 and the substrate support section 11 are electrically insulated from the casing of the plasma processing chamber 10.
  • the substrate support section 11 includes a main body section 111 and a ring assembly 112.
  • the main body portion 111 has a central region 111a for supporting the substrate W and an annular region 111b for supporting the ring assembly 112.
  • a wafer is an example of a substrate W.
  • the annular region 111b of the main body 111 surrounds the central region 111a of the main body 111 in plan view.
  • the substrate W is placed on the central region 111a of the main body 111, and the ring assembly 112 is placed on the annular region 111b of the main body 111 so as to surround the substrate W on the central region 111a of the main body 111. Therefore, the central region 111a is also called a substrate support surface for supporting the substrate W, and the annular region 111b is also called a ring support surface for supporting the ring assembly 112.
  • the main body 111 includes a base 1110 and an electrostatic chuck 1111.
  • Base 1110 includes a conductive member.
  • the conductive member of the base 1110 can function as a bottom electrode.
  • Electrostatic chuck 1111 is placed on base 1110.
  • Electrostatic chuck 1111 includes a ceramic member 1111a and an electrostatic electrode 1111b disposed within ceramic member 1111a.
  • Ceramic member 1111a has a central region 111a. In one embodiment, ceramic member 1111a also has an annular region 111b. Note that another member surrounding the electrostatic chuck 1111, such as an annular electrostatic chuck or an annular insulating member, may have the annular region 111b.
  • ring assembly 112 may be placed on the annular electrostatic chuck or the annular insulation member, or may be placed on both the electrostatic chuck 1111 and the annular insulation member.
  • at least one RF/DC electrode coupled to an RF (Radio Frequency) power source 31 and/or a DC (Direct Current) power source 32, which will be described later, may be arranged within the ceramic member 1111a.
  • at least one RF/DC electrode functions as a bottom electrode.
  • An RF/DC electrode is also referred to as a bias electrode if a bias RF signal and/or a DC signal, as described below, is supplied to at least one RF/DC electrode.
  • the conductive member of the base 1110 and at least one RF/DC electrode may function as a plurality of lower electrodes.
  • the electrostatic electrode 1111b may function as a lower electrode. Therefore, the substrate support 11 includes at least one lower electrode.
  • Ring assembly 112 includes one or more annular members.
  • the one or more annular members include one or more edge rings and at least one cover ring.
  • the edge ring is made of a conductive or insulating material
  • the cover ring is made of an insulating material.
  • the substrate support unit 11 may include a temperature control module configured to adjust at least one of the electrostatic chuck 1111, the ring assembly 112, and the substrate to a target temperature.
  • the temperature control module may include a heater, a heat transfer medium, a flow path 1110a, or a combination thereof.
  • a heat transfer fluid such as brine or gas flows through the flow path 1110a.
  • a channel 1110a is formed within the base 1110 and one or more heaters are disposed within the ceramic member 1111a of the electrostatic chuck 1111.
  • the substrate support section 11 may include a heat transfer gas supply section configured to supply heat transfer gas to the gap between the back surface of the substrate W and the central region 111a.
  • the shower head 13 is configured to introduce at least one processing gas from the gas supply section 20 into the plasma processing space 10s.
  • the shower head 13 has at least one gas supply port 13a, at least one gas diffusion chamber 13b, and a plurality of gas introduction ports 13c.
  • the processing gas supplied to the gas supply port 13a passes through the gas diffusion chamber 13b and is introduced into the plasma processing space 10s from the plurality of gas introduction ports 13c.
  • the showerhead 13 also includes at least one upper electrode.
  • the gas introduction section may include one or more side gas injectors (SGI) attached to one or more openings formed in the side wall 10a.
  • SGI side gas injectors
  • the gas supply section 20 may include at least one gas source 21 and at least one flow rate controller 22.
  • the gas supply 20 is configured to supply at least one process gas from a respective gas source 21 to the showerhead 13 via a respective flow controller 22 .
  • Each flow controller 22 may include, for example, a mass flow controller or a pressure-controlled flow controller.
  • gas supply 20 may include one or more flow modulation devices that modulate or pulse the flow rate of at least one process gas.
  • Power supply 30 includes an RF power supply 31 coupled to plasma processing chamber 10 via at least one impedance matching circuit.
  • RF power source 31 is configured to supply at least one RF signal (RF power) to at least one bottom electrode and/or at least one top electrode.
  • RF power source 31 may function as at least part of a plasma generation unit configured to generate a plasma from one or more process gases in plasma processing chamber 10 .
  • a bias potential is generated in the substrate W, and ion components in the formed plasma can be drawn into the substrate W.
  • the RF power supply 31 includes a first RF generation section 31a and a second RF generation section 31b.
  • the first RF generation section 31a is coupled to at least one lower electrode and/or at least one upper electrode via at least one impedance matching circuit, and generates a source RF signal (source RF power) for plasma generation. It is configured as follows.
  • the source RF signal has a frequency within the range of 10 MHz to 150 MHz.
  • the first RF generator 31a may be configured to generate multiple source RF signals having different frequencies. The generated one or more source RF signals are provided to at least one bottom electrode and/or at least one top electrode.
  • the second RF generating section 31b is coupled to at least one lower electrode via at least one impedance matching circuit, and is configured to generate a bias RF signal (bias RF power).
  • the frequency of the bias RF signal may be the same or different than the frequency of the source RF signal.
  • the bias RF signal has a lower frequency than the frequency of the source RF signal.
  • the bias RF signal has a frequency within the range of 100kHz to 60MHz.
  • the second RF generator 31b may be configured to generate multiple bias RF signals having different frequencies.
  • the generated one or more bias RF signals are provided to at least one bottom electrode. Also, in various embodiments, at least one of the source RF signal and the bias RF signal may be pulsed.
  • Power source 30 may also include a DC power source 32 coupled to plasma processing chamber 10 .
  • the DC power supply 32 includes a first DC generation section 32a and a second DC generation section 32b.
  • the first DC generator 32a is connected to at least one lower electrode and configured to generate a first DC signal.
  • the generated first bias DC signal is applied to the at least one bottom electrode.
  • the second DC generator 32b is connected to the at least one upper electrode and configured to generate a second DC signal.
  • the generated second DC signal is applied to the at least one top electrode.
  • At least one of the first and second DC signals may be pulsed.
  • a sequence of pulsed voltages is applied to the at least one bottom electrode and/or the at least one top electrode.
  • the pulse voltage may have a pulse waveform that is rectangular, trapezoidal, triangular, or a combination thereof.
  • a waveform generator for generating a sequence of pulsed voltages from a DC signal is connected between the first DC generator 32a and the at least one bottom electrode. Therefore, the first DC generating section 32a and the waveform generating section constitute a pulse voltage generating section.
  • the pulse voltage generation section is connected to at least one upper electrode.
  • the pulse voltage may have positive polarity or negative polarity. Further, the pulse voltage sequence may include one or more positive pulse voltages and one or more negative pulse voltages within one cycle. Note that the first and second DC generation units 32a and 32b may be provided in addition to the RF power source 31, or the first DC generation unit 32a may be provided in place of the second RF generation unit 31b. good.
  • the exhaust system 40 may be connected to a gas exhaust port 10e provided at the bottom of the plasma processing chamber 10, for example.
  • Evacuation system 40 may include a pressure regulating valve and a vacuum pump. The pressure within the plasma processing space 10s is adjusted by the pressure regulating valve.
  • the vacuum pump may include a turbomolecular pump, a dry pump, or a combination thereof.
  • the plasma processing apparatus 1 is equipped with an optical sensor 108 that can measure the intensity of light of each wavelength in the plasma in the plasma processing space 10s through a quartz window 109.
  • the optical sensor 108 includes a first sensor 108a and a second sensor 108b.
  • the first sensor 108a is a sensor for sensing the state of plasma generated within the plasma processing space 10s.
  • the second sensor 108b is a sensor for sensing the pattern shape on the surface of the substrate W placed on the base 1110. Sensing data from the first sensor 108a and the second sensor 108b is output to the control unit 2.
  • the control unit 2 measures/estimates the plasma state in the plasma processing chamber 10 and the pattern shape of the substrate W based on sensing data from the first sensor 108a and the second sensor 108b.
  • the control unit 2 processes computer-executable instructions that cause the plasma processing apparatus 1 to perform various steps described in this disclosure.
  • the control unit 2 may be configured to control each element of the plasma processing apparatus 1 to perform the various steps described herein. In one embodiment, part or all of the control unit 2 may be included in the plasma processing apparatus 1.
  • the control unit 2 may include a processing unit 2a1, a storage unit 2a2, and a communication interface 2a3.
  • the control unit 2 is realized by, for example, a computer 2a.
  • the processing unit two a1 may be configured to read a program from the storage unit two a2 and perform various control operations by executing the read program. This program may be stored in the storage unit 2a2 in advance, or may be acquired via a medium when necessary.
  • the acquired program is stored in the storage unit 2a2, and is read out from the storage unit 2a2 and executed by the processing unit 2a1.
  • the medium may be various storage media readable by the computer 2a, or may be a communication line connected to the communication interface 2a3.
  • the processing unit 2a1 may be a CPU (Central Processing Unit).
  • the storage unit 2a2 may include a RAM (Random Access Memory), a ROM (Read Only Memory), an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a combination thereof.
  • the communication interface 2a3 may communicate with the plasma processing apparatus 1 via a communication line such as a LAN.
  • the programs stored in the storage unit 2a2 include a simulator for virtual experiments that simulates processes (actual experiments) executed in the substrate processing system PS. Simulators for virtual experiments include plasma simulators, reaction product simulators, shape simulators, and the like. Further, the program stored in the storage unit 2a2 may be a program for realizing VM (Virtual Metrology) technology. These computer programs may be a single computer program or may be composed of multiple computer programs. Furthermore, these computer programs may partially use existing libraries.
  • a shape abnormality called bowing is a phenomenon in which, even when an opening is formed in the vertical direction (in the thickness direction of the substrate), the inner peripheral surface of the opening bulges in the horizontal direction (in the in-plane direction of the substrate).
  • a method of forming a protective film on the side wall of the opening has been proposed.
  • ALD atomic layer deposition
  • PEALD plasma-enhanced ALD
  • CVD chemical vapor deposition
  • PECCVD plasma-enhanced CVD
  • PECCVD plasma annular chemical vapor deposition
  • the term “pattern” refers to the overall shape formed on the substrate.
  • a pattern refers to a plurality of shapes formed on a substrate, such as holes, trenches, lines and spaces, etc., for example.
  • the term “concave portion” refers to a portion of the pattern formed on the substrate that is recessed in the thickness direction of the substrate.
  • the recess has a "side wall” which is a recessed inner peripheral surface, a "bottom” which is a recessed bottom part, and a “top” which is a substrate surface near the side wall that is continuous with the side wall.
  • the space surrounded by the top is called the "opening”. Note that the term “opening” is also used to refer to the entire space or any position in the space surrounded by the bottom and side walls of the recess.
  • FIG. 3 is a flowchart explaining the flow of general ALD.
  • the substrate processing system PS provides the object to be processed inside the plasma processing chamber 10 (step S11).
  • the object to be processed is, for example, a substrate on which a pattern with a high aspect ratio is formed by another process module.
  • the object to be processed may be a substrate on which no pattern is formed.
  • the pattern may be formed by partially etching the object to be processed.
  • the plasma processing apparatus 1 introduces the first gas into the plasma processing chamber 10 (step S12).
  • the first gas is also called a precursor.
  • the plasma processing apparatus 1 purges the plasma processing chamber 10 and discharges the components of the first gas excessively adsorbed on the surface of the object to be processed (step S13).
  • the plasma processing apparatus 1 introduces the second gas into the plasma processing chamber 10 and generates plasma of the second gas (step S14).
  • the second gas is also called a reactive gas.
  • the plasma processing apparatus 1 purges the plasma processing chamber 10 to discharge excess second gas components (step S15).
  • a protective film is formed on the side wall of the opening by the process of steps S12 to S15. After forming the protective film, the plasma processing apparatus 1 etches the object to be processed (step S16).
  • the procedure is such that both the film formation process and the etching process are performed in one plasma processing apparatus 1, but the film formation process is performed in one plasma processing apparatus and the etching process is performed in another plasma processing apparatus. Etching treatment may also be performed. Further, the film forming process and the etching process may be performed in the same substrate processing system or may be performed in different substrate processing systems.
  • the plasma processing apparatus 1 may measure the thickness of the protective film after step S15 and determine whether the required thickness has been obtained. If the required film thickness is not obtained, the plasma processing apparatus 1 may return the process to step S12 and continue forming the protective film.
  • the second sensor 108b is used in the case of in-situ
  • the observation device OC is used in the case of ex-situ.
  • the plasma processing apparatus 1 may measure the shape of the pattern after step S16 and determine whether the desired shape has been obtained. If the desired shape is not obtained, the plasma processing apparatus 1 may return the process to step S12 and continue forming and etching the protective film.
  • the second sensor 108b is used, and in the case of ex-situ, the observation device OC is used.
  • ALD atomic layer deposition
  • a film is formed by a specific component adsorbing and reacting with a substance present on the substrate surface in a self-controlled manner. Therefore, in ALD, conformal film formation can be achieved by providing sufficient processing time. For example, in the flowchart of FIG. 3, if the processing time of steps S12 and S14 is made sufficiently long (the processing conditions are set to saturation conditions), the components of the first gas are adsorbed onto the substrate, and the components of the first gas are The reaction between the gas and the components of the second gas reaches saturation and a conformal film is formed.
  • a conformal film is a film that has a uniform thickness regardless of its position on the substrate (eg, vertical position).
  • subconformal ALD the same processing procedure as ALD is used, but the processing conditions are controlled so that at least one of the adsorption and reaction of film-forming components does not reach saturation. That is, in subconformal ALD, a subconformal film is formed by not allowing self-limiting adsorption or reaction to complete on the surface of a substrate.
  • a subconformal film is a film whose thickness changes depending on its position on the substrate (for example, its position in the vertical direction). For example, the film may be thick on the upper side (opening side) and thin on the lower side, or the film may be a film whose thickness decreases from the upper side to the lower side.
  • FIG. 4 is an explanatory diagram illustrating the first method of subconformal ALD.
  • the object to be processed shown in FIG. 4 includes an etching target film EL1 and a mask MA.
  • a recessed portion having an opening OP is formed in the stack of the etching target film EL1 and the mask MA.
  • the plasma processing apparatus 1 introduces the precursor P into the plasma processing chamber 10 in which the object to be processed is placed (FIG. 4(A)).
  • a sufficient processing time is set for adsorption of the precursor P.
  • the precursor P is adsorbed onto the entire surface of the object to be processed (FIG. 4(B)).
  • the plasma processing apparatus 1 purges the plasma processing chamber 10, and then introduces the reactive gas R into the plasma processing chamber 10 (FIG. 4(C)).
  • the introduced reactive gas R reacts with the precursor P on the object to be processed, and gradually forms the protective film PF from above the mask MA.
  • the reactive gas R is purged.
  • the protective film PF is formed above the sidewall and the top of the recess, but is not formed below the sidewall and the bottom.
  • FIG. 5 is an explanatory diagram illustrating the second method of subconformal ALD.
  • the shape of the object to be processed shown in FIG. 5 is the same as the shape of the object to be processed shown in FIG.
  • the plasma processing apparatus 1 causes the precursor P to be adsorbed only to the upper part of the object to be processed (FIG. 5(A)).
  • the plasma processing apparatus 1 introduces the reactive gas R into the plasma processing chamber 10 (FIG. 5(B)).
  • the reactive gas R reacts and forms a film only at the position where the precursor P is adsorbed, so that the protective film PF is formed only above the object to be processed (FIG. 5(C)).
  • step S14 in FIG. 3 corresponds to an example in which step S14 in FIG. 3 is executed under unsaturated conditions
  • FIG. 5 corresponds to an example in which step S12 in FIG. 3 is executed under unsaturated conditions. If the processing time of step S12 and step S14 is made sufficiently long, the formed film becomes conformal. Therefore, in subconformal ALD, processing conditions are set so that at least one of adsorption and reaction of film forming components does not reach saturation.
  • the processing conditions to be adjusted to realize subconformal ALD include, for example, the temperature of the substrate support 11 on which the substrate W is placed, the pressure inside the plasma processing chamber 10, the flow rate and introduction time of the precursor to be introduced, and the reaction gas to be introduced. gas flow rate, introduction time, processing time, etc.
  • the film forming position can also be adjusted by adjusting the value of radio frequency (RF) power applied for plasma generation.
  • RF radio frequency
  • the substrate processing system PS provides an object to be processed inside the plasma processing chamber 10 (step S101).
  • the object to be processed is a substrate on which no pattern is formed.
  • sensing is performed by the second sensor 108b at any time, and sensing data obtained from the second sensor 108b is output to the control unit 2.
  • the control unit 2 measures/estimates the shape of the surface of the object to be processed provided inside the plasma processing chamber 10 based on the sensor data of the second sensor 108b (step S102).
  • the shape measured/estimated in step S102 may be the shape of each recess formed on the surface of the object to be processed, or may be the uniformity of the overall shape of the recess on the surface of the object to be processed.
  • the control unit 2 includes information regarding the substrate to be processed, processing conditions of the etching process, various output data output from the plasma processing apparatus 1, and data measured during execution of the etching process.
  • Various measurement data are input.
  • the information regarding the substrate to be processed includes information such as the material, thickness, aspect ratio, and mask coverage of the substrate.
  • the processing conditions of the etching process include information such as the pressure inside the chamber, the power of the high-frequency power source, the gas flow rate, the gas mixture ratio, the temperature inside the chamber, and the temperature on the surface of the object to be processed.
  • the output data of the plasma processing apparatus 1 includes data such as source RF power, bias RF power, and emission intensity by OES (Optical Emission Spectrometer).
  • Measurement data during processing includes data such as plasma density, ion energy, and ion flow rate.
  • the control unit 2 executes virtual etching that simulates the etching process in the plasma processing apparatus 1 (step S103).
  • the control unit 2 estimates the shape of the object to be processed after etching by simulation, using the pattern shape measured/estimated in step S102 as the initial shape.
  • a configuration example of the etching simulator will be described in detail later.
  • the control unit 2 acquires various parameters used in the virtual etching, and derives parameters to be applied to the actual experiment based on the acquired parameters (step S104).
  • the parameters used in virtual etching include substrate parameters such as substrate material, thickness, aspect ratio, and mask coverage, as well as pressure in the chamber, power of high-frequency power supply, gas flow rate, gas mixture ratio, Parameters include the temperature inside the chamber, the temperature on the surface of the object to be processed, source RF power, bias RF power, OES, plasma density, ion energy, ion flow rate, and the like.
  • the types of parameters to be applied to the actual experiment may be set in advance or may be selected by the control unit 2.
  • the control unit 2 may compare the parameters set in the actual experiment with the parameters acquired in the virtual experiment, and select the parameters to be applied to the actual experiment based on the difference between the two.
  • control unit 2 may use learning models of machine learning including deep learning, reinforcement learning, etc., statistical models, and models based on combinations thereof. good. These models are generated by using well-known methods such as machine learning and statistical analysis to find quantitative relationships between the parameters used in virtual etching and the parameters to be applied in actual experiments. be done.
  • the control unit 2 can derive parameters to be applied to the actual experiment by inputting the parameters acquired in step S104 to the generated model.
  • the control unit 2 also controls the control unit 2 to increase the matching rate between the shape measured/estimated in the actual experiment and the shape predicted in the virtual experiment, or to shorten the process processing time (throughput). Parameters to be applied to actual experiments may be optimized.
  • the plasma processing apparatus 1 acquires the parameters derived by the virtual experiment in step S103, and performs etching applying the acquired parameters (step S105). This etching process is a process used in actual experiments.
  • the control unit 2 acquires sensing data output from the second sensor 108b during execution of etching (actual experiment).
  • the control unit 2 measures/estimates the pattern shape of the pattern formed by the etching in step S105 based on the sensing data of the second sensor 108b (step S106).
  • the shape measured/estimated in step S106 may be the shape of each recess formed on the surface of the object to be processed, or may be the uniformity of the overall shape of the recess on the surface of the object to be processed.
  • the control unit 2 determines whether the ideal shape has been obtained based on the measurement/estimation results of the pattern shape (step S107).
  • the control unit 2 measures/estimates the shape of the recess formed by etching based on the sensor data obtained from the second sensor 108b, and determines whether the recess has the required aspect ratio. Determine whether the shape has been obtained.
  • the control unit 2 measures/estimates the opening width and opening depth of the recess formed by etching, and determines whether the opening width and opening depth are within a set range, thereby determining the ideal width and depth. It may also be determined whether the shape has been obtained.
  • control unit 2 may compare the pattern shape measurement/estimation result with a set value set for the pattern shape, and may stop subsequent processing depending on the comparison result.
  • the set values are values set for the aspect ratio, opening width, opening depth, etc. of the pattern shape.
  • the control unit 2 may output an alarm depending on the comparison result between the measurement/estimation result of the pattern shape and the set value.
  • the control unit 2 notifies the terminal carried by the user, through the communication interface 2a3, of information that the measurement/estimation result of the pattern shape exceeds the set value (or is less than the set value). Outputs an alarm. If the computer 2a has a display or an audio output unit, the warning may be output by displaying text information on the display or outputting audio from the audio output unit.
  • control unit 2 If it is determined that the ideal shape has not been obtained (S107: NO), the control unit 2 returns the process to step S105 and continues etching the object to be processed until the ideal shape is obtained. At this time, the control unit 2 acquires various output data and various measurement data output from the plasma processing apparatus 1 at any time during the execution of etching, and repeatedly performs virtual etching by referring to the acquired data. good. The control unit 2 can derive parameters to be applied to actual experiments from virtual etching, and apply them to etching (actual experiments) repeatedly performed in the plasma processing apparatus 1.
  • control unit 2 After executing virtual etching in step S103, the control unit 2 executes virtual ALD (step S108).
  • the control unit 2 estimates the shape of the object to be processed after ALD by simulation, using the pattern shape obtained by the virtual etching in step S103 as the initial shape.
  • a configuration example of the ALD simulator will be described in detail later.
  • the control unit 2 acquires various parameters used in the virtual ALD, and derives parameters to be applied to the actual experiment based on the acquired parameters (step S109).
  • Parameters used in virtual ALD are similar to those of virtual etching, including substrate material, thickness, aspect ratio, mask coverage, pressure in the chamber, power of the high-frequency power supply, gas flow rate, gas mixture ratio, and the inside of the chamber. parameters such as the temperature of the surface of the object to be processed, the source RF power, the bias RF power, the OES, the plasma density, the ion energy, and the ion flow rate.
  • the types of parameters to be applied to the actual experiment may be set in advance or may be selected by the control unit 2. For example, the control unit 2 may compare the parameters set in the actual experiment with the parameters acquired in the virtual experiment, and select the parameters to be applied to the actual experiment based on the difference between the two.
  • control unit 2 may use learning models of machine learning including deep learning, reinforcement learning, etc., statistical models, and models based on combinations thereof. good. These models are generated by using well-known techniques such as machine learning and statistical analysis to find quantitative relationships between the parameters used in virtual ALD and the parameters to be applied in actual experiments. be done.
  • the control unit 2 can derive parameters to be applied to the actual experiment by inputting the parameters acquired in step S109 into the generated model.
  • the control unit 2 also controls the control unit 2 to increase the matching rate between the shape measured/estimated in the actual experiment and the shape predicted in the virtual experiment, or to shorten the process processing time (throughput). Parameters to be applied to actual experiments may be optimized.
  • step S107 the plasma processing apparatus 1 acquires the parameters derived by virtual ALD, and performs ALD to which the acquired parameters are applied.
  • This ALD is a process used in actual experiments.
  • the ALD performed may be conformal ALD or subconformal ALD.
  • ALD is performed according to the following steps S110 to S118.
  • the plasma processing apparatus 1 introduces a first gas (precursor) into the plasma processing chamber 10 (step S110). Next, the plasma processing apparatus 1 purges the plasma processing chamber 10 to discharge the components of the first gas excessively adsorbed onto the surface of the object to be processed (step S111).
  • the plasma processing apparatus 1 introduces a second gas (reactive gas) into the plasma processing chamber 10 and generates plasma of the second gas (step S112).
  • a second gas reactive gas
  • the control unit 2 acquires sensing data output from the first sensor 108a during plasma generation, and measures/estimates the plasma state based on the acquired sensing data (step S113). The control unit 2 determines whether the plasma state in the plasma processing chamber 10 is a desired state based on the plasma state measurement/estimation results (step S114). If it is determined that the plasma state is not the required state (S114: NO), the control unit 2 adjusts control parameters such as source RF power and bias RF power (step S115), and returns the process to step S113.
  • control unit 2 purges the plasma processing chamber 10 to discharge excess second gas components (step S116).
  • the control unit 2 acquires sensing data output from the second sensor 108b during execution of ALD (actual experiment).
  • the control unit 2 measures/estimates the pattern shape of the object to be processed on which the protective film is formed by ALD, based on the sensing data of the second sensor 108b (step S117).
  • the shape measured/estimated in step S117 may be the shape of each recess formed on the surface of the object to be processed, or may be the uniformity of the overall shape of the recess on the surface of the object to be processed.
  • the control unit 2 determines whether the ideal shape has been obtained based on the measurement/estimation results of the pattern shape (step S118).
  • the control unit 2 measures/estimates the shape of the protective film formed by ALD based on the sensor data obtained from the second sensor 108b, and determines whether the protective film has the required thickness. Determine whether the shape has been obtained. If the ideal shape has not been obtained (S118: NO), the control unit 2 returns the process to step S110 and repeatedly executes ALD.
  • control unit 2 may compare the pattern shape measurement/estimation result with a set value set for the pattern shape, and may stop subsequent processing or output an alarm depending on the comparison result. You may.
  • the control unit 2 may acquire various output data and various measurement data output from the plasma processing apparatus 1 at any time during execution of ALD, and may repeatedly execute virtual ALD with reference to the acquired data.
  • the control unit 2 can derive parameters to be applied to the actual experiment from the virtual ALD and apply them to the ALD (actual experiment) repeatedly executed in the plasma processing apparatus 1.
  • step S119 the control unit 2 executes virtual etching (step S119).
  • the control unit 2 estimates the shape of the object to be processed after the etching process by simulation, using the pattern shape obtained by the virtual ALD in step S108 as the initial shape. Further, the control unit 2 determines whether or not the desired shape has been obtained as a result of the virtual etching, and if it is determined that the desired shape has not been obtained, the control unit 2 returns the process to step S104 or S108, and returns the process to the virtual experiment ( Virtual etching and virtual ALD) may be performed repeatedly.
  • the control unit 2 acquires various parameters used in the virtual etching, and derives parameters to be applied to the actual experiment based on the acquired parameters (step S120).
  • the parameters derived in virtual etching are similar to the parameters derived in step S104.
  • the control unit 2 performs an actual experiment by inputting the parameters acquired in step S120 into a learning model of machine learning including deep learning, machine learning, etc., a statistical model, or a model based on a combination thereof. It is possible to derive the parameters to be applied.
  • the control unit 2 also controls the control unit 2 to increase the matching rate between the shape measured/estimated in the actual experiment and the shape predicted in the virtual experiment, or to shorten the process processing time (throughput). Parameters to be applied to actual experiments may be optimized.
  • the plasma processing apparatus 1 acquires the parameters derived from the virtual experiment in step S119, and performs etching applying the acquired parameters (step S121). This etching process is a process used in actual experiments.
  • the control unit 2 acquires sensing data output from the second sensor 108b during execution of etching (actual experiment).
  • the control unit 2 measures/estimates the pattern shape of the pattern formed by the etching in step S121 based on the sensing data of the second sensor 108b (step S122).
  • the shape measured/estimated in step S122 may be the shape of each recess formed on the surface of the object to be processed, or may be the uniformity of the overall shape of the recess on the surface of the object to be processed.
  • the control unit 2 determines whether the ideal shape has been obtained based on the measurement/estimation results of the pattern shape (step S123).
  • the determination method in step S123 is the same as the determination method in step S107.
  • control unit 2 If it is determined that the ideal shape has not been obtained (S123: NO), the control unit 2 returns the process to step S105. If it is determined that the ideal shape has been obtained (S123: YES), the control unit 2 ends the process according to this flowchart.
  • control unit 2 may compare the pattern shape measurement/estimation result with a set value set for the pattern shape, and may stop subsequent processing or output an alarm depending on the comparison result. You may.
  • the configuration is such that etching and ALD are performed in one plasma processing apparatus 1, but a configuration may be adopted in which etching and ALD are performed using a plurality of process modules PM1 to PM6.
  • the control unit 2 When performing etching and ALD in one plasma processing apparatus 1, the control unit 2 only needs to measure/estimate the pattern shape and plasma state based on the output of the optical sensor 108 provided in the plasma processing apparatus 1 (in -situ).
  • the pattern shape is measured/estimated using the observation device OC (ex-situ), and the first sensor 108a provided in each process module PM1 to PM6 is used to measure and estimate the pattern shape.
  • the state may be measured/estimated.
  • the configuration is such that virtual etching and virtual ALD are executed in the control unit 2, but necessary information is exchanged between the control unit 2 and the control device MC, and the control device MC A configuration may be adopted in which virtual etching and virtual ALD are performed.
  • FIG. 8 is an explanatory diagram illustrating a configuration example of an etching simulator.
  • the etching simulator includes, for example, a plasma simulator SIM1, a shape simulator SIM2, and a reaction product simulator SIM3. These simulators SIM1 to SIM3 are simulators based on a particle model.
  • the plasma simulator SIM1 determines the spatial distribution of reactive species (ions, radicals, etc.) present in the plasma processing chamber 10 based on the process condition information, and further determines incident information such as the incident angle and incident energy of the reactive species.
  • the process condition information includes the type of reaction gas, gas flow rate, gas mixture ratio, gas pressure, source RF power, bias RF power, and the like.
  • the electric field distribution is determined from Poisson's equation
  • the spatial distribution of reactive species is calculated using the particle Monte Carlo method
  • the movement of the reactive species near the object to be processed is sampled, and the movement of the reactive species toward the object is calculated.
  • incident information such as incident angle and incident energy.
  • the particle Monte Carlo method charged particles in the plasma are represented by superparticles, and the behavior of the entire plasma is simulated by tracing the trajectories of thousands to hundreds of thousands of superparticles.
  • the shape simulator SIM2 calculates a local etching reaction amount and a macro etching reaction amount using information on the pattern shape on the surface of the object to be processed, in addition to the reactive species distribution amount and incident information determined by the plasma simulator SIM1. Note that the plasma simulator SIM1 appropriately updates the distribution amount and incident information of reactive species using the local etching reaction amount and macro etching reaction amount obtained by the shape simulator SIM2.
  • the reaction product simulator SIM3 uses the local etching reaction amount and macroscopic etching reaction amount obtained by the shape simulator SIM2 in addition to the distribution amount and incidence information of reactive species obtained by the plasma simulator SIM1. In addition to determining the amount of attached products, the amount of attached macroscopic reaction products is also determined, and the total amount of attached reaction products is determined.
  • the space defined by the pattern shape is divided into meshes, and reactive species and generated species are flown into this space using the Monte Carlo method so as to follow the incident angle obtained from the plasma simulator SIM1. Furthermore, when particles collide with a wall surface such as a mask, the settings are made so that they will react with a certain probability. When the amount of reactive species within the mesh exceeds a certain value, the material in that mesh portion is removed to cope with the phenomenon that the material disappears as etching progresses. Further, when the amount of generated species exceeds a certain value, a material (for example, a polymer, etc.) corresponding to the generated species is attached to the wall surface to support the deposition reaction. The control unit 2 repeatedly performs calculations using such an etching simulator to obtain the etched shape of the object to be processed.
  • FIG. 8 shows an example of the configuration of an etching simulator
  • the ALD simulator includes, for example, a plasma simulator, a shape simulator, and a reaction product simulator.
  • the plasma simulator calculates the distribution amount and incident information of reactive species
  • the shape simulator calculates local and macro deposition reaction amounts
  • the reaction product simulator calculates the deposition amounts of local and macro reaction products.
  • the control unit 2 may use the ALD simulator configured as described above to determine the shape of the protective film formed on the side wall of the recess.
  • FIG. 8 describes an etching simulator composed of a plasma simulator SIM1, a shape simulator SIM2, and a reaction product simulator SIM3, the configuration of the simulator is not limited to that shown in FIG. 8.
  • the control unit 2 can calculate the etching shape of the protective film using any simulator (model) that can virtually represent the process.
  • control unit 2 may update the simulator (model) used for the virtual experiment to match the result of the actual experiment.
  • FIG. 9 is a flowchart explaining the model update procedure.
  • the control unit 2 performs a real experiment and a virtual experiment according to the procedures shown in FIGS. 6 and 7, and obtains the results of the real experiment and the virtual experiment (steps S201 and S202).
  • the control unit 2 calculates the difference between the actual experiment result and the virtual experiment result (step S203), and determines whether the simulator (model) needs to be updated (step S204). If the calculated difference is greater than or equal to the set value, the control unit 2 determines to update the simulator (model) (S204: YES), and updates the simulator (model) (step S205). Specifically, the control unit 2 changes at least one of the parameters constituting the simulator from a pre-update value to a post-update value.
  • control unit 2 Using the updated simulator (model), the control unit 2 re-executes a virtual experiment including virtual etching and virtual ALD (step S206), and returns the process to step S202.
  • the control unit 2 updates the model as appropriate by repeating the processing from step S202 to step S206.
  • step S203 determines that updating is not necessary (S204: NO) and ends the process according to this flowchart.
  • FIG. 10 is an explanatory diagram illustrating the thickness of the protective film formed by the substrate processing method according to the embodiment.
  • FIG. 10(A) is a schematic diagram of the object to be processed used in the experiment.
  • the object to be processed includes an etching target film EL1 and a mask MA.
  • a recess having an opening OP is formed in the stack of the etching target film EL1 and the mask MA, and a protective film PF is formed on the side wall of the recess.
  • the opening dimension CD at an arbitrary position in the space surrounded by the recess side wall (protective film PF) was measured.
  • FIG. 10(B) is a graph showing the measurement results.
  • the vertical axis of the graph represents the depth of the recess
  • the horizontal axis of the graph represents the opening size at any position in the space surrounded by the side wall of the recess.
  • Reference Example 1 shows the results of an experiment in which only etching treatment was performed without ALD
  • Reference Example 2 shows the results of an experiment in which both etching treatment and ALD were performed.
  • the example shows experimental results when process conditions are derived by virtual etching and virtual ALD using the substrate processing method according to the present embodiment and applied to actual experiments.
  • the opening dimension CD decreases as the depth increases from a depth of 0.4 ⁇ m.
  • the maximum value of the aperture dimension CD in the range shown in the graph was 54.1 nm, and the minimum value was 46.1 nm, so the difference was 8.0 nm.
  • the opening dimension CD increases at a depth of 0.4 to 1.2 ⁇ m.
  • the maximum value of the aperture dimension CD in the range shown in the graph was 49.2 nm, and the minimum value was 42.2 nm, so the difference was 7.0 nm.
  • the protective film was formed with a substantially constant thickness regardless of the depth.
  • the maximum value of the aperture dimension CD in the range shown in the graph was 45.6 nm, and the minimum value was 40.0 nm, so the difference was 5.6 nm. That is, compared to Reference Example 1 and Reference Example 2, it was qualitatively shown that a favorable opening shape was obtained.
  • an actual experiment is performed by applying the process conditions derived through virtual experiments (virtual etching and virtual ALD), so that a good opening shape can be derived.
  • virtual experiments virtual etching and virtual ALD
  • the model based on the experimental results obtained in the actual experiment and the experimental results obtained in the virtual experiment, it is possible to reduce the number of trials in the actual experiment, and to achieve better results more efficiently.
  • the shape of the opening can be determined.
  • Embodiment 2 In Embodiment 2, a configuration for deriving parameters to be applied to actual experiments using a reinforcement learning method will be described.
  • the overall configuration of the substrate processing system and the device configuration of each device are the same as those in Embodiment 1, so their description will be omitted.
  • FIG. 11 is a schematic diagram of the reinforcement learning algorithm.
  • a reinforcement learning algorithm is an algorithm that deals with the problem of an agent placed in a certain environment observing the current state of an observation target and deciding what action to take.
  • DQN Deep Q-Network
  • the learning model in reinforcement learning calculates the value of the action value function (Q value) for each of the possible actions a1, a2, ..., an (n is an integer of 2 or more) when the current state s t of the observation target is input. is learned to output.
  • DQN is a method that approximates an action value function using a neural network and performs reinforcement learning.
  • a learning model MD is expressed using a neural network that approximates an action value function, and information regarding the value when selecting parameters to be applied to an actual experiment according to the current state of the object to be processed is described. Perform reinforcement learning to output .
  • the state s t input to the learning model MD is, for example, shape data measured/estimated in an actual experiment.
  • the learning model MD calculates the values of action value functions Q(s t , a1), Q for each of possible actions a1, a2, ..., an (n is an integer of 2 or more) for the input of the current state s t . (s t , a2), ..., Q(s t , an) are output.
  • the value of the action value function represents the expected value of profits obtained in the future when action a is selected in state s t and is also called the Q value. That is, the value of the action value function (Q value) does not represent a short-term reward, but represents value in a long-term sense.
  • action a corresponds to executing an actual experiment according to the selected parameters.
  • the agent refers to the Q value output for each action from the learning model MD and selects the action a t that has the highest Q value from among the actions a1, a2, . . . , an that can be taken in the state st .
  • the environment is updated by the selected action a t and the next state s t+1 is determined.
  • the agent is the control unit 2, and the environment is a simulator that performs a virtual experiment.
  • the agent obtains a reward r t+1 from the environment according to the next state s t+1 generated by selecting the action a t .
  • the reward r t+1 is, for example, the match rate between the shape of the recess measured/estimated by monitoring in an actual experiment and the shape of the recess predicted by a virtual experiment.
  • the reward r t+1 may be process processing time.
  • agents learn behaviors that maximize future rewards (profits). Specifically, the agent sequentially updates the learning model MD based on the following formula (1) using the state s t , the state s t+1 , and the reward r t+1 for the previous action a t .
  • ⁇ is a learning coefficient
  • ⁇ is a discount rate
  • r t+1 is a reward obtained as a result of action a t .
  • the learning coefficient ⁇ is a parameter that determines the speed of learning, and satisfies the relationship 0 ⁇ 1.
  • the discount rate ⁇ is a parameter indicating how much to discount the evaluation of the future state, and satisfies the relationship 0 ⁇ 1.
  • model parameters of the learning model MD are learned using error backpropagation or the like so that the second term on the right side of Equation (1) becomes zero. This means that when state s t transitions to state s t+1 due to action a t , the Q value of that action a t is changed to the value when the next state s t+1 is the state with the highest Q value. It means to get closer.
  • the agent repeatedly updates the learning model MD until a predetermined termination condition is met. By repeating the update, the learning model MD is trained to maximize the reward r t+1 .
  • the termination conditions are appropriately set, for example, when updating has been performed a predetermined number of times, when the shape of the recessed portion of the object to be processed approaches the target shape, when the object to be processed can no longer be cut.
  • the control unit 2 can use the learning model MD to derive parameters to be applied to the actual experiment. Specifically, the control unit 2 inputs the current state of the observation target s t (data of the shape measured/estimated in an actual experiment) into the trained learning model MD, and executes the calculation using the learning model MD. . As a result of the calculation by the learning model MD, a Q value is obtained for each of the possible actions a1, a2, . . . , an. The control unit 2 can derive parameters to be applied to the actual experiment by selecting the action with the highest Q value.
  • parameters to be applied to the actual experiment conducted in the plasma processing apparatus 1 can be derived using reinforcement learning. Note that in this embodiment, a configuration has been described in which parameters to be applied to an actual experiment in one plasma processing apparatus 1 are derived, but parameters derived for one plasma processing apparatus 1 can be applied to one or more other plasma processing apparatuses. Of course, the present invention may also be applied to a device.
  • the method for generating the learning model MD is not limited to Q learning, and includes, for example, TD learning (Temporal Difference Learning), policy gradient method (Policy gradients), Any reinforcement learning algorithm can be used, such as SARSA (State-Action-Reward-State-Action) or Actor-critic.
  • TD learning Temporal Difference Learning
  • Policy gradients Policy gradients
  • Any reinforcement learning algorithm can be used, such as SARSA (State-Action-Reward-State-Action) or Actor-critic.
  • Embodiment 3 In the first embodiment, the virtual experiment and parameter derivation are performed in the control unit 2 that controls the operation of the plasma processing apparatus 1, but the virtual experiment is performed in an external server device that is communicably connected to the control unit 2. and parameters may be derived.
  • Embodiment 3 a configuration in which virtual experiments and parameter derivation are performed in an external server device will be described.
  • FIG. 12 is a schematic diagram showing a first configuration example of a plasma processing system in Embodiment 3.
  • the plasma processing system in Embodiment 3 includes a plasma processing apparatus 1, a control section 2, and a server device 3.
  • the configurations of the plasma processing apparatus 1 and the control section 2 are the same as those in Embodiment 1, so a description thereof will be omitted.
  • the server device 3 is a computer that is communicably connected to the control unit 2 via the communication network NW, and includes a processing unit 3a, a storage unit 3b, a communication unit 3c, and the like.
  • the processing unit 3a includes a CPU, ROM, RAM, etc., and performs a virtual experiment that simulates a process (actual experiment) executed in the plasma processing apparatus 1, and derives parameters to be applied to the actual experiment.
  • the storage unit 3b includes a storage device such as an HDD or an SDD.
  • the storage unit 3b includes a simulator for virtual experiments that simulates processes (actual experiments) executed in the substrate processing system PS. Simulators for virtual experiments include plasma simulators, reaction product simulators, shape simulators, and the like.
  • the communication unit 3c includes a communication interface for communicating with the control unit 2 via the communication network NW.
  • the server device 3 acquires shape data measured/estimated by the control unit 2 when a process (actual experiment) is performed in the plasma processing device 1 via the communication network NW.
  • the server device 3 executes virtual etching or virtual ALD using the shape data acquired from the plasma processing device 1 as an initial value, and estimates the shape of the processed object by simulation.
  • the server device 3 acquires various parameters used in virtual etching and virtual ALD, and derives parameters to be applied to the actual experiment based on the acquired parameters.
  • the server device 3 performs the actual experiment so that the matching rate between the shape measured/estimated in the actual experiment and the shape predicted in the virtual experiment is high, or the process processing time (throughput) is shortened.
  • the parameters to be applied may be optimized.
  • the server device 3 may use a reinforcement learning method to derive parameters to be applied to the actual experiment.
  • the above processing executed by the server device 3 is similar to the virtual experiment procedure described in Embodiments 1 and 2, so detailed explanation thereof will be omitted.
  • the server device 3 transmits the derived parameters to the control unit 2 via the communication network NW.
  • the plasma processing apparatus 1 performs etching and ALD to which parameters received by the control unit 2 from the server device 3 are applied. These etching and ALD are treatments in actual experiments.
  • the above processing performed by the plasma processing apparatus 1 is similar to the actual experimental procedure described in Embodiments 1 and 2, so detailed explanation thereof will be omitted.
  • virtual experiments are conducted in the server device 3 communicably connected to the control unit 2, and parameters to be applied to the plasma processing apparatus 1 are derived. , can be fed back to the plasma processing apparatus 1.
  • FIG. 13 is a schematic diagram showing a second configuration example of the plasma processing system in Embodiment 3.
  • the plasma processing system in Embodiment 3 includes plasma processing apparatuses 1-1, 1-2, ..., 1-n, and a control section 2-. 1, 2-2,..., 2-n.
  • the control units 2-1, 2-2, ..., 2-n control the operations of the plasma processing apparatuses 1-1, 1-2, ..., 1-n, respectively, and are connected to the communication network NW.
  • the server device 3 acquires data obtained by the plasma processing device 1, performs virtual experiments (virtual etching and virtual ALD), and derives parameters to be applied to the plasma processing device 1. .
  • the server device 3 transmits the derived parameters to the control section 2 that controls the plasma processing apparatus 1, and also controls the control sections 2-1, 2 that control the plasma processing apparatuses 1-1, 1-2, ..., 1-n. -2,...,2-n.
  • the plasma processing apparatus 1 performs etching and ALD to which parameters received by the control unit 2 from the server device 3 are applied.
  • the plasma processing apparatuses 1-1, 1-2,..., 1-n perform etching or ALD processing using the parameters received from the server apparatus 3 by the control units 2-1, 2-2,..., 2-n. Execute.
  • parameters to be applied to the plasma processing apparatus 1 can be derived and fed back to the plasma processing apparatus 1, and the parameters to be applied to the plasma processing apparatus 1 can be fed back to the other plasma processing apparatuses 1-1, 1-2. , ..., 1-n can also be fed back.
  • the application is not limited to the capacitively coupled type, but can also be applied to Inductively Coupled Plasma (ICP), Radial Line Slot Antenna (RLSA), Electron Cyclotron Resonance Plasma (ECR). It is applicable to any type of plasma processing equipment such as Helicon Wave Plasma (HWP). Further, instead of ALD, CVD (Chemical Vapor Deposition) may be used.
  • ICP Inductively Coupled Plasma
  • RLSA Radial Line Slot Antenna
  • ECR Electron Cyclotron Resonance Plasma
  • HWP Helicon Wave Plasma
  • CVD Chemical Vapor Deposition
  • the substrate processing apparatus according to Supplementary Note 1 or 2, wherein the step of forming the protective film includes at least one selected from the group consisting of CVD (Chemical Vapor Deposition), ALD (Atomic Layer Deposition), and unsaturated ALD. control method.
  • CVD Chemical Vapor Deposition
  • ALD Atomic Layer Deposition
  • unsaturated ALD unsaturated ALD. control method.
  • the step of forming the protective film includes: supplying a gas containing a precursor to the object to be processed so that the precursor is adsorbed to at least a side wall of the recess; Supplementary Note 1 or 2 includes a step of supplying a reactive gas that reacts with the precursor to the object to be processed, and forming the protective film by a reaction between the reactive gas and the precursor adsorbed in the recess. A method of controlling the substrate processing apparatus described above.
  • Appendix 6 The method for controlling a substrate processing apparatus according to appendix 5, wherein the film forming conditions include at least one of a film forming method, number of cycles, time, gas type, dilution, temperature, and RF power.
  • Appendix 8 The method for controlling a substrate processing apparatus according to appendix 7, wherein the etching conditions include at least one of RF power, processing time, gas type, gas mixture ratio, and temperature.
  • the substrate processing apparatus includes: a) partially etching the object to be processed to form a recess in the object to be processed; b) forming a protective film on the side wall of the recess formed in the object to be processed; c) further etching the object to be processed on which the recess and the protective film are formed; d) repeating the steps b) and c);
  • the control method includes: i) acquiring first data indicating the shape of the object to be processed after any of the treatments a) to d); ii) performing a virtual experiment simulating the steps a) to d) on the object to be processed, and acquiring second data indicating the shape of the object after the virtual experiment; iii) a step of deriving a parameter to be applied to at least one of a) to d) based on the first data and the second data.
  • (Appendix 14) a) partially etching the object to be processed to form a recess in the object to be processed; b) forming a protective film on the side wall of the recess formed in the object to be processed; c) further etching the object to be processed on which the recess and the protective film are formed; d) repeating the steps b) and c); a substrate processing device that performs a monitoring unit that monitors the object to be processed obtained in at least one of a) to d); a simulation unit that performs a virtual experiment simulating the steps a) to d); a derivation unit that derives a parameter to be applied to at least one of a) to d) based on the monitoring result of the object to be processed and the result of the virtual experiment; and a control device that causes the substrate processing apparatus to execute at least one of a) to d).
  • the control device stops the process or outputs an alarm depending on a comparison result between the shape of the recess estimated by monitoring the object to be processed and a setting value set for the shape. 15.
  • the control device includes: learning to derive the parameters by a match rate between the shape of the recess estimated by monitoring the object to be processed and the shape of the recess predicted by the virtual experiment, or reinforcement learning using process processing time as a reward; generate a model, The substrate processing system according to appendix 14, wherein parameters derived using the learning model are applied to substrate processing in one or more substrate processing apparatuses including the substrate processing apparatus.
  • (Appendix 17) a) partially etching the object to be processed to form a recess in the object to be processed; b) forming a protective film on the side wall of the recess formed in the object to be processed; c) further etching the object to be processed on which the recess and the protective film are formed; d) a substrate processing apparatus configured to perform a process comprising repeating the steps b) and c); i) acquiring first data indicating the shape of the object to be processed after any of the treatments a) to d); ii) performing a virtual experiment simulating the steps a) to d) on the object to be processed, and acquiring second data indicating the shape of the object after the virtual experiment; iii) deriving a parameter to be applied to at least one of a) to d) based on the first data and the second data; , a control device, and a substrate processing system.
  • Plasma processing apparatus Control unit 2a Computer 2a1 Processing unit 2a2 Storage unit 2a3 Communication interface 10 Plasma processing chamber

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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005051210A (ja) * 2003-07-15 2005-02-24 Matsushita Electric Ind Co Ltd 面内分布データの圧縮法、面内分布の測定方法、面内分布の最適化方法、プロセス装置の管理方法及びプロセス管理方法
JP2008532324A (ja) * 2005-03-03 2008-08-14 アプライド マテリアルズ インコーポレイテッド 制御された処理結果分布を有するエッチング方法
JP2012186394A (ja) * 2011-03-07 2012-09-27 Mizuho Information & Research Institute Inc プラズマ加工形状シミュレーション装置及びプログラム
JP2018152552A (ja) * 2017-02-08 2018-09-27 ヴェリティー インストルメンツ，インコーポレイテッド 複雑な構造の測定システム及び方法
WO2020049974A1 (ja) * 2018-09-03 2020-03-12 株式会社Ｐｒｅｆｅｒｒｅｄ Ｎｅｔｗｏｒｋｓ 学習装置、推論装置、学習モデルの生成方法及び推論方法
JP2020053506A (ja) * 2018-09-26 2020-04-02 株式会社Ｋｏｋｕｓａｉ Ｅｌｅｃｔｒｉｃ 基板処理装置、半導体装置の製造方法および記録媒体。
JP2022526124A (ja) * 2019-03-18 2022-05-23 ラム リサーチ コーポレーション 高アスペクト比フィーチャのエッチング中の限界寸法制御および保護層の形成に使用される炭素ベース堆積物
JP2022079865A (ja) * 2020-11-17 2022-05-27 東京エレクトロン株式会社 基板処理方法および基板処理システム
WO2022132704A1 (en) * 2020-12-15 2022-06-23 Lam Research Corporation Machine-learning in multi-step semiconductor fabrication processes

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9972478B2 (en) 2016-09-16 2018-05-15 Lam Research Corporation Method and process of implementing machine learning in complex multivariate wafer processing equipment

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005051210A (ja) * 2003-07-15 2005-02-24 Matsushita Electric Ind Co Ltd 面内分布データの圧縮法、面内分布の測定方法、面内分布の最適化方法、プロセス装置の管理方法及びプロセス管理方法
JP2008532324A (ja) * 2005-03-03 2008-08-14 アプライド マテリアルズ インコーポレイテッド 制御された処理結果分布を有するエッチング方法
JP2012186394A (ja) * 2011-03-07 2012-09-27 Mizuho Information & Research Institute Inc プラズマ加工形状シミュレーション装置及びプログラム
JP2018152552A (ja) * 2017-02-08 2018-09-27 ヴェリティー インストルメンツ，インコーポレイテッド 複雑な構造の測定システム及び方法
WO2020049974A1 (ja) * 2018-09-03 2020-03-12 株式会社Ｐｒｅｆｅｒｒｅｄ Ｎｅｔｗｏｒｋｓ 学習装置、推論装置、学習モデルの生成方法及び推論方法
JP2020053506A (ja) * 2018-09-26 2020-04-02 株式会社Ｋｏｋｕｓａｉ Ｅｌｅｃｔｒｉｃ 基板処理装置、半導体装置の製造方法および記録媒体。
JP2022526124A (ja) * 2019-03-18 2022-05-23 ラム リサーチ コーポレーション 高アスペクト比フィーチャのエッチング中の限界寸法制御および保護層の形成に使用される炭素ベース堆積物
JP2022079865A (ja) * 2020-11-17 2022-05-27 東京エレクトロン株式会社 基板処理方法および基板処理システム
WO2022132704A1 (en) * 2020-12-15 2022-06-23 Lam Research Corporation Machine-learning in multi-step semiconductor fabrication processes

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