WO2020106784A1 - Process optimization using design of experiments and response surface models - Google Patents
Process optimization using design of experiments and response surface modelsInfo
- Publication number
- WO2020106784A1 WO2020106784A1 PCT/US2019/062308 US2019062308W WO2020106784A1 WO 2020106784 A1 WO2020106784 A1 WO 2020106784A1 US 2019062308 W US2019062308 W US 2019062308W WO 2020106784 A1 WO2020106784 A1 WO 2020106784A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- candidates
- metrology tool
- processor
- settings
- response surface
- Prior art date
Links
Classifications
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
- H01L22/00—Testing or measuring during manufacture or treatment; Reliability measurements, i.e. testing of parts without further processing to modify the parts as such; Structural arrangements therefor
- H01L22/30—Structural arrangements specially adapted for testing or measuring during manufacture or treatment, or specially adapted for reliability measurements
- H01L22/34—Circuits for electrically characterising or monitoring manufacturing processes, e. g. whole test die, wafers filled with test structures, on-board-devices incorporated on each die, process control monitors or pad structures thereof, devices in scribe line
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- 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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
- H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
- H01L21/67—Apparatus specially adapted for handling semiconductor or electric solid state devices during manufacture or treatment thereof; Apparatus specially adapted for handling wafers during manufacture or treatment of semiconductor or electric solid state devices or components ; Apparatus not specifically provided for elsewhere
- H01L21/67005—Apparatus not specifically provided for elsewhere
- H01L21/67242—Apparatus for monitoring, sorting or marking
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
- H01L22/00—Testing or measuring during manufacture or treatment; Reliability measurements, i.e. testing of parts without further processing to modify the parts as such; Structural arrangements therefor
- H01L22/10—Measuring as part of the manufacturing process
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
- H01L22/00—Testing or measuring during manufacture or treatment; Reliability measurements, i.e. testing of parts without further processing to modify the parts as such; Structural arrangements therefor
- H01L22/10—Measuring as part of the manufacturing process
- H01L22/12—Measuring as part of the manufacturing process for structural parameters, e.g. thickness, line width, refractive index, temperature, warp, bond strength, defects, optical inspection, electrical measurement of structural dimensions, metallurgic measurement of diffusions
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
- H01L22/00—Testing or measuring during manufacture or treatment; Reliability measurements, i.e. testing of parts without further processing to modify the parts as such; Structural arrangements therefor
- H01L22/20—Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps
- H01L22/24—Optical enhancement of defects or not directly visible states, e.g. selective electrolytic deposition, bubbles in liquids, light emission, colour change
-
- 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
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/45—Nc applications
- G05B2219/45031—Manufacturing semiconductor wafers
Definitions
- This disclosure relates to process optimization and, more particularly, to process optimization for semiconductor manufacturing.
- Fabricating semiconductor devices typically includes processing a semiconductor wafer using a large number of fabrication processes to form various features and multiple levels of the semiconductor devices.
- lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a photoresist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing (CMP), etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer that are separated into individual semiconductor devices.
- Inspection processes are used at various steps during semiconductor manufacturing to detect defects on wafers to promote higher yield in the manufacturing process and, thus, higher profits.
- Metrology processes are also used at various steps during semiconductor manufacturing to monitor and control the process. Metrology processes are different than inspection processes in that, unlike inspection processes in which defects are detected on wafers, metrology processes are used to measure one or more characteristics of the wafers that cannot be determined using existing inspection tools.
- Metrology processes can be used to measure one or more characteristics of wafers such that the performance of a process can be determined from the one or more characteristics. For example, metrology processes can measure a dimension (e.g., line width, thickness, etc.) of features formed on the wafers during the process. In addition, if the one or more characteristics of the wafers are unacceptable (e.g., out of a predetermined range for the characteristic ⁇ )), the measurements of the one or more characteristics of the wafers may be used to alter one or more parameters of the process such that additional wafers manufactured by die process have acceptable characteristics).
- a dimension e.g., line width, thickness, etc.
- the“brute force method” varied every tool setting over its available range. For discrete settings (e.g., unpolarized, s-polarized, or p-polarized illumination), every permutation was tested. For continuous settings (e.g., focal position), the full usable range can be discretized by selecting a step size. The total number of required experiments could be unreasonably large. Additionally, current brute force methods are not able to account for inherent process variability. [0009] Therefore, new process optimization techniques and systems are needed.
- a metrology tool is provided in a first embodiment.
- the metrology tool comprises an energy source that generates a beam, a stage that secures a wafer in a path of the beam from the energy source, a detector, and a processor in electronic communication with the detector.
- the processor is configured to: receive a plurality of measurements; determine a plurality of combinations of tool settings on the metrology tool using analysis of variance (ANOVA); determine candidates from the plurality of combinations; generate a response surface model for each of the candidates; and determine a list of the candidates of the tool settings that provide a maximum response and are least sensitive to sources of noise.
- ANOVA analysis of variance
- Each of the candidates on the list of the candidates is each from a denser region of the response surface model.
- the processor can be further configured to send instructions to perform the measurements on a semiconductor wafer using the metrology tool.
- the measurements may be collected with a mixed effects model.
- Tunable settings on the metrology tool can be independent variables.
- the list of candidates can provide improved performance of the metrology tool relative to a remainder of the candidates.
- the processor can be further configured to determine a score based on measurement quality.
- the response surface model can be a 3x3 response surface model.
- the processor can be further configured to adjust one or more settings on the metrology tool based on the list of candidates.
- a method is provided in a second embodiment.
- the method comprises receiving a plurality of measurements from a metrology tool at a processor.
- a plurality of combinations of tool settings on the metrology tool are determined using ANOVA.
- candidates from the plurality of combinations are determined.
- a response surface model is generated for each of the candidates.
- a list of the candidates of the tool settings that provide a maximum response and are least sensitive to sources of noise are determined.
- Each of the candidates on list of the candidates is each from a denser region of the response surface model.
- the method can further comprise performing the measurements on a semiconductor wafer using the metrology tool.
- the measurements may be collected with a mixed effects model.
- Tunable settings on the metrology tool can be independent variables.
- the list of candidates can provide improved performance of the metrology tool relative to a remainder of die candidates.
- the method can further comprise determining a score based on measurement quality.
- the response surface model can be a 3x3 response surface model.
- the method can further comprise adjusting, using the processor, one or more settings on the metrology tool using the list of candidates.
- a non-transitory computer readable medium storing a program can be configured to instruct a processor to execute the method of the second embodiment.
- FIG. 1 is a flowchart of a method embodiment in accordance with the present disclosure
- FIG. 2 is a block diagram of a metrology system embodiment in accordance with the present disclosure
- FIG. 3 is an exemplary 41x32 response surface
- FIG. 4 is an exemplary 9x9 response surface
- FIG. 5 is a table of an exemplary Type III test of fixed effects
- FIGS. 6A-6B illustrate 4-way interaction
- FIGS.7A-7C illustrate exemplary response surfaces for a top recipe candidate
- FIG. 8 illustrates exemplary results.
- a process optimization technique for metrology systems is disclosed. This technique is accurate, verifiable, and resulted in a reduction in cost (i.e., time-to-results).
- DOE designed experiment
- candidate setup conditions have been identified, response surface methodology is utilized to interpolate points of optimal and stable response for the validation phase.
- a validation phase can use dense wafer sampling and success criteria can be based on meeting targeted residuals
- the metrology tools should perform precise measurements consistently and in adverse process conditions.
- An optimal set of equipment conditions are needed that will be robust to uncontrollable or unknown sources of variability.
- the techniques disclosed herein can provide such equipment conditions. These techniques also are robust to different semiconductor manufacturers, each of which has its own specific requirements. These techniques also address a multiple response problem where the results contain several responses designed to assess the metrology tool’s ability to measure accurately and consistently.
- FIG. 1 is a flowchart of a method 100. Some of all of the steps in the method 100 can be performed using a processor.
- a mixed effects model can be used to determine which combination of equipment settings will be robust to process variations. Modeled results can provide information that provides confidence in future measurements. Each measurement may be different, but, for example, a model that provides us with a less than 1% chance of being incorrect when new wafers are measured using those conditions can be determined.
- the experiment can be constructed to ensure all tool settings are assessed independently.
- tool settings can be included as fixed effects and sources of noise can be included as the random effects. Therefore, this can be a mixed model. Including known but uncontrollable sources of variation into the model, effects from random factors can be blocked and metrology tool settings can be assessed independently of the noise.
- Measurements are performed on a semiconductor wafer using a metrology tool at 101. This step 101 may be performed separately from the other steps. Thus, results can be sent to a processor from a separate metrology tool or can be sent to the metrology tool’s processor. The measurements can be collected with a mixed effects model.
- An appropriate Design of Experiment (DOE) can be selected such that the maximum information is extracted with a reasonable number of experiments (i.e., measurements).
- DOE types include split plot designs, orthogonal inner/outer arrays, or fractional factorials. Other DOE types can be used.
- the method 100 can work on any equipment with any combination of discreet and/or continuous variable settings.
- the measurements from the metrology tool are received at a processor at 102.
- Combinations of tool settings on the metrology tool are determined using analysis of variance (ANOVA) at 103.
- Tool settings can include mark design, aperture settings, polarization settings, illumination bandwidth, wavelength, focus, or other tool settings.
- ANOVA can be used as a screening process so that insignificant parameters can be ignored. This results in a smaller problem to be solved.
- Other techniques besides ANOVA also can be used at step 103.
- Candidates from the combinations are determined at 104.
- ANOVA can determine the most robust categorical settings before measuring combinations of continuous variables. In the case of no continuous variables, ANOVA can determine the best settings per semiconductor layer. Each layer may be unique and may require individual settings. ANOVA can provide specific combination of settings and provide confidence for future measurements. After the mixed effects model is fit, tests of statistical significance can be calculated for each main effect and each interaction effect. For the statistically significant parameters, the top-ranking combinations can be determined, such as at step 104.
- a response surface model is generated for each of the candidates at 105.
- tile method 100 examines individual local maxima in a larger response surface and expands the DOE until curvature is detected in the response. This can provide more accurate predictions.
- Prior knowledge about which settings produce the desired results are needed in a typical manufacturing process. However, the semiconductor industry can have chemical and optical considerations that are unique to each individual process step and prior knowledge is generally not available.
- the response surface model may be a 3x3 response surface model.
- Tunable settings on the metrology tool can include wavelength or focus.
- a response surface method can be used to efficiently sample the response of continuous independent variables with a small number of experimental tests, and then determine the best settings by fitting a simple, smooth function (such as a linear of quadratic fit) to this experimental dataset. This approach helps reduce the number of experiments required.
- a response surface model can he based on peaks, which can be the regions ofinterest. Peaks can be extracted from the landscape.
- a response surface model can be generated from a nine point DOE (e.g., a 3x3 response surface model). Nine points can cover a region with no curvature on its landscape. A nine point DOE also can be designed to fit a quadratic model. [0038] A local optimization can be performed over the statistically significant variables for the top ranking recipes using the initial date. Regions of interest can be located for this local optimization. Response surface methodology can be applied to the top candidates (secondary equipment settings), with the primary (tunable) settings as independent variables. This can provide a predicted set of equipment settings that may or may not have actually been measured. The method 100 can predict a set of optimal equipment settings.
- a nine point DOE e.g., a 3x3 response surface model. Nine points can cover a region with no curvature on its landscape. A nine point DOE also can be designed to fit a quadratic model.
- a local optimization can be performed over the statistically significant variables for the top ranking recipes using the initial date. Regions of interest can be located for this local optimization.
- a response surface model can determine: maximum interpolated response location, response surface shape, sensitivity to wavelength and focus changes, and/or fit diagnostics to determine adequacy of a model.
- a quadratic response surface model can be fit in a region. The response can be interpolated within the DOE space by running the response surface methodology (RSM) model which provides a fit equation. A new response can be predicted to within a level of error. Trusted candidates within, for example, a 5% margin of error may be selected.
- RSM modeling is two models in one: quadratic fitting and canonical modeling. Canonical modeling provides eigenvalues that determine the directions of the largest variability. Two eigenvalues can be obtained fora quadratic model.
- both eigenvalues are negative, then a region of maximum response has been found. If both eigenvalues are positive, an area of minimum response has been found. If the signs of the eigenvalues are mixed, a saddle shape (the response increases in one direction and decreases in a different direction) has been found. Sensitivities can be determined by the fit equation and the fit equation can be used to model the predicted response change in a one unit change to the continuous variable. Fit diagnostics (p-values) can provide a level of confidence that a quadratic model is actually appropriate. If the fit diagnostics are poor (e.g., p-values > 0.05), a determination can be made that a particular model coefficient was not needed. Fit diagnostics can be used to test for curvature.
- the area of the response surface model where attention should be focused is not known. It may not be possible to monitor the same region on every response surface model because the response surface models can be different.
- a list of the candidates of the tool settings that provide a maximum response and are least sensitive to sources of noise is determined at 106. This determination can use ANOVA and RSM modeling as described herein.
- Each of the candidates on list of the candidates is from a denser region of die response surface model. In an example, the entire region in wavelength and focus from the minimum to the maximum in each variable is measured. SPOC allows interpolation and the number of points to be measured can be reduced by over 75% still using the entire range. This can be considered the dense region, though other reductions are possible for the dense region.
- the list of candidates also can provide improved performance of the metrology tool relative to a remainder of the candidates.
- any process change (such as photoresist thickness) can drift the best wavelength needed.
- SPOC can provide a solution where a 10 nm wavelength change does not alter the predicted response or the effect of a process change is minimized.
- an experimental design can be constructed that is centered at the predicted equipment settings found in step 105.
- a denser region can be explored around the chosen candidate to determine the equipment settings that can deliver improved quality and which are least sensitive to sources of noise. Exploring around a chosen candidate and determining equipment settings can be performed simultaneously. One or more of the equipment settings on a metrology tool (or other tool) can be changed based on the results of the method 100.
- the design of method 100 can use the full response space and detects regions of interest to run traditional response surface models.
- the metrology tool can be monitored and controlled over time after the candidates that provide a maximum response are determined.
- a metrology tool may drift over time or the thickness of a wafer can change, which will result in changes of the response surface model.
- Metrology tools may never run identical experiments.
- Embodiments of the method 100 provide a way to detect and adapt to the changes.
- the mathematical model can be modified automatically on a case-by-case basis.
- Visual aids for semiconductor manufacturers can also adapt to each unique experiment
- the system 200 includes optical based subsystem 201.
- the optical based subsystem 201 is configured for generating optical based output fora specimen 202 by directing light to (or scanning light over) and detecting light from the specimen 202.
- the specimen 202 includes a wafer.
- the wafer may include any wafer known in the art
- the specimen includes a reticle.
- the reticle may include any reticle known in the art.
- optical based subsystem 201 includes an illumination subsystem configured to direct light to specimen 202.
- the illumination subsystem includes at least one light source.
- the illumination subsystem includes light source 203.
- the illumination subsystem is configured to direct the light to the specimen 202 at one or more angles of incidence, which may include one or more oblique angles and/or one or more normal angles.
- light from light source 203 is directed through optical element 204 and then lens 205 to specimen 202 at an oblique angle of incidence.
- the oblique angle of incidence may include any suitable oblique angle of incidence, which may vary depending on, for instance, characteristics of the specimen 202.
- the optical based subsystem 201 may be configured to direct the light to the specimen
- the optical based subsystem 201 may be configured to alter one or more characteristics of one or more elements of the illumination subsystem such that the light can be directed to the specimen 202 at an angle of incidence that is different than that shown in FIG. 2.
- the optical based subsystem 201 may be configured to move light source 203, optical element 204, and lens 205 such that the light is directed to the specimen 202 at a different oblique angle of incidence or a normal (or near normal) angle of incidence.
- the optical based subsystem 201 may be configured to direct light to the specimen 202 at more than one angle of incidence at the same tone.
- the illumination subsystem may include more than one illumination channel, one of the illumination channels may include light source 203, optical element 204, and 1ms 205 as shown in FIG. 2 and another of the illumination channels (not shown) may include similar elements, which may be configured differently or the same, or may include at least a light source and possibly one or more other components such as those described further herein.
- one or more characteristics e.g., wavelength, polarization, etc.
- characteristics e.g., wavelength, polarization, etc.
- the illumination subsystem may include only one light source (e.g., light source 203 shown in FIG. 2) and light from the light source may be separated into different optical paths (e.g., based on wavelength, polarization, etc.) by one or more optical elements (not shown) of the illumination subsystem. Light in each of the different optical paths may then be directed to the specimen 202.
- Multiple illumination channels may be configured to direct light to the specimen 202 at the same time or at different times (e.g., when different illumination channels are used to sequentially illuminate the specimen).
- the same illumination channel may be configured to direct light to the specimen 202 with different characteristics at different times.
- optical element 204 may be configured as a spectral filter and the properties of the spectral filter can be changed in a variety of different ways (e.g., by swapping out the spectral filter) such that different wavelengths of light can be directed to the specimen 202 at different times.
- the illumination subsystem may have any other suitable configuration known in the art for directing the light having different or the same characteristics to the specimen 202 at different or the same angles of incidence sequentially or simultaneously.
- light source 203 may include a broadband plasma (BBP) source.
- BBP broadband plasma
- the light generated by the light source 203 and directed to the specimen 202 may include broadband light.
- the light source may include any other suitable light source such as a laser.
- the laser may include any suitable laser known in the art and may be configured to generate light at any suitable wavelength or wavelengths known in the art.
- the laser may be configured to generate light that is monochromatic or nearly-monochromatic. In this manner, the laser may be a narrowband laser.
- the light source 203 may also include a polychromatic light source that generates light at multiple discrete wavelengths or wavebands.
- Light from optical element 204 may be focused onto specimen 202 by lens 205. Although lens 205 is shown in FIG.
- lens 205 may include a number of refractive and/or reflective optical elements that in combination focus the light from the optical element to the specimen.
- the illumination subsystem shown in FIG. 2 and described herein may include any other suitable optical dements (not shown). Examples of such optical elements include, but are not limited to, polarizing components), spectral filters), spatial filter(s), reflective optical elements), apodizer(s), beam splitter(s) (such as beam splitter 213), aperture(s), and the like, which may include any such suitable optical elements known in the art.
- the optical based subsystem 201 may be configured to alter one or more of the elements of the illumination subsystem based on the type of illumination to be used for generating the optical based output.
- the optical based subsystem 201 may also include a scanning subsystem configured to cause the light to be scanned over the specimen 202.
- the optical based subsystem 201 may include stage 206 on which specimen 202 is disposed during optical based output generation.
- the scanning subsystem may include any suitable mechanical and/or robotic assembly (that includes stage 206) that can be configured to move the specimen 202 such that the light can be scanned over the specimen 202.
- the optical based subsystem 201 may be configured such that one or more optical elements of the optical based subsystem 201 perform some scanning of the light over the specimen 202.
- the light may be scanned over the specimen 202 in any suitable fashion such as in a serpentine-like path or in a spiral path.
- the optical based subsystem 201 further includes one or more detection channels. At least one of the one or more detection channels includes a detector configured to detect light from die specimen 202 due to illumination of the specimen 202 by the subsystem and to generate output responsive to the detected light
- the optical based subsystem 201 shown in FIG. 2 includes two detection channels, one formed by collector 207, element 208, and detector 209 and another formed by collector 210, element 211 , and detector 212.
- the two detection channels are configured to collect and detect light at different angles of collection.
- both detection channels are configured to detect scattered light
- the detection channels are configured to detect tight that is scattered at different angles from the specimen 202.
- one or more of the detection channels maybe configured to detect another type of light from the specimen 202 (e.g., reflected light).
- both detection channels are shown positioned in the plane of the paper and the illumination subsystem is also shown positioned in the plane of die paper. Therefore, in this embodiment, both detection channels are positioned in (e.g., centered in) the plane of incidence. However, one or more of the detection channels may be positioned out of die plane of incidence.
- the detection channel formed by collector 210, element 211, and detector 212 may be configured to collect and detect light that is scattered out of the plane of incidence. Therefore, such a detection channel may be commonly referred to as a“side” channel, and such a side channel may be centered in a plane that is substantially perpendicular to the plane of incidence.
- FIG. 2 shows an embodiment of the optical based subsystem 201 that includes two detection channels
- the optical based subsystem 201 may include a different number of detection channels (e.g. only one detection channel or two or more detection channels).
- the detection channel formed by collector 210, element 211, and detector 212 may form one side channel as described above, and the optical based subsystem 201 may include an additional detection channel (not shown) formed as another side channel that is positioned on the opposite side of foe plane of incidence.
- the optical based subsystem 201 may include the detection channel that includes collector 207, element 208, and detector 209 and that is centered in the plane of incidence and configured to collect and detect light at scattering angle(s) that are at or close to normal to the specimen 202 surface.
- This detection channel may therefore be commonly referred to as a“top” channel, and the optical based subsystem 201 may also include two or more side channels configured as described above.
- the optical based subsystem 201 may include at least three channels (i.e., one top channel and two side channels), and each of the at least three channels has its own collector, each of which is configured to collect light at different scattering angles than each of the other collectors.
- each of the detection channels included in the optical based subsystem 201 may be configured to detect scattered light. Therefore, the optical based subsystem 201 shown in FIG. 2 may be configured for dark field (DF) output generation for specimens 202. However, the optical based subsystem 201 may also or alternatively include detection channels) that are configured for Wight field (BF) output generation for specimens 202. In other words, the optical based subsystem 201 may include at least one detection channel that is configured to detect light specularly reflected from the specimen 202. Therefore, the optical based subsystems 201 described herein may be configured for only DF, only BF, or both DF and BF imaging.
- DF dark field
- BF Wight field
- each of the collectors are shown in FIG.2 as single refractive optical elements, it is to be understood that each of the collectors may include one or more refractive optical die(s) and/or one or more reflective optical elements).
- the one or more detection channels may include any suitable detectors known in the art
- the detectors may include photo-multiplier tubes (PMTs), charge coupled devices (CCDs), time delay integration (TD1) cameras, and any other suitable detectors known in the art
- the detectors may also include non-imaging detectors or imaging detectors. In this manner, if the detectors are non-imaging detectors, each of the detectors may be configured to detect certain characteristics of die scattered light such as intensity but may not be configured to detect such characteristics as a function of position within the imaging plane.
- the output that is generated by each of the detectors included in each of the detection channels of the optical based subsystem may be signals or data, but not image signals or image data.
- a processor such as processor 214 may be configured to generate images of the specimen 202 from die non-imaging output of the detectors.
- the detectors may be configured as imaging detectors that are configured to generate imaging signals or image data. Therefore, the optical based subsystem may be configured to generate optical images or other optical based output described herein in a number of ways.
- FIG. 2 is provided herein to generally illustrate a configuration of an optical based subsystem 201 that may be included in the system embodiments described herein or that may generate optical based output that is used by the system embodiments described herein.
- the optical based subsystem 201 configuration described herein may be altered to optimize the performance of the optical based subsystem 201 as is normally performed when designing a commercial output acquisition system.
- the systems described herein may be implemented using an existing system (e.g., by adding functionality described herein to an existing system).
- the methods described herein may be provided as optional functionality of fee system (e.g., in addition to other functionality of the system).
- fee system described herein may be designed as a completely new system.
- the processor 214 may be coupled to the components of the system 200 in any suitable manner (e.g., via one or more transmission media, which may include wired and/or wireless transmission media) such that the processor 214 can receive output.
- the processor 214 may be configured to perform a number of functions using the output.
- the system 200 can receive instructions or other information from the processor 214.
- the processor 214 and/or the electronic data storage unit 215 optionally may be in electronic communication with a wafer inspection tool, a wafer metrology tool, or a wafer review tool (not illustrated) to receive additional information or send instructions.
- the processor 214 and/or the electronic data storage unit 215 can be in electronic communication with an SEM.
- the processor 214, other system(s), or other subsystem(s) described herein may be part of various systems, including a personal computer system, image computer, mainframe computer system, workstation, network appliance ⁇ internet appliance, or other device.
- the subsystem ⁇ ) or system(s) may also include any suitable processor known in the art, such as a parallel processor.
- fee subsystem(s) or system(s) may include a platform wife high-speed processing and software, either as a standalone or a networked tool.
- Theprocessor 214 and electronic data storage unit 2I5 maybe disposed in or ot erwise part of the system 200 or another device.
- the processor 214 and electronic data storage unit 215 may be part of a standalone control unit or in a centralized quality control unit. Multiple processors 214 or electronic data storage units 215 may be used.
- the processor 214 may be implemented in practice by any combination of hardware, software, and firmware. Also, its functions as described herein may be performed by one unit, or divided up among different components, each of which may be implemented in turn by any combination of hardware, software and firmware. Program code or instructions for the processor 214 to implement various methods and functions may be stored in readable storage media, such as a memory in the electronic data storage unit 215 or other memory.
- the system 200 includes more than one processor 214, then the different subsystems may be coupled to each other such that images, data, information, instructions, etc. can be sent between the subsystems.
- one subsystem may be coupled to additional subsystem(s) by any suitable transmission media, which may include any suitable wired and/or wireless transmission media known in the art.
- Two or more of such subsystems may also be effectively coupled by a shared computer-readable storage medium (not shown).
- the processor 214 may be configured to perform a number of functions using the output of the system 200 or other output. For instance, the processor 214 may be configured to send the output to an electronic data storage unit 215 or another storage medium.
- the processor 214 may be fbrther configured as described herein.
- the processor 214 may be configured according to any of the embodiments described herein.
- the processor 214 also may be configured to perform other functions or additional steps using the output of the system 200 or using images or data from other sources.
- the carrier medium may include a storage medium such as a read-only memory, a random access memory, a magnetic or optical disk, a non-volatile memory, a solid state memory, a magnetic tape, and tire like.
- a carrier medium may include a transmission medium such as a wire, cable, or wireless transmission link.
- the various steps described throughout the present disclosure may be carried out by a single processor 214 or, alternatively, multiple processors 214.
- different sub-systems of tire system 200 may include one or more computing or logic systems. Therefore, the above description should not be interpreted as a limitation on the present disclosure but merely an illustration.
- the processor 214 is in communication with the system 200.
- the processor 214 is configured to receive a plurality of measurements; determine a plurality of combinations of tool settings on the metrology tool using ANOVA; determine candidates from the plurality of combinations; generate a response surface model for each of the candidates; and determine a list of the candidates of the tool settings that provide a maximum response and are least sensitive to sources of noise.
- Each of the candidates on list of the candidates is each from a denser region of die response surface model.
- the list of candidates can provide improved performance of the metrology tool relative to a remainder of the candidates.
- the candidates for the response surface model can be secondary equipment settings on the metrology tool and tunable settings on the metrology tool can be independent variables.
- the response surface model may be a 3x3 response surface model.
- the processor can be further configured to send instructions to perform the measurements on a semiconductor wafer using the metrology tool.
- the measurements can be collected with a mixed effects model.
- the processor 214 can be further configured to determine a score based on measurement quality.
- a composite desirability approach can be used to deconvolve a situation where each measured attribute has different units and different variation.
- Composite desirability can transform each measured value to a score between 0 and 1.
- Additive, multiplicative, or geometric means can be used to combine all measured quality attributes into a single score.
- An additional embodiment relates to a non-transitory computer-readable medium storing program instructions executable on a controller for performing a computer-implemented method for process optimization, as disclosed herein.
- electronic data storage unit 215 or other storage medium may contain non-transitory computer-readable medium that includes program instructions executable on die processor 214.
- the computer-implemented method may include any step(s) of any method(s) described herein, including method 100.
- the program instructions may be implemented in any of various ways, including procedure-based techniques, component-based techniques, and/or object-oriented techniques, among others.
- the program instructions may be implemented using ActiveX controls, C++ objects, JavaBeans, Microsoft Foundation Classes (MFC), Streaming SIMD Extension (SSE), or other technologies or methodologies, as desired.
- MFC Microsoft Foundation Classes
- SSE Streaming SIMD Extension
- “Factor A” designs of interest were SB1, SB2, and SB3.
- “Factor B” settings of interest were Low, Mid, High.“Factor C” states of interest were P, S and Un.“Factor D” settings of interest are discreet values and“Factor E” levels of interest are in the range from -2400 to 2400.
- “Factor A,”“Factor B,” and“Factor C” were held constant while a location on the wafer was measured.
- “Factor D” and“Factor E” were varied.
- split-plot designs are applicable to many situations where difficult to change factors are applied to larger experimental units (e.g., wafers) and easy to change factors are applied at smaller sub-experimental units (e.g., sites on wafer).
- the brute force method was to generate a 41x32 response surface (1312 runs) for each main plot treatment combination. It was determined through experimentation using previous data that any 41x32 response surface can be divided into 9 L 2 treatment combinations and still maintain the overall structure of the response surface.
- the initial experiment focused on first generating the split-plot model and evaluating the significant effects and interactions to determine candidates for response surface modeling.
- Three to five candidate recipes were chosen to receive a quadratic response surface model in a 3 A 2 face- centered composite design centered on the specific candidate’s“Factor D” and“Factor E.”
- the initial 9 L 2 response surface contains the points needed for the central composite face centered response surface model.
- a composite desirability score was calculated using a geometric means method. Once a single composite response (d_c) was generated for the model it served as the response for both the split plot analysis and for a response surface model.
- a statistical linear effects model was constructed based on the design.
- a linear effects model is one where the response is a function of linear components of the individual effects. Effects are either controllable factors, interactions between controllable factors, or random but known sources of variation.
- the type III tests of a fixed effects (reduced model) are shown in FIG. 5. This output is standard output for the mixed model GLIMMDC procedure in SAS and provides F-test statistics and p-values that help us to understand which combinations of equipment settings are important
- a GLIMMDC procedure fits statistical models to data with correlations or non-constant variability and where the response is not necessarily normally distributed. These models are known as generalized linear mixed models (GLMM).
- the outputs from least square means can be used to determine specific settings of each factor.
- Least square means is similar to traditional averaging, but when the number of observations in a DOE are not equal across different levels. Least square means can provide a correction for unbalanced designs. If the design is balanced (i.e., has the same number of observations across levels) then the least square means equals the average.
- a quadratic response surface model was run on a smaller area of the full response surface using 3 L 2 points subset from the 9 L 2“Factor D” and“Factor E” measurements and centered around the candidates“Factor D” and“Factor E.” Proceeding with validation measurements required that several criteria be met. First, a lack of fit test determines if a quadratic model is appropriate. Second, the response surface contains a local maximum if the above two conditions are met Third, a stationary response point can be calculated (FIGS. 7A-7C).
- This point of optimal response is an interpolated point that needs to be tested and validated.
- Collecting the response surface data as a 9 A 2 factorial provides a 3 L 2 face centered response surface model centered around the“Factor D" and“Factor E” of each candidate without further data collection.
- Information can be obtained from fitting a quadratic response surface in a statistical software package.
- X-axis and Y-axis response surfaces are shown separately.
- a quadratic model is used in response surface modeling and a lack of fit test for each response surface (not shown) identified that a quadratic model was warranted (i.e., no lack of fit in second order terms) and that each stationary point was at a point of maximum response in that region.
- FIGS- 7A-7C as (“Factor D’ -2“Factor E’ -5.3 ⁇ .
- Reported eigenvalues and eigenvectors make up the canonical analysis and their magnitude and direction provide useful information regarding sensitivity and surface shape.
- the sign of the eigenvalues (all negative in the examples shown in FIGS. 7A-7C) indicates that the calculated stationary points are at a maximum and that fee composite quality score decreases more in the direction of“Factor D” than in fee direction of“Factor E”.
- the sensitivity differences could be a new metric itself in fee search for fee optimum set of operating conditions.
- a split plot design to identify significant factor combinations allowed for the ability to focus the response surface methodology on a small subset of the whole plot treatment combinations.
- a single“Factor A” design SB3
- SB3 single“Factor B”
- LS-Means plots and connecting letters reports proved to be useful aids to validate the results of the type PI test of fixed effects and can provide semiconductor manufacturers with fee ability to visually inspect fee recipe settings that are provided.
- Response surface modeling centered around candidate“Factor D” and“Factor E” values can lead to measurable candidates for validation testing across the entire wafer.
- Each of fee steps of the method may be performed as described herein.
- the methods also may include any other step(s) that can be performed by the processor and/or computer subsystem(s) or system(s) described herein.
- the steps can be performed by one or more computer systems, which may be configured according to any of fee embodiments described herein.
- the methods described above may be performed by any of fee system embodiments described herein.
Abstract
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US9412673B2 (en) * | 2013-08-23 | 2016-08-09 | Kla-Tencor Corporation | Multi-model metrology |
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US20050246044A1 (en) * | 2004-04-28 | 2005-11-03 | International Business Machines Corporation | System and method for optimizing metrology sampling in apc applications |
US20060009872A1 (en) * | 2004-07-08 | 2006-01-12 | Timbre Technologies, Inc. | Optical metrology model optimization for process control |
US20060265162A1 (en) * | 2005-05-04 | 2006-11-23 | Hitachi Global Storage Technologies | Aggregated run-to-run process control for wafer yield optimization |
KR20130111555A (en) * | 2010-09-14 | 2013-10-10 | 어플라이드 머티어리얼스, 인코포레이티드 | Transfer chamber metrology for improved device yield |
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