Classifications

• • H—ELECTRICITY
  • H01—ELECTRIC ELEMENTS
  • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
  • H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
  • H01J37/32—Gas-filled discharge tubes
• • H—ELECTRICITY
  • H01—ELECTRIC ELEMENTS
  • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
  • H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
  • H01J37/32—Gas-filled discharge tubes
  • H01J37/32917—Plasma diagnostics
  • H01J37/32935—Monitoring and controlling tubes by information coming from the object and/or discharge
• • H—ELECTRICITY
  • H01—ELECTRIC ELEMENTS
  • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
  • H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
  • H01J37/32—Gas-filled discharge tubes
  • H01J37/32917—Plasma diagnostics
  • H01J37/3299—Feedback systems

Definitions

• This invention relates generally to semiconductor fabrication technology, and more particularly, to a method for automating workpiece temperature control during high-density plasma (HDP) etch and/or deposition processes
• HDP high-density plasma
• the thickness of a layer formed on the workpiece the thickness of a layer formed on the workpiece, the bulk physical properties of a material formed on the workpiece, and the overall uniformity
• it would be useful to monitor and control the thickness of a dielectric film deposited on the workpiece during an HDP etch and/or deposition process the refractive index of the dielectric film, and thickness variations across the workpiece of the dielectric film
• SPC statistical process control
• variations in the parameters for films deposited and/or etched by HDP processes typically result from poor control of the temperature of the workpiece during HDP processes
• the HDP process typically uses an electromagnetic chuck to clamp, hold, and cool the workpiece during the HDP processing
• the workpiece temperature T is a complicated function of the argon (Ar) sputtering parameters, the workpiece contact w ith the chuck, and the cooling of the chuck by water flow cooling and/or by helium (He) flow cooling, so the co entional approach uses manual control based on a process engineer s experience and potentially fallible judgment
• the present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above
• a method for manufacturing including processing a workpiece in a high-density plasma (HDP) processing step, measuring a parameter characteristic of the HDP processing performed on the workpiece in the HDP processing step, and modeling the characteristic parameter measured The method also includes applying the model to modify at least one HDP control input parameter
• HDP high-density plasma
• a computer-readable, program storage device encoded with instructions that, when executed by a computer, perform a method for manufacturing a workpiece, the method including processing a workpiece in a high-density plasma (HDP) processing step, measuring a parameter characteristic of the HDP processing performed on the workpiece in the HDP processing step and modeling the characteristic parameter measured
• the method also includes applying the model to mod ⁇ f ⁇ at least one HDP control input parameter
• a computer programmed to perform a method of manufacturing including processing a workpiece in a high-density plasma (HDP) processing step, measuring a parameter characteristic of the HDP processing performed on the workpiece in the HDP
• HDP processing step and modeling the characteristic parameter measured
• the method also includes apphing the model to modify at least one HDP control input parameter
• Figures 1-7 schematically illustrate various embodiments of a method for manufacturing according to the present invention, and, more particularly
• Figure 1 schematically illustrates a method for fabricating a semiconductor device practiced in accordance with the present invention
• FIG. 2 schematically illustrates workpieces being processed using a high-density plasma (HDP) processing tool, using a plurality of control input signals, in accordance with the present invention
• Figures 3-4 schematically illustrate one particular embodiment of the process and tool in Figure 2
• Figure 5 schematically illustrates one particular embodiment of the method of Figure 1 as may be practiced with the process and tool of Figures 3-4
• Figure 6 schematically illustrates conventional HDP deposition of a film on high aspect ratio metal lines
• FIG. 7 schematically illustrates an HDP deposition of a film on high aspect ratio metal lines in accordance with various embodiments of the present invention While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and are herein described in detail It should be understood, however, that the description herein of specific embodiments is not intended to limit the invention to the particular forms disclosed, but on the contrary the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims
• Figure 1 illustrates one particular embodiment of a method 100 practiced in accordance with the present invention
• Figure 2 illustrates one particular apparatus 200 with which the method 100 ma be practiced
• the method 100 shall be disclosed in the context of the apparatus 200
• the invention is not so limited and admits wide variation, as is discussed further below
• the HDP processing tool 210 may be any HDP processing tool known to the art, such as a Novellus HDP tool, provided it includes the requisite control capabilities
• the HDP processing tool 210 includes an HDP processing tool controller 215 for this purpose
• the nature and function of the HDP processing tool controller 215 will be implementation specific
• the HDP processing tool controller 215 may control HDP control input parameters such as HDP recipe control input parameters
• the HDP recipe control input parameters may include HDP control input parameters for water flow cooling, helium flow cooling, argon sputtering, electrostatic chuck clamping voltage, and the like
• Four workpieces 205 are shown in Figure 2, but the lot of workpieces or wafers, i e , the "wafer lot,” may be any practicable number of wafers from one to any finite number
• the method 100 begins, as set forth in box 120, by measuring a parameter characteristic of the HDP processing performed on the workpiece 205 in the HDP processing tool 210
• a parameter characteristic of the HDP processing performed on the workpiece 205 in the HDP processing tool 210 The nature, identity, and measurement of characteristic parameters will be largely implementation specific and even tool specific For instance, capabilities for monitoring process parameters vary, to some degree, from tool to tool Greater sensing capabilities may permit wider latitude in the characteristic parameters that are identified and measured and the manner in which this is done Conversely, lesser sensing capabilities may restrict this latitude
• the Novellus HDP tool reads the temperature of a workpiece 205, and/or an average of the temperatures of the workpieces 205 in a lot, using a pyrometer, and the pyrometer needs to know the emissivity of the workpiece 205 and/or workpieces 205 being read, but this emissivity may vary from wafer to wafer
• the Novellus HDP tool pyrometer typically does not feedback the temperature information to the Novellus HD
• the HDP process characteristic parameters are measured and/or monitored by tool sensors (not shown)
• the outputs of these tool sensors are transmitted to a computer system 230 over a line 220
• the computer system 230 analyzes these sensor outputs to identify the characteristic parameters
• the method 100 proceeds by modeling the measured and identified characte ⁇ stic parameter, as set forth in box 130
• the computer system 230 in Figure 2 is, in this particular embodiment programmed to model the characteristic parameter The manner in which this modeling occurs will be implementation specific
• a database 235 stores a plurality of models that might potentially be applied, depending upon which characteristic parameter is identified This particular embodiment therefore, requires some a priori knowledge of the characte ⁇ stic parameters that might be measured
• the computer system 230 then extracts an appropriate model from the database 235 of potential models to apply to the identified characteristic parameters If the database 235 does not include an appropriate model, then the characteristic parameter may be ignored, or the computer system 230 may attempt to develop one, if so programmed
• the database 235 may be stored on any kind of computer-readable, program storage medium, such as an optical disk 240, a floppy disk 245, or a hard disk drive (not shown) of the computer system 230
• the database 235 may also be stored on a separate computer system (not shown) that interfaces with the computer system 230
• Modeling of the identified characteristic parameter may be implemented differently in alternative embodiments
• the computer system 230 may be programmed using some form of artificial intelligence to analyze the sensor outputs and controller inputs to develop a model on-the-fly in a real-time implementation This approach might be a useful adjunct to the embodiment illustrated in Figure 2, and discussed above, where characteristic parameters are measured and identified for which the database 235 has no appropriate model
• the method 100 of Figure 1 then proceeds by applying the model to modify an HDP control input parameter, as set forth in box 140 Depending on the implementation, applying the model may yield either a new value for the HDP control input parameter or a correction to the existing HDP control input parameter The new HDP control input is then formulated from the value yielded by the model and is transmitted to the HDP processing tool controller 215 over the line 220 The HDP processing tool controller 215 then controls subsequent HDP process operations in accordance with the new HDP control inputs
• Some alternative embodiments may employ a form of feedback to improve the modelmg of characteristic parameters
• the implementation of this feedback is dependent on several disparate facts, including the tool's sensing capabilities and economics
• One technique for doing this would be to monitor at least one effect of the model's implementation and update the model based on the effect(s) monitored
• the update may also depend on the model For instance, a linear model may require a different update than would a non-linear model, all other factors being the same
• the acts set forth in the boxes 120-140 in Figure 1 are, in the illustrated embodiment, software-implemented, m whole or in part
• some features of the present invention are implemented as instructions encoded on a computer-readable, program storage medium
• the program storage medium may be of any type suitable to the particular implementation
• the program storage medium will typically be magnetic, such as the floppy disk 245 or the computer 230 hard disk drive (not shown), or optical, such as the optical disk 240
• the computer may be a desktop computer, such as the computer 230
• the computer might alternatively be a processor embedded in the HDP processing tool 210
• the computer might also be a laptop a workstation, or a mainframe in various other embodiments
• the scope of the invention is not limited by the type or nature of the program storage medium or computer with which embodiments of the invention might be implemented
• processing refers to the act ⁇ on(s) and processes of a computer system, or similar electronic and/or mechanical computing device, that manipulates and transforms data, represented as physical (electromagnetic) quantities within the computer system's registers and/or memories, into other data similarly represented as physical quantities within the computer system's memories and/or registers and/or other such information storage, transmission and/or display devices
• FIG. 3-4 An exemplary embodiment 300 of the apparatus 200 in Figure 2 is illustrated in Figures 3-4, in which the apparatus 300 comprises a portion of an Advanced Process Control ("APC") system
• Figures 3-4 are conceptualized, structural and functional block diagrams respectively, of the apparatus 300
• a set of processing steps is performed on a lot of wafers 305 on an HDP processing tool 310
• the wafers 305 are processed on a run-to-run basis
• process adjustments are made and held constant for the duration of a run, based on run-level measurements or averages
• a "run" may be a lot, a batch of lots, or even an individual wafer
• the wafers 305 are processed by the HDP processing tool 310 and various operations in the process are controlled by a plurality of HDP control input signals on a line 320 between the HDP processing tool 310 and a workstation 330
• Exemplary HDP control inputs for this embodiment might include a water flow cooling signal, a helium flow cooling signal, and an argon sputtering signal, an electrostatic chuck clamping voltage signal, and the like
• the semiconductor wafers 305 being processed in the HDP processing tool 310 is examined in a review station 317
• the HDP control inputs generally affect the temperature of the semiconductor wafers 305 and, hence, the variability and properties of the dielectric film etched/deposited by the HDP processing tool 310 on the wafers 305
• the HDP control inputs on the line 320 are modified for a subsequent run of a lot of wafers 305
• Modifying the control signals on the line 320 is designed to improve the next process step m the HDP processing tool 310
• the modification is performed in accordance with one particular embodiment of the method 100 set forth in Figure 1, as described more fully below
• the relevant HDP control input signals for the HDP processing tool 310 are updated, the HDP control input signals with new settings are used for a subsequent run of semiconductor devices Referring now to both Figures 3 and 4, the HDP processing tool 310 communicates with a manufacturing framework
• the machine interface 430 receives commands, status events, and collected data from the equipment interface 410 and forwards these as needed to other APC components and event channels In tum, responses from APC components are received by the machine interface 430 and rerouted to the equipment interface 410
• the machine interface 430 also reformats and restructures messages and data as necessary
• the machine interface 430 supports the startup/shutdown procedures within the APC System Manager 440 It also serves as an APC data collector, buffering data collected by the equipment interface 410, and emitting appropriate data collection signals
• the APC system is a factory-wide software system, but this is not necessary to the practice of the invention
• the control strategies taught by the present invention can be applied to virtually any semiconductor HDP processing tool on a factory floor Indeed, the present invention may be simultaneously employed on multiple HDP processing tools in the same factory or in the same fabrication process
• the APC framework permits remote access and monitoring of the process performance Furthermore by utilizing the APC framework, data storage can be more convenient, more flexible, and less expensive than data storage on
• the illustrated embodiment deploys the present invention onto the APC framework utilizing a number of software components
• a computer script is written for each of the semiconductor HDP processing tools involved in the control system
• the semiconductor HDP processing tool When a semiconductor HDP processing tool m the control system is started in the semiconductor manufacturing fab, the semiconductor HDP processing tool generally calls upon a script to initiate the action that is required by the HDP processing tool controller
• the control methods are generally defined and performed using these scripts
• the development of these scripts can comprise a significant portion of the development of a control system
• Figure 5 illustrates one particular embodiment 500 of the method 100 in Figure 1
• the method 500 may be practiced with the apparatus 300 illustrated in Figures 3-4 but the invention is not so limited
• the method 500 may be practiced with any apparatus that may perform the functions set forth in Figure 5
• the method 100 in Figure 1 be practiced in embodiments alternative to the method 500 in Figure 5
• the method 500 begins with processing a lot of w afers 305 through an HDP processing tool 310, as set forth in box 510
• the HDP processing tool 310 has been initialized for processing by the APC system manager 440 through the machine interface 430 and the equipment interface 410
• the APC system manager script is called to initialize the HDP processing tool 310
• the script records the identification number of the HDP processing tool 310 and the lot number of the wafers 305
• the identification number is then stored against the lot number in a data store 360
• the rest of the script such as the APCData call and the Setup and StartMachine calls, are formulated with blank or dummy data in order to force the machine to use default settings
• the initial setpoints for HDP control are provided to the HDP processing tool controller 315 over the line 320
• These initial setpoints may be determined and implemented in any suitable manner known to the art
• HDP controls are implemented by control threads Each control thread acts like a separate controller and is differentiated by various process conditions
• the control threads are separated by a combination of different conditions These conditions may include, for example, the semiconductor HDP processing tool 310 currently processing the wafer lot, the semiconductor product, the semiconductor manufacturing operation, and one or more of the semiconductor processing tools (not shown) that previously processed the semiconductor wafer lot
• Control threads are separated because different process conditions affect the HDP error differently
• the HDP error can become a more accurate portrayal of the conditions in which a subsequent semiconductor wafer lot in the control thread will be processed Since the error measurement is more relevant changes to the HDP control input signals based upon the error will be more appropriate
• the control thread for the HDP control scheme depends upon the current HDP processing tool, current operation, the product code for the current lot, and the identification number at a previous processing step
• the first three parameters are generally found in the context information that is passed to the script from the HDP processing tool 310
• the fourth parameter is generally stored when the lot is previously processed Once all four parameters are defined, they are combined to form the control thread name, HDP02 OPER01 PROD01 HDP01 is an example of a control thread name
• the control thread name is also stored in correspondence to the wafer lot number in the data store 360
• the initial settings for that control thread are generally retrieved from the data store 360
• the script initializes the control thread assuming that there is no error associated with it and uses the target values of the HDP errors as the HDP control input settings It is preferred that the controllers use the default machine settings as the initial settings By assuming some settings, the HDP errors can be related back to the control settings in order to facilitate feedback control
• the initial settings are stored under the control thread name In this case one or more wafer lots have been processed under the same control thread name as the current wafer lot and have also been measured for HDP error using the review station 317 When this information exists the HDP control input signal settings are retrieved from the data store 360 These settings are then downloaded to the HDP processing tool 310
• the wafers 305 are processed through the HDP processing tool 310 This includes in the embodiment illustrated dielectric film or layer etch and/or deposition and/or etch/deposition
• the wafers 305 are measured on the review station 317 after their HDP processing on the HDP processing tool 310
• the review station 317 examines the wafers 305 after they are processed for a number of errors
• the data generated by the instruments of the review station 317 is passed to the machine interface 430 via sensor interface 415 and the line 320
• the review station script begins with a number of APC commands for the collection of data
• the review station script locks itself in place and activates a data available script This script facilitates the actual transfer of the data from the review station 317 to the APC framework Once the transfer is completed the script exits and unlocks the review station script
• the interaction with the review station 317 is then generally complete
• the data generated by the review station 317 should be preprocessed for use Review stations, such as KLA review stations, provide the control algorithms for measuring
• preprocessing may include outlier rejection
• Outlier rejection is a gross error check ensuring that the received data is reasonable in light of the historical performance of the process This procedure involves comparing each of the HDP errors to its corresponding predetermined boundary parameter In one embodiment, even if one of the predetermined boundaries is exceeded, the error data from the entire semiconductor wafer lot is generally rejected
• Preprocessing may also smooth the data, which is also known as filtering Filtering is important because the error measurements are subject to a certain amount of randomness, such that the error significantly deviates in value Filtering the review station data results in a more accurate assessment of the error in the HDP control input signal settings
• filtering Filtering is important because the error measurements are subject to a certain amount of randomness, such that the error significantly deviates in value Filtering the review station data results in a more accurate assessment of the error in the HDP control input signal settings
• the HDP control scheme utilizes a filtering procedure known as an Exponentially-Weighted Moving Average (“EWMA”) filter, although other filtering procedures can be utilized in this context
• Equation (1) One embodiment for the EWMA filter is represented by Equation (1)
• AVG N W * M C + (1-W) * AVGp (1) where AVG N ⁇ the new EWMA average,
• W ⁇ a weight for the new average (AVG ⁇ ), M c ⁇ the current measurement; and AVGp ⁇ the previous EWMA average.
• the weight is an adjustable parameter that can be used to control the amount of filtering and is generally between zero and one.
• the weight represents the confidence in the accuracy of the current data point. If the measurement is considered accurate, the weight should be close to one. If there were a significant amount of fluctuations in the process, then a number closer to zero would be appropriate.
• the first technique uses the previous average, the weight, and the current measurement as described above.
• advantages of utilizing the first implementation are ease of use and minimal data storage.
• One of the disadvantages of utilizing the first implementation is that this method generally does not retain much process information.
• the previous average calculated in this manner would be made up of every data point that preceded it, which may be undesirable.
• the second technique retains only some of the data and calculates the average from the raw data each time.
• the manufacturing environment in the semiconductor manufacturing fab presents some unique challenges.
• the order that the semiconductor wafer lots are processed through an HDP processing tool may not correspond to the order in which they are read on the review station. This could lead to the data points being added to the EWMA average out of sequence.
• Semiconductor wafer lots may be analyzed more than once to verify the error measurements. With no data retention, both readings would contribute to the EWMA average, which may be an undesirable characteristic.
• some of the control threads may have low volume, which may cause the previous average to be outdated such that it may not be able to accurately represent the error in the HDP control input signal settings.
• the HDP processing tool controller 315 uses limited storage of data to calculate the EWMA filtered error, i.e., the first technique.
• Wafer lot data including the lot number, the time the lot was processed, and the multiple error estimates, are stored in the data store 360 under the control thread name.
• the stack of data is retrieved from data store 360 and analyzed.
• the lot number of the current lot being processed is compared to those in the stack. If the lot number matches any of the data present there, the error measurements are replaced. Otherwise, the data point is added to the current stack in chronological order, according to the time periods when the lots were processed. In one embodiment, any data point within the stack that is over 48 hours old is removed.
• the data is collected and preprocessed, and then processed to generate an estimate of the current errors in the HDP control input signal settings.
• the data is passed to a compiled Matlab® plug-in that performs the outlier rejection criteria described above.
• the inputs to a plug-in interface are the multiple error measurements and an array containing boundary values.
• the return from the plug-in interface is a single toggle variable. A nonzero return denotes that it has failed the rejection criteria, otherwise the variable returns the default value of zero and the script continues to process.
• the data is passed to the EWMA filtering procedure.
• the controller data for the control thread name associated with the lot is retrieved, and all of the relevant operation upon the stack of lot data is carried out. This includes replacing redundant data or removing older data.
• These arrays are fed into the EWMA plug-in along with an array of the parameter required for its execution
• the return from the plug-in is comprised of the six filtered error values
• data preprocessing includes measuring a characteristic parameter in an HDP operation, such as workpiece 305 temperature, arising from HDP processing control of the HDP processing tool 310, as set forth in box 520 .
• a characteristic parameter in an HDP operation such as workpiece 305 temperature
• potential characteristic parameters may be identified by characteristic data patterns or may be identified as known consequences to modifications to critical dimension control
• the example of how changes in temperature affect deposition variability of the etch/deposited dielectric film given above falls into this latter category
• the next step in the control process is to calculate the new settings for the HDP processing tool controller 315 of the HDP processing tool 310
• the previous settings for the control thread corresponding to the current wafer lot are retrieved from the data store 360 This data is paired along with the current set of HDP errors
• the new settings are calculated by calling a compiled Matlab® plug-in This application incorporates a number of inputs, performs calculations in a separate execution component, and returns a number of outputs to the mam script
• the inputs of the Matlab® plug-in are the HDP control input signal settings, the review station errors, an array of parameters that are necessary for the control algorithm, and a currently unused flag error
• the outputs of the Matlab® plug-in are the new controller settings calculated in the plug-in according to the controller algorithm described above
• a HDP process engineer or a control engineer who generally determines the actual form and extent of the control action, can set the parameters They include the threshold values, maximum step sizes, controller weights, and target values
• the script stores the setting in the data store 360 such that the HDP processing tool 310 can retrieve them for the next wafer lot to be processed
• the calculation of new settings includes, as set forth in box 530, modeling the identified characteristic parameter
• This modeling may be performed by the Matlab® plug-m
• the models are stored in a database 335 accessed by a machine interface 430
• the database 335 may reside on the workstation 330, as shown, or some other part of the APC framework
• the models might be stored in the data store 360 managed by the APC system manager 440 in alternative embodiments
• the model will generally be a mathematical model, i e , an equation describing how the change(s) in HDP recipe control(s) affects the HDP performance and the dielectric film properties such as deposition uniformity, film thickness variation across the wafer, film refractive index, and the like
• the particular model used will be implementation specific, depending upon the particular HDP processing tool 310 and the particular characteristic parameter being modeled Whether the relationship in the model is linear or non-linear will be dependent on the particular parameters involved
• the machine interface 430 retrieves the model from the database 335, plugs in the respective value(s), and determines the necessary change(s) in the HDP recipe control input parameter(s) The change is then communicated by the machine interface 430 to the equipment interface 410 ov er the line 320 The equipment interface 410 then implements the change
• the present embodiment furthermore provides that the models be updated This includes as set forth in boxes 550-560 of Figure 5, monitoring at least one effect of modifving the HDP recipe control input parameters (box 550) and updating the applied model (box 560) based on the effect(s) monitored For instance various aspects of the HDP processing tool 310's operation will change as the HDP processing tool 310 ages By monitoring the effect of the HDP recipe change(s) implemented as a result of the characteristic parameter (e g , workpiece 305 temperature) measurement, the necessary value could be updated to yield superior performance
• the characteristic parameter e g , workpiece 305 temperature
• this particular embodiment implements an APC system
• changes are implemented "between" lots
• the actions set forth in the boxes 520-560 are implemented after the current lot is processed and before the second lot is processed, as set forth in box 570 of Figure 5
• the invention is not so limited
• a lot may constitute any practicable number of wafers from one to several thousand (or practically any finite number) What constitutes a "lot is implementation specific, and so the point of the fabrication process in which the updates occur will vary from implementation to implementation Hierarchical Ordering of Conditions.
• One particular variation of this implementation includes a hierarchical ordering of conditions that constitute a control thread Hierarchical ordering is taught generally, without reference to the present invention, in Application Serial No 09/371,665, filed 8/10/1999, entitled "Method and Apparatus for Performing Run-to-Run Control in a Batch Manufacturing Environment, ' by Anthony J Toprac, William J Campbell, and Christopher A Bode (Attorney Docket No 2000 015200/TT3078)
• the hierarchical ordering of control thread data is related to the strength of the effects that these conditions exert on the control of a manufacturing process
• each wafer lot processed will generally involve a discrete value from each hierarchy, but will contribute process information to multiple hierarchical levels Control of each wafer lot will use the lowest hierarchical level (the second level being lower than the first level) for which there is previous process metrology information Generally, the processing of each wafer lot will add information to each hierarchical level
• the HDP control inputs of the HDP processing tool used in a process may be defined as the first level of a hierarchical ordering of control thread data
• the HDP control inputs of a previous operation may be the second most influential factor on the HDP control inputs of the present process, which can be defined as the second level of the hierarchical ordering of control thread data
• the HDP control inputs relating to a similar product type may be the third most influential factor on the HDP control inputs of the present process, which can be defined as the third level of the hierarchical ordering of control thread data
• Further levels of the hierarchical ordering of control thread data can be defined using other similarities between previous processes and a current process
• an automatic spawning of control threads can be implemented in one embodiment, initially, the HDP control inputs for a plurality of semiconductor lots are placed into a single control thread When sufficient data is present to prove a statistically significant difference between lots belonging to different hierarchical levels, a process controller splits the initial control thread into two control threads As more data is obtained, new control threads that represent different hierarchical levels are generated
• the modeled characteristic parameter may be one factor defining or ordering the hierarchy as described above
• one control thread is used to run a manufacturing process on a lot of semiconductor devices, much as is described above for Figures 3-5
• Metrology process data is acquired and HDP control input errors are calculated
• the HDP control inputs for the next process run are modified on a run-to-run basis, based upon errors detected from a previous manufacturing run
• new HDP control input settings are determined and an appropriate bin in a hierarchical level is filled with data, effectively creating new control threads
• Figure 6 schematically illustrates conventional HDP deposition of a film on high aspect ratio metal lines
• a structure layer 600 of a workpiece (such as a semiconducting wafer) has two conducting lines 605 (such as metal lines) formed thereon
• the conducting lines 605 may be parallel bit and/or digit lines on a DRAM chip
• the conducting lines 605 may have a thickness t meta) in a range of approximately 3000 A to 7000 A, for example
• An opening and/or space 610 may be formed between the conducting lines 605, the space 610 having a width w in a range of approximately 2000 A to 4000 A, for example
• the space 610 may have sides 61 OS and a bottom 610B
• the aspect ratio r of the space 610 may be give by the thickness t raeta
• divided by the width w r t meta
• the conducting lines 605 may be electrically isolated from each other by a dielectric film 615, formed of silicon dioxide (S ⁇ 0 2 ), for example
• the dielectric film 615 may have a thickness t recipe ⁇ m in a range of approximately 5000 A to 10000 A, for example Conventional HDP etch/deposition of S1O7, using silane (S ⁇ H 4 ) in the presence of molecular oxygen (0 2 ) and an argon (Ar) sputter (so that the S ⁇ 0 2 is directlv deposited and then sputtered by the Ar), without the benefit of automated HDP workpiece temperature control, such as that provided by various illustrative embodiments of the present invention, can lead to irregularities, as shown in Figure 6, and to inadequate step coverage of spaces 610 having high aspect ratios r
• sidewall coverage is non-uniform, with protuberances 620 and scant or no coverage on portions of the sides 61 OS and on the bottom 610B, with voids between the sides 61 OS
• FIG. 7 schematically illustrates an HDP etch/deposition of an S ⁇ 0 2 film on high aspect ratio metal lines, in accordance with various embodiments of the present invention
• a structure layer 700 of a workpiece such as a semiconducting wafer
• the conducting lines 705 may be parallel bit and/or digit lines on a DRAM chip
• the conducting lines 705 may have a thickness t meta ⁇ in a range of approximately 3000 A to 7000 A, for example
• An opening and/or space 710 may be formed between the conducting lines 705, the space 710 having a width w in a range of approximately 2000 A to 4000 A, for example
• the space 710 may hav e sides 710S and a bottom 710B
• the aspect ratio r of the space 710 may be give by the thickness t raetal divided by the width w
• the conducting lines 705 may be electrically isolated from each other by a dielectric film 715, formed of silicon dioxide (S ⁇ 0 2 ), for example
• the dielectric film 715 may have a thickness t fi ⁇ m in a range of approximately 5000 A to 10000 A, for example
• having the benefit of automated HDP workpiece temperature control such as that provided by various illustrative embodiments of the present invention, as shown in Figures 1-5 and as described above, can lead to substantial uniformities, and to adequate step coverage of spaces 710 having high aspect ratios r
• overall coverage is substantially uniform covering sides 71 OS and bottom 71 OB, with no protuberances such as the protuberances 620 in Figure 6, and w ith no voids between the sides 71 OS
• the engineer may be provided with advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format as well as event logging, real-time graphical display of both current processing parameters and the processmg parameters of the entire run, and remote, / e , local site and worldwide, monitoring
• advanced process data monitoring capabilities such as the ability to provide historical parametric data in a user-friendly format as well as event logging, real-time graphical display of both current processing parameters and the processmg parameters of the entire run, and remote, / e , local site and worldwide, monitoring
• critical processmg parameters such as throughput accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like
• This more optimal control of critical processing parameters reduces this variability
• This reduction in variability manifests itself as fewer within-run disparities, fewer run-to-run disparities and fewer tool-to-tool disparities
• This reduction in the number of these disparities that can propagate means fewer deviations in product quality and performance
• a monitoring and diagnostics system
• any of the above-disclosed embodiments of a method of manufacturing according to the present invention enables the monitor and control the thickness of a dielectric film deposited on the workpiece during an HDP etch and/or deposition process, the refractive index of the dielectric film, and thickness variations across the workpiece of the dielectric film, and enables the making of supervisory processing adjustments, either manually and/or automatically, to improve and/or better control the yield Additionally, any of the above-disclosed embodiments of a method of manufacturing according to the present invention enables semiconductor device fabrication with increased device accuracy and precision, increased efficiency and increased device yield, enabling a streamlined and simplified process flow, thereby decreasing the complexity and lowering the costs of the manufacturing process and increasing throughput
• any of the above-disclosed embodiments of a method of manufacturing according to the present invention enables reduced variations in the parameters for films deposited and/or etched by HDP processes, resulting, in part, from better control of the temperature of the workpiece during HDP processes
• an HDP process may use an electromagnetic chuck to clamp, hold, and cool the workpiece during the HDP processing
• the workpiece temperature T may be a complicated function of the argon (Ar) sputtering parameters, the workpiece contact with the chuck, and the cooling of the chuck by water flow cooling and or by helium (He) flow cooling
• any of the above-disclosed embodiments of a method of manufacturing according to the present invention enables automatic control based on modeling of parameters characte ⁇ stic of HDP processing, thereby improving over the conventional approach that uses manual control based on a process engineer's experience and potentially fallible judgment

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• Physics & Mathematics (AREA)
• Engineering & Computer Science (AREA)
• Plasma & Fusion (AREA)
• Chemical & Material Sciences (AREA)
• Analytical Chemistry (AREA)
• Drying Of Semiconductors (AREA)
• Testing Or Measuring Of Semiconductors Or The Like (AREA)

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• 2000
  • 2000-09-20 WO PCT/US2000/025725 patent/WO2001050496A1/en not_active Application Discontinuation
  • 2000-09-20 EP EP00961972A patent/EP1245037A1/en not_active Withdrawn
  • 2000-09-20 KR KR1020027008704A patent/KR20020063616A/ko not_active Application Discontinuation
  • 2000-09-20 JP JP2001550776A patent/JP2003519906A/ja not_active Withdrawn

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