Patent Application
20230057145
Embodiments of the present disclosure pertain to the field of semiconductor processing and, in particular, a plasma chamber with rotating crossflow and uniformity tuning.
During a plasma etch, deposition or other treatment processes, a workpiece, such as a semiconductor wafer, is inserted to a sealed plasma reactor chamber, and gas is injected into the chamber over the wafer and then pumped from the chamber. Plasma chambers often comprise (1) a parallel plate capacitively coupled plasma (CCP) source where one electrode has the workpiece on its plasma-facing surface and the other electrode has an array of gas inlet holes (showerhead) in the plasma-facing surface or (2) an inductively coupled plasma (ICP) or microwave source with a radio-frequency (RF) window generally opposite and facing the workpiece, and an array of gas inlet holes in or near the window.
With the axisymmetric gas flow approach described above, pressure & concentration gradients cause center-to-edge processing differences on the workpiece. In addition, extraneous plasma may form in gas inlet holes due to proximity to dense plasma or breakdown due to high electric fields, leading to non-uniformity changing overtime. More specifically, the gas inlet holes are typically formed in a plate of material, such as silicon or silicon carbide. Energetic ions bombarding the edges of the holes can deform or facet the holes overtime. The deformed holes, in turn, result in higher intensity plasma that disrupts the plate, requiring a change in showerheads after some number of hours (e.g., 600 hrs.). In some applications, approximately $15 of a semiconductor wafer cost may be allocated just to the costs of the showerheads.
Embodiments disclosed herein include a plasma treatment chamber, comprising one or more sidewalls. A support surface within the one or more sidewalls holds a workpiece. An array of individual gas injectors is distributed about a periphery of the one or more sidewalls. One or more pump ports is along the one or more sidewalls to eject gas from the plasma treatment chamber. A controller is configured to control the plasma treatment chamber during an etch application. An etch rate uniformity of a material on the workpiece is tuned or controlled by: i) using the array of individual gas injectors to inject one or more gas flows in a direction generally parallel to and across a surface of the workpiece; ii) injecting a first gas flow from a first set of adjacent ones of the individual gas injectors to etch the materials on the workpiece; and iii) simultaneously injecting a second gas flow from at least a remaining set of the individual gas injectors. According to embodiments, the second gas flow is used to i) dilute the first gas flow to reduce an area on the workpiece having a faster etch rate; or ii) act as an additional etchant to increase the etch rate in the area of the workpiece having the faster etch rate.
Embodiments disclosed herein further include a plasma treatment chamber, comprising one or more sidewalls. A support surface within the one or more sidewalls holds a workpiece. An array of individual gas injectors is distributed about a periphery of the one or more sidewalls. One or more pump ports is along the one or more sidewalls to eject gas from the plasma treatment chamber. A controller is configured to control the plasma treatment chamber during an etch application. An etch rate uniformity of a material on the workpiece is tuned or controlled by: i) using the array of individual gas injectors to inject a gas flow in a direction generally parallel to and across a surface of the workpiece; and ii) prior to or during gas flow injection, changing the gas flow injection angle across the workpiece by selecting between a wide set of adjacent ones of the individual gas injectors and a narrow set of adjacent ones of the individual gas injectors, wherein selecting the narrow set of adjacent ones of the individual gas injectors decreases the gas injection angle.
Embodiments disclosed herein include a method of controlling etch rate uniformity of a material on the workpiece in a plasma treatment chamber. The method comprises injecting, from a first set of adjacent ones of the individual gas injectors, a first gas flow in a direction generally parallel to and across a surface of the workpiece to etch materials on the workpiece. Simultaneously injecting, from at least a portion of a remaining set of the individual gas injectors, a second gas flow in a direction generally parallel to and across a surface of the workpiece, wherein the second gas flow is used to i) dilute the first gas flow to reduce an area on the workpiece having a faster etch rate; or ii) act as an additional etchant to increase the etch rate in the area of the workpiece having the faster etch rate.
Referring now to
The disclosed embodiments relate to a plasma chamber having a rotating modulated crossflow and uniformity tuning. In the following description, numerous specific details are set forth, in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without these specific details. In other instances, well-known aspects, such as integrated circuit fabrication, are not described in detail in order to not unnecessarily obscure embodiments of the present disclosure. Furthermore, it is to be understood that the various embodiments shown in the Figures are illustrative representations and are not necessarily drawn to scale.
Traditional plasma chambers (i.e., CCP or ICP) typically inject gas axisymmetrically over a workpiece from gas inlet holes that are typically located directly above the workpiece or symmetrically around its periphery. As noted above, axisymmetric gas flow can result in pressure and concentration gradients and the gas hole inlets may breakdown, creating non-uniformities in the workpiece. That is, as wear occurs in gas holes in the dense, high |E| plasma regions, geometry of the holes change and as plasma penetrates, the holes may modify the local plasma characteristics in the vicinity of the holes. In addition, the local gas flow rate and velocity may change as a result of geometric changes. Therefore, the showerheads need to be replaced relatively often, increasing cost of the workpiece.
Accordingly, embodiments disclosed herein are directed to a plasma chamber (e.g., CCP or ICP) with a multiphase rotating modulated gas crossflow for etching, deposition or other materials treatment. The plasma treatment chamber includes two or more gas injectors and two or more pump ports along a sidewall. In a first phase, one of the gas injectors forces a gas flow in one direction generally parallel and across a surface of a workpiece or device, where the gas is then pumped out via a pump port. In a second phase, gas flow is rotated by using another gas injector to force the gas flow in a different direction generally parallel and across the surface of the workpiece, where the gas is then pumped out via another pump port. In another embodiment, gas inlet valves coupled to the gas injector and/or throttle valves coupled to the pump ports can be used to modulate the rotating gas flows.
The plasma treatment chamber with rotating modulated gas crossflow eliminates the need for showerheads (and gas inlet holes) in the dense, high |E| plasma regions, and therefore prevents the source of plasma non-uniformity. The disclosed embodiments prevent plasma from forming in gas holes due to proximity to dense plasma or breakdown due to high electric fields, leading to non-uniformity and plasma characteristics changing over time change. The disclosed embodiments avoid high center-to-edge pressure and concentration gradients that cause center-to-edge processing differences. Pressure distribution can be tailored across the plasma volume to minimize plasma non-uniformity. In addition, the disclosed embodiments eliminate stagnant low-gas velocity regions (i.e., center of the workpiece) for uniform reactant and byproduct removal.
Referring to both
In one embodiment shown, the one or more sidewalls 112 surround a processing region 110 in which the workpiece 116 (e.g., wafer or substrate) is treated. In the example shown, the plasma treatment chamber 100A is shown with an axially symmetrical shape (e.g., a cylindrical) resulting in a single cylindrical sidewall 112. However, in other embodiments, the plasma treatment chamber 100A may have any other shape, such as an oval, which also results in a single sidewall 112, or as a square or rectangle, in which case the plasma treatment chamber 100A would have four sidewalls.
According to the disclosed embodiments, the plasma treatment chamber 100 includes at least two gas injectors 118A and 118B (collectively referred to as gas injectors 118) and at least two pump ports 120A and 120B (collectively referred to as pump ports 120) located generally along the sidewall(s) 112. In one embodiment, the gas injectors are formed in the openings through a liner of the sidewall 112. The plasma treatment chamber 100A may be configured to use the gas injectors 118 and the pump ports 120 to rotate gas flows 124 laterally across the workpiece 116 to provide a multiphase rotating crossflow operation. In one embodiment, the multiphase rotating crossflow operation comprises at least a 2-phase cycle, and may comprise a 3-phase cycle, a 4-phase cycle, and so on, where in each phase gas is injected from one side of plasma treatment chamber 100A and pumped out generally from an opposite side. As used herein, the phrase “located generally along the sidewall(s)” is intended to describe that any of the gas injectors 118 and/or pump ports 120 may be located in a sidewall or horizontally abutting or adjacent to the sidewall, or located in an outer periphery region of the chamber lid or an outer periphery region of the chamber bottom.
Rotation of gas flow laterally across the workpiece 116 may result in improved control of gas velocity and pressure gradients leading to better process uniformity across a wafer and from wafer-to-wafer.
Referring to
In an embodiment, the workpiece 116 may comprise any substrate that is commonly used in semiconductor manufacturing environments. For example, the workpiece may comprise a semiconductor wafer. In an embodiment, semiconductor materials may comprise, but are not limited to, silicon or III-V semiconductor materials. The semiconductor wafer may be a semiconductor-on-insulator (SOI) substrate in some embodiments. Typically, semiconductor wafers have standard dimensions, (e.g., 200 mm, 300 mm, 450 mm, or the like). However it is to be appreciated that the workpiece 116 may have any dimension. Embodiments may also include workpieces that comprise non-semiconductor materials, such as glass or ceramic materials. In an embodiment, the workpiece 116 may comprise circuitry or other structures manufactured using semiconductor processing equipment. In yet another embodiment, the workpiece 116 may comprise a reticle or other lithography mask object.
Thus, gas injector 118A and the opposing pump port 120A form one gas injector-pump port pair, while gas injector 118B and opposing pump port 120B form a second gas injector-pump port pair. In one embodiment, each of the gas injectors 118A and 118B may comprise an array of individual gas injectors, as shown in
As shown in
While in some embodiments, the number of gas injectors 118 and pump ports 120 is equal, in other embodiments, the number of gas injectors 118 and pump ports 120 may differ. In some embodiments, a single pump port is associated with a corresponding gas injector, as depicted. In other embodiments, an array of pump ports is associated with a corresponding gas injector.
As shown in
Along the vertical plane, which is generally parallel to the orientation of the support pedestal 108, locations of the pump ports 120 may be vertically offset from locations of the gas injectors 118 by a distance approximately equal to the distance between a bottom of the chamber lid 104 and a top of the support pedestal 108 in one embodiment. In this embodiment, the pump ports 120 may be located in cavities between the sidewall 112 and the support pedestal 108, and above the chamber floor 106. In another embodiment, the pump ports 120 may be located in additional openings in the sidewall 112 anywhere between the chamber lid 104 and the chamber floor 106. In another embodiment, gas can be injected from peripheral regions of the chamber lid, and/or pumped from peripheral regions of the chamber bottom, and over the workpiece processing region and still flow substantially parallel to the workpiece.
As described above, the plasma treatment chamber 100A of the disclosed embodiments injects gas generally parallel and across the workpiece 116. This is in contrast to a typical axisymmetric top-down gas flow injection from a “showerhead” electrode in a CCP source reactor, and in contrast to a radial outward/downward gas injection from a nozzle array near a central axis in an ICP or microwave source reactor. In addition, instead of a pump port or pumping plenum located axisymmetrically around the periphery of the workpiece, in embodiments, gas is preferentially pumped out from a side of a workpiece generally opposite the injection side.
In embodiments, the gas flow 124 of each cross flow phase can be switched on and off to control gas flow rotation. In another embodiment, instead of switching the gas flow 124 on and off, a modulating function may be applied to a flow rate of the gas flows 124 from the gas injectors 118 and/or to an outlet conductance (or pressure) caused by the pump ports 120 to either approximate open/closed states or to ramp between states using a modulating function, such as sinusoidal. As shown in
In addition, in some embodiments one or more of the pump ports 120 may be modulated. For example, pump port conductance (pressure) may be modulated using individual pressure control valves 127A and 127B on pump ports 120A and 120B. Also shown is that the pump ports 120A and 120B are coupled to one or more pumps 132 to evacuate the gas. In the example shown, pressure control valve 127A in pump port 120A is in the closed position, while pressure control valve 127B is shown in the open position to expel the first gas flow 124A. The pressure control valves 127A and 127B may be operated smoothly between two states of conductance or pressure, which are then cycled through in a like sequence as the gas injectors 118A and 118B. In one embodiment, pressure control valves 127A and 127B comprise throttle valves.
The plasma chamber 100A may inject a variety of types of process gases. Exemplary process gases may include the following: i) dielectric etch gases including one or more of C4F8, C4F6, C3F6, CH2F2, C3H2F4; ii) deposition gases including one or more of CH4, C2H2; iii) additional gases for co-flow for either etch or deposition including one or more of Ar, N2, O2, He, Kr, Xe, COS; iv) semiconductor material etch deposition gases including one or more of SiCl4, SiCH2Cl2; v) hydride-based deposition gases including one or more of BH3, AlH3, GaH3, NH3; vi) oxide material etch deposition gases including one or more of SiCl4, SiCH2Cl2, and O2; and vii) annealing gases including one or more of NH3, N2, Ar.
In some embodiments, the plasma treatment chamber 100A may further include sensors 131 and systems to monitor process chamber conditions including gas flow, velocity, pressure, temperature and the like, with high sensitivities and real time measurement. Particular embodiments can include capacitive wall sensors, on-chip or off-chip thermal sensors, pressure sensors, and/or integrated sensors (capacitive sensors and thermal sensors) on substrates such as ceramic substrate or glass or silicon or flexible substrates. In some embodiments, the sensors can be distributed throughout the chamber to monitor the chamber conditions at various locations, which then can be correlated to overall process performances such as etch rate, etch non-uniformity, particle generation, process drifting, pressure uniformity, etc. In one embodiment, a plurality or an array of pressure sensors can be distributed throughout the chamber to provide data regarding gas flow (e.g., rotation rates, uniformity, velocity) during processing.
The multiphase architecture of the plasma treatment chamber enables many different configuration options. For example,
Referring to
In this embodiment, the gas injectors 218 each comprise as a single vent in the sidewall 212, as shown. In one embodiment, the gas injectors 218 are symmetrically arranged about the central axis of the plasma treatment chamber 200, and the pump ports 220 are symmetrically arranged about the central axis of the plasma treatment chamber 200, as shown. In the 3-phase rotating cross flow embodiment comprising three injector-pump port pairs, the injector-pump port pairs are offset from one another by 120° (360°/3). More specifically, the gas injectors 218 are located approximately 120° from one another, and the pump ports 220 are located 120° from one another. The pump ports 220 dispersed laterally between the spaced-apart gas injectors 218 as well as vertically offset from the gas injectors 218.
A controller may be coupled to the plasma treatment chamber 200 and configured to control the gas inlet valves 122A-122C and pressure control valves 127A-127C. The controller starts the first phase by fully opening GV1 to 100 percent, and partially opening GV2 and GV3, for example, at approximately 2-5%. During the first phase, PV1 is opened while PV2 and PV3 are closed, and chamber pressure is between 1 mT and 500 mT.
GV1 begins closing near a transition between the first phase and the second phase, and the direction of the gas flow is rotated by fully opening GV2 to 100 percent to begin the second phase. GV1 and GV3 are partially open at approximately 2-5%. During the second phase, the controller opens PV2 and keeps PV1 and PV3 closed. Chamber pressure may remain between 1 mT and 500 mT in some embodiments, or between 10 mT and 200 mT in other embodiments.
Near a transition between the second phase and the third phase, GV2 is ramped down, and the direction of the gas flow is rotated by opening GV3 to 100 percent to begin the third phase. GV1 and GV2 are partially open at approximately 2-5%. During the third phase, the controller opens PV3 and keeps PV1 and PV2 closed. This completes the 3-phase cycle, which may be repeated as necessary. As shown, a relatively constant chamber pressure is maintained during the three gas flow phases. In an embodiment, opening and closing GV1, GV2 and GV3 sequentially effectively creates a rotational gas flow, which may mimic rotation of a wafer. In one embodiment, a single full rotation of the gas flow is performed at a rate approximately in a range of 100 ms to 10 sec.
Many different variations between the gas flow phases and cycles may occur. That is each parameter controlling operation of the plasma treatment chamber may vary across phases and cycles. For example, the time to complete a full cycle may be the same or different across different cycles. The time to complete a phase may be the same or different within a cycle, and may be the same or different across different cycles. The direction of gas flow rotation (e.g., clockwise, counterclockwise) may be the same or different within phases of a cycle, may be non-sequential, or may be the same or different across cycles. The velocity of the gas flows may be the same or different within phases of a cycle, or may be the same or different across cycles. The % open of the gas valves and the time the gas valves are open may be the same or different within phases of a cycle, or may be the same or different across cycles. The % open of the pressure control valves and the time the pressure control valves are open may be the same or different within phases of a cycle, or may be the same or different across cycles. For example, in an embodiment, rotation is performed for a first portion of a process at one rate, and is then slowed to a second rate for a second portion of the process. In an embodiment, rotation is performed for a first portion of a process at one rate, and is then sped up to a second rate for a second portion of the process. In an embodiment, rotation is fast for a first portion of a single rotation cycle, and slows for a second portion of the rotation. In an embodiment, rotation is slow for a first portion of a single rotation cycle, and is sped up for a second portion of the rotation. By varying rotation speed within a single cycle, or cycle to cycle, process non-uniformities may be compensated for. In other embodiments, direction is changed between clockwise and counter-clockwise within a cycle, cycle-to-cycle, or between sets of cycles. Likewise, in embodiments, gas flow rates between a first phase, a second phase, and a third phase can be varied within a cycle, cycle-to-cycle, or between sets of cycles.
In embodiments, the gas inlet valves 122 may comprise analog variable conductance fast gas valves that allow fast response without excessive pressure spikes that lead to gas light up or arcing or make it difficult for RF match control to follow. Specific examples of the gas inlet valves include commercially available Swagelok eDE Valves and Fujikin Piezo Valves. The Swagelok eDE Valves may have 15-20 msec open/close times, are good for sealing atm/vacuum, and have a lifespan of 40M cycles. The Fujikin Piezo Valves have a proportional flow, a 10 msec open/close time and may have a lifespan much greater than 40M cycles depending on use. Both may provide gas flows up to 2.5 slm @400 T upstream pressure.
Actuators 277 are coupled to the pressure control valves 127 to control each of the pump ports 120.
Multiphase Rotating Crossflow for Etch Rate Uniformity Tuning
The use of rotating modulated crossflows can permit process uniformity control from the peripheral boundaries of the chamber. This tuning capability, which uses gas injection and/or pumping at peripheral boundaries and outside of the dense plasma region as control inputs, without introducing geometric discontinuities (i.e., gas injection holes), allows formation of a uniform plasma with minimal drift or change over time due to etching, wear, or coating of exposed plasma facing surfaces, namely electrode/showerheads with gas holes or gas nozzles.
Etch rate uniformity tuning of the disclosed embodiments includes two aspects. The first aspect of etch rate uniformity tuning includes varying gas flow injection angles across the workpiece, as shown in
The second aspect of etch rate uniformity tuning, which can be used alone or in combination with the first aspect, includes simultaneously injecting a process gas mixture and an independent gas injection (IGI) mixture across the workpiece where the IGI mixture is used as a gas curtain to either increase or decrease an area of the workpiece having a faster etch rate, as shown in
Referring to
In this embodiment, the individual gas injectors in each of the gas injector arrays 318 can be switched on/off or have a modulated flow rate controlled by the gas inlet valves 122 (
In the example of
Increasing or decreasing the gas flow injection angle from one phase to the next may provide the phases with different flow rates and alters etch rate uniformity on the workpiece. Such non-uniform gas injection angles of the disclosed embodiments may be deliberately changed and controlled over time to control workpiece process uniformity. As an example, decreasing the gas injection angle may increase etch rate uniformity. In an embodiment, during one phase or cycle, between phases or cycles, or between sets of cycles, the gas flow injection angle of one or more of the gas injectors may be varied.
Referring to
Each crossflow phase may inject a gas flow that comprises a process gas mixture 324A or 324B, such as an etchant gas, and an IGI mixture 326A or 326B. According to the disclosed embodiments, the IGI mixture 326A is used as a gas curtain that acts either as a dilution booster to the process gas mixture 324A, and the IGI mixture 326B is used as a gas curtain that acts as an etchant concentration ratio booster to the process gas mixture 324B.
In embodiments, the process gas mixture 324A and 324B may comprise an etchant gas or deposition gas. Examples of etchant gases may include CXFY (such as C3F6, C4F6, C4F8, C5F8, etc.), CXHYFZ (such as CHF3, CH2F2, C3H2F4, etc.), a Fluorine rich gas (such as NF3, SF6, etc.), and CXHY (such as CH4, C2H2, etc.).
The process gas mixture 324A and 324B or the IGI mixture 326A and 326B may comprise a diluent, inert or a cleaning gas. Examples of a diluent gas may include He, Ne, Ar, Kr, Rn, N, and Xe, and examples of a cleaning gas may include OX, N2, SFx, NFx and the like. As is well-known in the art of plasma etching, in general, higher carbon-to-fluorine ratios and higher hydrogen-to-fluorine ratios tend to increase probability of deposition of surfaces, while lower ratios increase probability of etching of surfaces. That is, high carbon-to-fluorine ratio (e.g., 1/1) or a high hydrogen-to-fluorine (e.g., 3/1) ratio gas, such as CH3F, commonly behave more as a depositing gas, while a lower carbon-to-fluorine ratio (e.g., 1/3) or a lower hydrogen-to-fluorine (e.g., 1/3) ratio gas, such as CHF3, behave more as an etching gas. Dielectric etch/deposition gases can behave as etchant gases or deposition (polymerizing) gases depending on plasma (other gases present and their concentrations, electron density and electron energy distribution in bulk plasma, ion energy distribution at surfaces) and surface conditions (temperature and material composition).
The advantages of these aspects of the disclosed embodiments include: (1) the crossflow gas flow has a horizontal velocity that is at least 2× faster along edge of the workpiece, and at most 5× faster on center than through use of a showerhead. (2) The crossflow design can maintain a Peclet number greater than 1 anytime everywhere on the workpiece. This means advective transportation can minimize re-dissociation under the plasma. (3) Overall density of fluorocarbons and etch byproducts is more uniform in the crossflow operation, thus improving plasma (sheath) uniformity.
Reactive Ion Etching
As an example application, the plasma treatment chamber may be used to perform precise reactive ion etching during semiconductor manufacturing.
The mask layer 408 may define the pattern of an integrated circuit, with a pattern to guide deposition or removal of material from the wafer in subsequent patterning steps. In this example, reactive ion etching is performed by the plasma treatment chamber to remove the material between some of the openings in the mask layer 408 to form openings 410 through the ILD layers 406 and the alternating layer stack 404 to the substrate 402, where the intersections of the openings 410 and the metal layers 404A may eventually form a memory cell. The gas flows injected by plasma treatment chamber (as described above) can be customized to control both etch depth uniformity as well as aspect ratio (depth-to-width) uniformity of the openings 410. In one embodiment, one or more the openings 410 may be etched to have a first aspect ratio through the ILD layers 406 and a second aspect ratio through the alternating layer stack 404. In embodiments, one or more of the openings 410 may have a varying aspect ratio, referred to as bowing, through the alternating layer stack 404, as shown. In one embodiment, the openings 410 may be etched to have high aspect ratios greater than 8-1, 9-1 or 10-1. In embodiments, one or more the openings 410 may also have varying etch depth.
In embodiments, 3D-NAND ion etch applications may include a pillar etch as described above, a slit etch, a peri contact etch, a staircase contact etch, a cell contact-1 etch, and a cell contact-1 etch. In embodiments, aspect ratios, etch depths and bowing characteristics may be parameters that are monitored by a machine learning model, as described below.
Use of a Machine Learning (ML) Model to Control a Plasma Treatment Chamber Having a Multiphase Rotating Crossflow
Configuring the plasma treatment chamber described above to provide a desired outcome on a workpiece (e.g., wafer) requires a process recipe that comprises a complex combination of many different processing parameters (i.e., knobs) that can be individually controlled. Examples include total gas flow mixture, gas pressure (mTorr), gas flow ramp open times (msec), gas flow time (msec), gas flow ramp closed times (msec) and the like.
In order to develop a process recipe for high volume manufacturing (HVM) process engineers rely on their experience and expertise to identify a baseline recipe that may provide a rough approximation of the desired outcome on the wafer. A design of experiment (DoE) that relies on the processing of a set of wafers (or coupons) in order to identify how the knobs interact is then generated around the baseline recipe. The DoE results may be interpreted by the process engineer to further refine the baseline recipe. Additional DoEs may also be executed in order to converge on the desired outcome on the wafer. Such an iterative process is time and resource intensive.
Additionally, once the final processing recipe has been developed, chamber drift during many iterations of the process for different wafers may result in changes to the outcome on the wafer. Chamber drift may be the result of erosion of consumable portions of the chamber, degradation of components (e.g., sensors, lamps, etc.), deposition of byproduct films over surfaces, or the like. Accordingly, additional tuning is needed even after the extensive recipe development process.
Consequently, recipe development and chamber baselining are time and resource intensive. Particularly, the process space available to tune and optimize a given process is extremely large, and it is practically impossible to explore the entire process space empirically within any reasonable timeframe. Furthermore, due to the interaction between processing parameters and their impact on the process performance, it is extremely hard to predict the combined effect of simultaneous variation of multiple processing parameters by manually scanning one processing parameter at a time.
A second aspect of the disclosed embodiments comprises a semiconductor manufacturing tool utilizing one or more machine learning (ML) models to control the plasma treatment chamber having a multiphase rotating crossflow. The ML model may be used for developing process recipes and/or processing a device or workpiece. The ML model may connect input processing parameters to device outputs.
In an embodiment a method of controlling processing comprises querying the ML models to control timing of the gas flow rotation. In an embodiment, a method for developing a semiconductor manufacturing process recipe comprises selecting one or more device outcomes, and querying the ML model to obtain a process recipe recommendation suitable for obtaining the device outcomes when processed by the plasma treatment chamber having a multiphase rotating crossflow. This may be referred to as feed forward process adjustment. In an embodiment, the method may further comprise executing a design of experiment (DoE) on a set of wafers to validate the process recipe recommended by the ML model. Measurements of the DoE may be taken and used to change the process recipe for future wafers, for feedback process adjustments.
Additionally, the ML model may be updated during processing of wafers in a chamber as on-tool performance becomes available and then update a process recommendation or actively change the recipe. This may be referred to as “on the fly” or real-time process adjustments.
Recipe changes may include modifying the recipe within a step, e.g., increasing the rotation frequency of the gas flows when etching the top of the wafer and lowering the rotation frequency as it reaches lower, or vice versa. Another example is the updated machine learning model modifying inputs parameters within a single rotation, such as making the etch depth slightly different at the beginning and the end of a gas flow rotation when processing of the stacked memory device of
Accordingly, embodiments disclosed herein leverage the use of a ML model to query an entire process space without the need to process physical wafers in a large design of experiment (DoE). Therefore, time and resources dedicated to recipe development can be significantly reduced.
The ML model may be a model of a process space generated from the combination of a statistical model and a physical model. As used herein, a “process space” may refer to a multi-dimensional process space that maps processing parameters to one or more device outcomes on the wafer. The processing parameters, sometime called knobs, are variables that can be controlled to control a process. For example, knobs or processing parameters may include, but are not limited to, any combination of: temperature, RF source power, bias power, gas pressure (mTorr), gas flow ramp open times (msec), gas flow time (msec), gas flow ramp closed time (msec), gas flow fraction at various gas injectors, gas composition at various injectors, gas flow fraction going to various injectors, gas flow rotation frequency, gas flow composition frequency, gas flow rate/velocity (pressure gradient), gas flow direction, gas rotation phase, electron/plasma density, plasma density gradient, electron temperature, ion current density, plasma potential, sheath electric field, potential, sheath electric field tilt angle, sheath electric field z-component, mass fractions, fluxes, and ion current density to workpiece.
The device outcomes may refer to measurable properties of features on a wafer after processing. For example, the selected device outcomes may comprise any combination of: a feature profile, a layer thickness, a thickness uniformity, a material composition of a layer, a composition uniformity, a porosity, a film stress, process uniformity across chambers in a facility (e.g., chamber matching), wafer to wafer uniformity, uniformity between different wafer lots, and the like. During an etch processes, the selected device outcomes may further include any combination of: etch rate, etch or uniformity center-to-edge, etch rate uniformity azimuthal, etch feature uniformity (generally described by top v. bottom critical dimension (CD)), tilt, bow, and mask remaining, and the like. That is, device outcomes are not limited to an outcome on a single wafer. Each point in the process space may be a representation of a set of processing parameter values and the resulting device outcome (or outcomes) produced by the set of processing parameters.
In an embodiment, the statistical model of the ML model may be built using a DoE of actual wafers to populate a portion of the process space. Algorithms may then be used to extrapolate the remainder of the process space. The physical model is based on real world physical and chemical interactions that occur within the processing chamber. A simulation of the physical and chemical interactions in the processing chamber over a range of different processing parameters may be used to generate the physical model. In an embodiment, the physical model is merged with the statistical model to provide the ML model. For example, the physical model may be used to fill any gaps in the statistical model and/or to verify extrapolated data points.
Referring now to
In an embodiment, the ML model server 620 may include a statistical model 625 and a physical model 627. The statistical model 625 and the physical model 627 may be communicatively coupled to a database 630 for storing input data (e.g., sensor data, model data, metrology data, etc.) used to build and/or update the statistical model 625 and the physical model 627.
In an embodiment, the statistical model 625 may be generated from a physical DoE and use interpolation to provide an expanded process space model. The physical wafers that are processed may be used to provide a mapping of processing parameters to specific device outcomes. The physical DoE may also be used to identify interactions between different processing parameters. After the data (e.g., metrology data, sensor data, process parameter data, etc.) for the physical wafers is provided, interpolation is used to fill gaps in the process space. In an embodiment, data, such as metrology data, may be obtained using an external tool that is communicatively coupled to the ML model server 620 by a data link (e.g., a wired or wireless data link). The interpolation may be done using any suitable algorithm or algorithms. Algorithms may include, but are not limited to a neural network, deep learning or any other known techniques used for regression analysis (e.g., linear, partial least squares, Gaussian, polynomials, convolution neural networks for regression, regression trees and others).
In an embodiment, the statistical model 625 may be provided as a module that is sold or licensed for use in conjunction with the processing tool. That is, a physical DoE for the statistical model 625 may be executed by the manufacturer of the processing tool. In other embodiments, the statistical model 625 may be generated by executing the physical DoE on-site. In yet another embodiment, a generic statistical model 625 may be provided by the tool manufacturer and a subsequent physical DoE may be executed on-site to provide a calibration of the statistical model 625 to more closely model the particular processing tool being investigated.
In an embodiment, the physical model 627 may be generated using real world physics and chemistry relationships. For example, physics and chemistry equations for various interactions within a processing chamber may be used to build the physical model. The physical model 627 may also utilize chamber geometries or other chamber configurations to improve the accuracy of the physical model 627. The physical model 627 may be the result of a simulation of the physical and chemical interactions within a processing tool across a plurality of different processing parameters. The physical model 627 may be a module that is sold or licensed for use in conjunction with the processing tool.
In an embodiment, the physical model 627 and the statistical model 625 may be able to reference each other (as indicated by the arrow). Cross-referencing between the two models 627 and 625 allows for validation of each of the models and for filling in any gaps in the individual models. In an embodiment, the physical model 627 and the statistical model 625 may be combined to provide a more robust ML model.
As shown, the ML model server 620 may be integrated with the processing tool 600. For example, the ML model server 620 may be communicatively coupled to a front end server 660 by a network connection, as indicated by the arrow. However, in other embodiments, the ML model server 620 may be external to the processing tool 600. For example, ML model server 620 may be communicatively coupled to the processing tool 600 through an external network or the like.
In an embodiment, the front end server 660 may comprise a user interface 665 for the ML model server 620. The user interface 665 provides an interface for a process engineer to utilize the ML modeling in order to execute various operations, such as recipe development or chamber baselining, as will be described in greater detail below. In one embodiment, the user interface 665 may correspond to user interface 142 of
The control server 650 may comprise a smart monitoring and control block 655. The smart monitoring and control block 655 may comprise modules for providing diagnostics and other monitoring of the processing tool 600. Modules may include, but are not limited to health checks, sensor drift, fault recovery, and leak detection. The smart monitoring and control block 655 may receive data from various sensors implemented in the tool hardware 640 as inputs. The sensors may include standard sensors 647 that are generally present in semiconductor manufacturing tools 600 to allow for operation of the tool 600. The sensors may also include modelling sensors 645 that are added into the tool 600. The modelling sensors 645 provide additional information that is necessary for the building of highly detailed ML models. For example, the modelling sensors may include virtual sensors and/or witness sensors. Virtual sensors may utilize the data obtained from two or more physical sensors and implement interpolation and/or extrapolation in order to provide additional sensor data not obtainable from physical sensors alone. In a particular example, a virtual sensor may utilize an upstream pressure sensor and a downstream pressure sensor in order to calculate a flow rate through a portion of the processing tool, such as a gas cartridge. Generally, modelling sensors may include any type of sensor, such as, but not limited to, pressure sensors, temperature sensors, and gas concentration sensors. In an embodiment, the smart monitoring and control block 655 may provide data that is used by the ML model server 620. In other embodiments, output data from the various modelling sensors 645 may be provided directly to the ML model server 620. In one embodiment, the control server 650 may correspond to controller 140 of
Referring now to
In an embodiment, the statistical model engine 724 may be implemented as hardware and/or software suitable for analyzing the various data sources and outputting a statistical model 725. The statistical model engine 724 may utilize machine learning based on neural networks, or any other known techniques used for regression analysis (e.g., linear, partial least squares, Gaussian, polynomials, convolution neural networks for regression, regression tress, and others) in order to interpolate a larger process space than is available from the physical DoE data alone.
In an embodiment, a physical model engine 726 is used to generate the physical model 727. In an embodiment, the physical model engine 726 may be implemented as hardware and/or software. The physical model engine 726 takes as inputs the chamber configuration and real world physics and chemical equations. The physical model engine 726 may implement a simulation of the physical and chemical interactions within a processing tool across a plurality of different processing parameters in order to build the physical model 727. As such, changes to processing parameters that modify the physical and/or chemical reactions in the processing tool may be mapped to expected device outcomes.
In an embodiment, the statistical model 725 and the physical model 727 are used as inputs for the generation of a ML model 728. For example, the statistical model 725 and the physical model 727 may be inputs for a ML model engine 729. The ML model engine 729 processes the physical model 727 and the statistical model 725 and outputs the ML model 728. In some embodiments, the physical model 727 may be used to derive some physical measurements that cannot be measured, and the physical model 727 outputs may be considered as additional inputs to the statistical model. In such situations, the ML model engine 729 adds the information from the physical model 727 to the statistical model 725 to provide the ML model 728. The ML model 728, therefore, allows for the two models 725 and 727 to be used for validation of individual points in the process space, and provides a more complete process space that can be individually tailored to a given processing tool. However, in some embodiments, the physical model 727 and the statistical model 725 may be standalone models, depending on the outputs. That is, in some embodiments, the statistical model 725 and the physical model 727 may not be merged into a ML model.
In an embodiment, the ML model may also be considered as another instance of a statistical model 725. For example, in
Referring now to
In an embodiment, process 870 may continue with operation 872, which comprises querying a ML model to select a set of processing parameters. In an embodiment, the ML model may be a model of a process space generated from the combination of a statistical model and a physical model. The statistical model may be generated using a DoE of actual wafers as described above. The physical model may be based on real world physics and chemical equations. For example, the physical model may be generated from a simulation of physical and chemical interactions within the processing tool across a plurality of different processing parameters. In an embodiment, the ML model may cover an entire process space available to the processing tool.
The ML model allows for a stable process recipe to be identified without relying solely on the experience and knowledge of a process engineer. Instead, a baseline recipe that is expected to produce device outcomes that closely match the targeted device outcomes is able to be selected from the process space of the ML model.
In an embodiment, process 870 may continue with operation 873, which comprises executing a small DoE to validate the model recommendation. Due to the high precision of the ML model, a small DoE (e.g., 20 or fewer wafers) may be all that is needed to validate the model recommendation. In an embodiment, the DoE may be designed by a process engineer. In another embodiment, the DoE may be designed using the ML model.
In an embodiment, process 870 may continue with operation 874, which comprises measuring the DoE wafer results with one or more metrology tools. The metrology data can be used to verify that the targeted device outcomes have been achieved on the wafer.
In an embodiment, process 870 may continue with operation 875, which comprises determining if the desired device outcomes have been achieved. If the desired device outcomes have been achieved, then the process proceeds along to operation 876 and the process is completed. If the desired device outcomes have not been achieved, then the process may cycle or feedback to operation 872. In an embodiment, the data from the small DoE may be fed back into the ML model in order to update the ML model. For example, if the process iteratively cycles back to operation 872, then DoEs executed at operation 873 may be designed based on knowledge of where the ML model is lacking (e.g., for a particular a process or plasma chamber) based on additional knowledge learned from the DoEs executed in the prior cycles. The updated ML model may then be queried to provide a second baseline recipe. In this manner, even when the first iteration is not successful, the process may still converge to the proper recipe quickly, without the need for extensive DoE and wasted resources.
Referring now to
In an embodiment, the process 980 may begin with operation 981, which comprises running a limited DoE of wafers with external metrology to baseline chamber performance. In an embodiment, the limited DoE may include twenty wafers or fewer. The limited DoE may utilize the process recipe of record as a baseline. The external metrology may include any metrology suitable to determine device outcomes for the processed wafers. For example, in the case of an oxidation process, ellipsometry may be used to investigate film thickness and thickness uniformity across a wafer.
In an embodiment, the process 980 may continue with operation 982, which comprises adding the device outcomes and other metrology data to the ML model. The additional data added to the ML model may be referred to as a calibration data set. The calibration data set is used to update the ML model so that the ML model more accurately reflects the current condition of the processing tool. For example, the process 580 may include operation 583, which comprises adjusting a model prediction to account for specific chamber conditions. That is, the process space of the ML model is updated to more closely match the conditions of the processing tool being investigated.
In an embodiment, the ML model may be a model of a process space generated from the combination of a statistical model and a physical model. The statistical model may be generated using a DoE of actual wafers as described above. The physical model may be based on real world physics and chemical equations. For example, the physical model may be generated from a simulation of physical and chemical interactions within a processing tool such as the plasma treatment chamber with rotating crossflows across a plurality of different processing parameters. In an embodiment, the ML model may cover an entire process space available to the processing tool.
In an embodiment, process 980 may continue with operation 984, which comprises predicting optimized process parameters to achieve a desired wafer outcome of wafers subsequently processed in the chamber. The optimized process parameters may be selected after the ML model has been updated to include the calibration data set. Accordingly, the new process recipe provides wafer parameters that result in wafer outcomes that are more closely matched to the targeted values, despite changes to the chamber condition. As such, chamber drift may be monitored and accounted for in order to maintain a tight process window and increase uniformity, repeatability, and yield. Additionally, unscheduled downtime of the tool is reduced since the processing recipe can be accurately adjusted to account for chamber drift. Furthermore, when PM does occur, process 980 may be implemented to provide a shorter recovery time, which improves tool utilization.
In an embodiment, a ML model may further be used to provide continuous (or near continuous) revision of a processing recipe to account for chamber drift. For example, wafer and process data obtained during the processing of device wafers may be obtained and used to update the ML model. That is, a dedicated DoE may not be necessary to provide a calibration data set. Wafer data from device wafers may be obtained for every wafer or for a subset of the wafers being processed.
Such an embodiment, may include a providing a ML model of a processing tool. The ML model may include a statistical model and a physical model that is similar to the ML models described above. In an embodiment, the process may begin with a recipe being executed in the processing tool to process a first wafer. After processing the first wafer, wafer data from the first wafer and process data from the processing tool relating to the execution of the recipe may be obtained. In an embodiment, the wafer data may comprise metrology data, such as, but not limited to, a thickness, a thickness uniformity, and a profile. In an embodiment, process data may include data obtained from sensors within processing tool and/or tool configuration information. In an embodiment, the wafer data and the process data is provided to the ML model to generate an updated ML model. In an embodiment, the updated ML model is used to generate a modified recipe to account for chamber drift in the processing tool. Embodiments may then include executing the modified recipe in the processing tool to process a second wafer. While processing of a single first wafer is described above, it is to be appreciated that a plurality of first wafers may be processed before the updated ML model is generated. In such an embodiment, multiple sets of wafer data and process data may be used to generate the updated ML model.
The exemplary computer system 1000 includes a processor 1002, a main memory 1004 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 1006 (e.g., flash memory, static random access memory (SRAM), MRAM, etc.), and a secondary memory 1018 (e.g., a data storage device), which communicate with each other via a bus 1030.
Processor 1002 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processor 1002 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processor 1002 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), network processor, or the like. Processor 1002 is configured to execute the processing logic 1026 for performing the operations described herein.
The computer system 1000 may further include a network interface device 1008. The computer system 1000 also may include a video display unit 1010 (e.g., a liquid crystal display (LCD), a light emitting diode display (LED), or a cathode ray tube (CRT)), an alphanumeric input device 1012 (e.g., a keyboard), a cursor control device 1014 (e.g., a mouse), and a signal generation device 1016 (e.g., a speaker).
The secondary memory 1018 may include a machine-accessible storage medium (or more specifically a computer-readable storage medium) 1032 on which is stored one or more sets of instructions (e.g., software 1022) embodying any one or more of the methodologies or functions described herein. The software 1022 may also reside, completely or at least partially, within the main memory 1004 and/or within the processor 1002 during execution thereof by the computer system 1000, the main memory 1004 and the processor 1002 also constituting machine-readable storage media. The software 1022 may further be transmitted or received over a network 1020 via the network interface device 1008.
While the machine-accessible storage medium 1032 is shown in an exemplary embodiment to be a single medium, the term “machine-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable storage medium” shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “machine-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media.
In accordance with an embodiment of the present disclosure, a machine-accessible storage medium has instructions stored thereon which cause a data processing system to perform a method of processing a wafer using insight from a ML model and/or a method of updating or building a ML model.
Embodiments of a plasma chamber having a rotating modulated crossflow have been disclosed.
Example embodiment 1: A plasma treatment chamber, comprises one or more sidewalls. A support surface within the one or more sidewalls holds a workpiece. An array of individual gas injectors is distributed about a periphery of the one or more sidewalls. One or more pump ports is along the one or more sidewalls to eject gas from the plasma treatment chamber. A controller is configured to control the plasma treatment chamber during an etch application. An etch rate uniformity of a material on the workpiece is tuned or controlled by: i) using the array of individual gas injectors to inject one or more gas flows in a direction generally parallel to and across a surface of the workpiece; ii) injecting a first gas flow from a first set of adjacent ones of the individual gas injectors to etch the materials on the workpiece; and iii) simultaneously injecting a second gas flow from at least a remaining set of the individual gas injectors. According to embodiments, the second gas flow is used to i) dilute the first gas flow to reduce an area on the workpiece having a faster etch rate; or ii) act as an additional etchant to increase the etch rate in the area of the workpiece having the faster etch rate.
Example embodiment 2: The plasma treatment chamber of embodiment 1, wherein the first gas flow comprises a process gas mixture including an etchant gas and the second gas flow comprises an independent gas injection (IGI) mixture.
Example embodiment 3: The plasma treatment chamber of embodiment 2, wherein the controller is further configured to change a gas flow injection angle of the process gas mixture or a gas flow injection angle of the IGI mixture to increase or decrease, respectively, concentration of the etchant gas.
Example embodiment 4: The plasma treatment chamber of embodiment 2, wherein the process gas mixture and the IGI mixture are injected during a single phase without gas flow rotation.
Example embodiment 5: The plasma treatment chamber of embodiment 2, wherein the process gas mixture and the IGI mixture are injected during multiple phases with gas flow rotation to achieve radial etch rate uniformity on the workpiece.
Example embodiment 6: The plasma treatment chamber of embodiment 2, wherein the process gas mixture and comprises one of CXFY, CXHYFZ, and CXHY.
Example embodiment 7: The plasma treatment chamber of claim 2, wherein the IGI mixture comprises a diluent gas including He, Ne, Ar, Kr, Rn, N, or Xe.
Example embodiment 8: The plasma treatment chamber of claim 2, wherein the IGI mixture comprises a cleaning gas including OX, N2, SFx, or NFx.
Example embodiment 9: The plasma treatment chamber of embodiment 1, wherein the array of individual gas injectors is located in one or more openings in the one or more sidewalls.
Example embodiment 10: The plasma treatment chamber of embodiment 1, wherein locations of the one or more pump ports are vertically offset from locations of the array of individual gas injectors
Example embodiment 11: The plasma treatment chamber of embodiment 1, wherein the first gas flow and the second gas flow are switched on and off to control gas flow rotation.
Example embodiment 12: The plasma treatment chamber of embodiment 2, further comprising a modulating function applied to a flow rate of at least one of the first and second gas flows or applied to an outlet conductance caused by at least one of the first and second pump ports.
Example embodiment 13: A plasma treatment chamber, comprising one or more sidewalls. A support surface within the one or more sidewalls holds a workpiece. An array of individual gas injectors is distributed about a periphery of the one or more sidewalls. One or more pump ports is along the one or more sidewalls to eject gas from the plasma treatment chamber. A controller is configured to control the plasma treatment chamber during an etch application. An etch rate uniformity of a material on the workpiece is tuned or controlled by: i) using the array of individual gas injectors to inject a gas flow in a direction generally parallel to and across a surface of the workpiece; and ii) prior to or during gas flow injection, changing the gas flow injection angle across the workpiece by selecting between a wide set of adjacent ones of the individual gas injectors and a narrow set of adjacent ones of the individual gas injectors, wherein selecting the narrow set of adjacent ones of the individual gas injectors decreases the gas injection angle.
Example embodiment 14: The plasma treatment chamber of embodiment 13, wherein the array of individual gas injectors comprises a plurality gas injector arrays, each having multiple ones of the individual gas injectors, wherein selected sets of adjacent gas injectors comprise the individual gas injectors from a particular one of gas injector arrays.
Example embodiment 15: The plasma treatment chamber of embodiment 13, wherein the array of individual gas injectors comprises a plurality gas injector arrays, each having multiple ones of the individual gas injectors, wherein selected sets of adjacent gas injectors comprise the individual gas injectors from neighboring ones of the gas injector arrays.
Example embodiment 16: The plasma treatment chamber of embodiment 13, wherein the gas flow comprises a first gas flow, the controller further configured to simultaneously inject a second gas flow from at least a portion of a remaining set of the individual gas injectors, wherein the second gas flow is used to i) dilute the first gas flow to reduce an area on the workpiece having a faster etch rate; or ii) act as an additional etchant to increase the etch rate in the area of the workpiece having the faster etch rate
Example embodiment 17: The plasma treatment chamber of embodiment 13, wherein decreasing the gas injection angle increases the etch rate uniformity.
Example embodiment 18: The plasma treatment chamber of embodiment 13, wherein the array of individual gas injectors is located in one or more openings in the one or more sidewalls.
Example embodiment 19: The plasma treatment chamber of embodiment 13, wherein locations of the one or more pump ports are vertically offset from locations of the array of individual gas injectors.
Example embodiment 20: The plasma treatment chamber of embodiment 13, wherein the first gas flow and the second gas flow are switched on and off to control gas flow rotation.
Example embodiment 21: The plasma treatment chamber of embodiment 13, further comprising a modulating function applied to a flow rate of at least one of the first and second gas flows or applied to an outlet conductance caused by at least one of the first and second pump ports.
Example embodiment 21: Embodiments disclosed herein include a method of controlling etch rate uniformity of a material on the workpiece in a plasma treatment chamber. The method comprises injecting, from a first set of adjacent ones of the individual gas injectors, a first gas flow in a direction generally parallel to and across a surface of the workpiece to etch materials on the workpiece. Simultaneously injecting, from at least a portion of a remaining set of the individual gas injectors, a second gas flow in a direction generally parallel to and across a surface of the workpiece, wherein the second gas flow is used to i) dilute the first gas flow to reduce an area on the workpiece having a faster etch rate; or ii) act as an additional etchant to increase the etch rate in the area of the workpiece having the faster etch rate.
Example embodiment 23: The method of embodiment 21 further comprising querying a machine learning (ML) model to control timing of the first gas flow and the second gas flow.
Example embodiment 24: Embodiments disclosed herein include a method of performing a rotating gas crossflow in a plasma treatment chamber and a non-transitory computer readable medium having stored thereon software instructions that, when executed by a processor, cause the processor to rotate gas crossflow in a plasma treatment chamber, by executing the following steps: i) using an array of individual gas injectors to inject one or more gas flows in a direction generally parallel to and across a surface of the workpiece; ii) injecting a first gas flow from a first set of adjacent ones of the individual gas injectors to etch the materials on the workpiece; and iii) simultaneously injecting a second gas flow from at least a remaining set of the individual gas injectors. According to embodiments, the second gas flow is used to i) dilute the first gas flow to reduce an area on the workpiece having a faster etch rate; or ii) act as an additional etchant to increase the etch rate in the area of the workpiece having the faster etch rate.
Example embodiment 25: Embodiments disclosed herein include a method of performing a rotating gas crossflow in a plasma treatment chamber and a non-transitory computer readable medium having stored thereon software instructions that, when executed by a processor, cause the processor to rotate gas crossflow in a plasma treatment chamber, by executing the following steps: i) using an array of individual gas injectors to inject a gas flow in a direction generally parallel to and across a surface of the workpiece; and ii) prior to or during gas flow injection, changing the gas flow injection angle across the workpiece by selecting between a wide set of adjacent ones of the individual gas injectors and a narrow set of adjacent ones of the individual gas injectors, wherein selecting the narrow set of adjacent ones of the individual gas injectors decreases the gas injection angle.
This application is a continuation of co-pending Patent Application Ser. No. 63/236,166, filed Aug. 23, 2021, assigned to the assignee of the present application, and both incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63236166 | Aug 2021 | US |
