|Publication number||USRE39534 E1|
|Application number||US 10/302,567|
|Publication date||Mar 27, 2007|
|Filing date||Nov 22, 2002|
|Priority date||Mar 3, 1998|
|Publication number||10302567, 302567, US RE39534 E1, US RE39534E1, US-E1-RE39534, USRE39534 E1, USRE39534E1|
|Inventors||David Cooperberg, Richard A. Gottscho, Vahid Vahedi|
|Original Assignee||Lam Research Corporation|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (34), Non-Patent Citations (15), Referenced by (6), Classifications (15), Legal Events (2)|
|External Links: USPTO, USPTO Assignment, Espacenet|
This is a continuation-in-part of U.S. patent application Ser. No. 09/033,997, now U.S. Pat. No. 6,151,532 filed Mar. 3, 1998 in the names of inventors Maria E. Barone, Richard A. Gottscho, and Vahid Vahedi and commonly assigned herewith. It is also related to Applications of a semi-empirical physically based profile simulator, Enhanced process and profile simulator algorithms filed in common date herewith.
1. Field of the Invention
This invention relates to plasma processing of semiconductor devices. In particular, this invention provides a method and apparatus to calibrate a semi-empirical process simulator for predicting the surface profile and the etch or deposition rates that a given plasma process will create.
2. Background Art
Various forms of processing with ionized gases, such as plasma etching and reactive ion etching, are increasing in importance particularly in the area of semiconductor device manufacturing. Of particular interest are the devices used in the etching process.
TCP power controller 106 sets a set point for TCP power supply 110 configured to supply a radio frequency (RF) signal, tuned by a TCP match network 114, in a TCP coil 116 located near plasma chamber 104. A RF transparent window 118 is typically provided to separate TCP coil 116 from plasma chamber 104 while allowing energy to pass from TCP coil 116 to plasma chamber 104.
Bias power controller 108 sets a set point for bias power supply 112 configured to supply a RF signal, tuned by a bias match network 120, to an electrode 122 located within the plasma reactor 104 creating a direct current (DC) bias above electrode 122 which is adapted to receive a substrate 124, such as a semi-conductor wafer, being processed.
A gas supply mechanism 126, such as a pendulum control valve, typically supplies the proper chemistry required for the manufacturing process to the interior of plasma reactor 104. A gas exhaust mechanism 128 removes particles from within plasma chamber 104 and maintains a particular pressure within plasma chamber 104. A pressure controller 130 controls both gas supply mechanism 126 and gas exhaust mechanism 128.
A temperature controller 134 controls the temperature of plasma chamber 104 to a selected temperature setpoint using heaters 136, such as heating cartridges, around plasma chamber 104.
In plasma chamber 104, substrate etching is achieved by exposing substrate 104 124 to ionized gas compounds (plasma) under vacuum. The etching process starts when the gases are conveyed into plasma chamber 104. The RF power delivered by TCP coil 116 and tuned by TCP match network 110 ionizes the gases. The RF power, delivered by electrode 122 and tuned by bias match network 120, induces a DC bias on substrate 124 to control the direction and energy of ion bombardment of substrate 124. During the etching process, the plasma reacts chemically with the surface of substrate 124 to remove material not covered by a photoresistive mask.
Input parameters such as plasma reactor settings are of fundamental importance in plasma processing. The amount of actual TCP power, bias power, gas pressure, gas temperature, and gas flow within plasma chamber 104 greatly affects the process conditions. Significant variance in actual power delivered to plasma chamber 104 may unexpectedly change the anticipated value of other process variable parameters such as neutral and ionized particle density, temperature, and etch rate.
Traditionally, a suite of values of these input parameters suitable for creating a given set of device features has been determined by trial and error. Development of a single process by this empirical approach is costly and time-consuming, requiring treatment of several patterned wafers and subsequent study of the resulting profiles by scanning electron microscopy. Because of the unpredictable way a small change in one input parameter may affect the profile, any modification of the layout—for example, in device dimension, pattern density on the wafer or change in total open area—from one application to another, has often necessitated redevelopment of the process, with the attendant outlay of resources.
Recent advances in device fabrication technology are rendering this approach even more onerous. Decreasing feature sizes demand tighter tolerances on feature dimensions and morphologies, so that the number of trials required to optimize a given process is increasing. The acceleration of wafer diameter growth and the complete redesign of the process involved with an incremental change in diameter have increased the number of times this empirical process must be repeated. The increasing use of devices tailor-made to a specific application also increases the amount of development and optimization activity required.
An alternative, computational approach would derive input parameters from a complete physical description of a plasma process including a plasma model for describing the coupling between the macroscopic input parameters and the macroscopic fluxes, concentrations and energy distributions of the various species in the plasma; and a profile simulator for determining atomistically from the macroscopic fluxes the resulting etch or deposition rate along the wafer surface and calculating the profile evolution therefrom. Ideally, such a physical description of plasma etching and deposition processes would enable the ab initio selection of the macroscopic input parameters appropriate for generating a desired profile on the substrate, eliminating the need for expensive and time-consuming test sequences.
Research in this field has done much to elucidate mechanisms at work in plasma processes, and thus has contributed scaling laws that could frame a physical description. However, notwithstanding the availability of computational means sufficiently powerful to perform the necessary calculations based on known scaling laws, the implementation of such an ab initio approach has been limited by lack of data. For example, the manner in which the values of some coefficients in these laws depend on the particulars of a given process is unknown as yet. In some investigations, determination of the value of such a scaling coefficient consistent with a plasma process defined by a given set of input parameters has been done by comparing a finished profile, created by applying that process, with a simulated profile including one or more of these coefficients as adjustable parameters. Such hindsight evaluation may promote understanding a given coefficient's role in scaling law, but it has not afforded the ability to predict profile evolution for any process defined by a set of input parameters differing from the set used in the experimental process used to derive the value of that coefficient.
Thus, there is a need for a method for calibrating a semi-empirical process simulator for predicting the surface profiles and the etch or deposition rates that a given plasma process will create.
A method and apparatus for calibrating a semi-empirical process simulator used to determine process values in a plasma process for creating a desired surface profile on a process substrate includes providing a test model which captures all mechanisms responsible for profile evolution in terms of a set of unknown surface parameters. A set Sets of test conditions processes is are derived for which the profile evolution is governed by only a limited number of parameters. For each set of test conditions process, model test values are selected and a test substrate is substrates are actually subjected to a the test process processes defined by the test values , thereby creating a test surface profile profiles. The test values are used to generate an approximate profile prediction predictions and are adjusted to minimize the discrepancy between the test surface profile profiles and the approximate profile prediction predictions, thereby providing a final model of the profile evolution in terms of the process values.
Those of ordinary skill in the art will realize that the following description of the present invention is illustrative only and not in any way limiting. Other embodiments of the invention will readily suggest themselves to such skilled persons having the benefit of this disclosure.
A semi-empirical process simulator is a model that seeks to determine the values of input parameters in a plasma process for creating a desired surface profile on a substrate, such as a semi-conducting wafer. Previous calibration schemes used in the process simulator relied on fitting the simulation output to test data gathered near a process window of interest. While useful, this technique is likely to produce more empirical and less physically based models. An alternative approach to calibration is to design sets of experiments in which the results are functions of fewer unknown parameters and for which coefficient values can be derived from best fit to data with greater confidence. The present invention seeks to improve such calibration process by using models that more faithfully capture the actual kinetics governing feature evolution and are likely to yield more accurate profile predictions when using reactor settings which may lie outside a previously calibrated window of operation.
The form of the surface kinetic model for each class of exposed film and class of plasma chemistry may be derived from experimentally observed trends and scalings. Unknown model coefficients however must be calibrated for each specific film and chemistry. A complete surface kinetics model includes equations governing surface coverage and an equation governing etch or deposition rate. Surface reaction rates for the adsorption, desorption, and surface reactions of active neutral species lead to a determination of surface coverage. The mechanisms determining local etch rate will be functions of the computed surface coverage and the ion and neutral flux distributions incident on a feature surface element. Etch mechanisms include physical sputtering due to energetic particle bombardment, chemical assisted sputtering which additionally depends on the surface concentration of one or more etchant species, and thermal etching dependent only on chemical coverages (and surface temperature). Deposition rate is typically defined by the adsorption rate of depositing neutral species.
The general procedure to calibrate a semi-empirical process simulator is illustrated in FIG. 1 1B. Free parameters needed to characterize mechanisms such as thermal etching, ion enhanced etching, physical sputtering, and deposition are determined by performing test processes under conditions where only a limited number of such mechanisms contribute to etch or deposition rates and/or surface profiles. The reduced set of unknown surface parameters can then be determined by best fit to etch rate and/or surface profile data collected under these conditions.
In block 100, a test model which captures all mechanisms responsible for profile evolution in terms of a set of unknown surface parameters is provided. The input parameters include descriptors of the pre-processing state of the substrate (“substrate parameters”) and quantities defining the operation of a given plasma reactor during the test process (“reactor parameters”). The substrate parameters may include, for example, gross dimensions of the substrate, the distribution and dimensions of any features on the surface, and the substrate composition. The reactor parameters may include any of: power levels, gas temperature, gas pressure, inlet gas composition, and length of time the substrate is treated by the plasma process.
In block 102, a set sets of test conditions is process(es) are derived from a subset of the test model defined in block 100 such that only a limited number of unknown surface parameters of the test model defined in block 100 contribute contributes to the etch or deposition rates and the surface profiles. Such test conditions is process(es) are derived by adjusting the value of the input parameters such that some of the surface model parameters to render their effects have negligible on the test processes effect on the etch/deposition rates or surface profile evolution.
For each set of test conditions process(es) defined in block 102, surface model unknown parameter test values are yet to be solved for. In block 104 111, these unknown parameter test values are isolated by eliminating parameters that do not contribute to etch or deposition rates or feature evolution.
The unknown parameter test values of reactor variables and reactor fixedTest process(es) reactor parameters as selected in block 106, are used in block 108, in which a plasma model, containing a mathematical description of the plasma in terms of the reactor parameters for the reactor of interest and coefficients of known or empirically determined value, operates to approximately determine the macroscopic properties of the plasma generated by the test processprocesses. The plasma model characterizes the species of interest only as belonging to a general class of actors, for example, as being either charged particles, and thus attracted to the substrate by any applied bias, or neutral species such as gas molecule and excited radicals. Physical models—for example, Maxwell's and Boltzmann's equations—underlying the functional dependence in terms of governing experimental input parameters of such plasma descriptors as particle fluxes, energy, and angular distributions are well known in the art. (See, e.g., Lieberman and Lichtenberg, Principles of Plasma Discharges and Materials Processing, John Wiley, 1994). Much is known in the art about how these descriptors scale with the input parameters, based on such basic physical models in conjunction with experimental data. However, the absolute value of these fluxes or distributions is not known a priori for a given process. Any such coefficient in the reactor model whose value is unknown is treated by the calibration as an adjustable parameter or is determined experimentally.
Those macroscopic properties of the plasma can alternatively be determined by measuring the flux distribution at the wafer in a laboratory under the set sets of test conditions process(es) as selected in block 104 102.
In block 112, a profile simulator, containing a mathematical model of the time evolution of the substrate surface profile in terms of the substrate parameters, the macroscopic plasma properties, and coefficients of unknown value, uses the results of block 108 and the substrate parameters provided in block 110 to approximately predict the etch or deposition rates and surface profiles on the substrate for each set of test process(es) defined in block 104 . In general, such mathematical model includes a local transport model for calculating fluxes arriving at each point along the substrate surface, a site balance model for calculating the resulting local etch and deposition rates, and a surface advancement algorithm. In a presently preferred specific embodiment of the present invention, a well-known Monte Carlo type approach is used for the local transport.
The site balance model is preferably based on a Langmuir-type model of the kinetics of particle-surface interactions. The model characterizes species that remove material from the substrate as etchants and those that deposit material onto the substrate surface as inhibitors. With reference to
oed0e/dt=ΓeSe(1−0d−θe)−0eks1θ1e−0eke1θ1ekthe0e =0 and
Wherein ke1, ks1, and kth are coefficients, generally of unknown value, associated with the ion-enhanced, physical sputtering and thermal etching mechanisms, respectively. The parameters Γre and Γis are products of ion-enhanced etching and physical sputtering yields, respectively, with ion flux integrated over incident ion energies greater than respective threshold energies, expressed respectively as Eth and Eth, and over all angles,
Γie=∫∫YirφF)Γ1(φ, Fe=∫dF.(F.1/2Esh,s 1/2)∫dφYe(φ,Γe)∫(φ, E) and
This model assumes that the etch yields are products of angular functions and the square root of ion energy, which dependence has been observed experimentally. However, other scaling laws could be used instead. The yield functions only represent functional dependencies, the absolute magnitudes being lumped into the coefficients k's along with other constants.
Expressions for θe and θd can be derived from the steady-state site balance equation on each type of surface present on the substrate, for example the substrate top and trench bottoms and sidewalls. At each point on the surface, the etch rate, ER, can be written:
The coefficients ke2, ks2, kd and kth are yield constants associated with physical sputtering, ion-enhanced etching, thermal etching and ion-induced deposition, respectively. Incorporating the expressions for θe, θd, Γ1s and Γie renders the etch rate in terms of the plasma characteristics and the coefficients k, each of which is in principle an adjustable parameter that can be determined by the calibration.
It is also preferred to employ an analytic scheme for surface advancement so that the fine features can be resolved more accurately. One such scheme known in the art and capable of modeling fine feature aspects, such as sharp corners, is the method of characteristics, also known as the shock-front-tracking algorithm. (See, e.g., S. Hamaguchi, “Modeling of Film Deposition for Microelectronic Applications”, Thin Films, vol. 22, p. 81, S. Rossnagel, ed. Academic Press, San Diego, 1996). Another is the level set approach. (See, e.g., J. A. Sethian, Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge University Press 1996). The shock-front-tracking approach models the surface (i.e., the interface layer between vacuum and solid) as a collection of piecewise continuous line segments, for each of which a rate of movement is calculated. The possibility of each segment's advancing or receding along its normal independently of the movement of the other segments allows for multiple potential solutions for the resulting surface. In order to avoid multiple solutions, these analytic schemes model the points between the line segments as shocks (i.e., discontinuities in the slope), and properly track the movement of the shocks.
Thus, the cumulative output of blocks 104 through 112, particularly of the operation of the plasma model in block 108 and the profile simulator in block 112, which together comprise an initial mathematical surface profile model as specified above, generates a quantitative but approximate prediction of the etch or deposition rates and surface profile(s) created by the test process processes.
In block 114, the test values of the input parameters the test process(es) provided in block 104 are used to provide a test surface profile profiles, created experimentally by subjecting a test substrate substrate(s) to a test process processes, in the reactor of interest, defined by the test values of the input parameters. The etch or deposition rates and surface profile(s) of the test substrate substrates are then measured. In block 116, the etch or deposition rates and surface profile(s) of the test substrate substrates are quantitatively compared with the quantitative approximate prediction predictions resulting from block 112. The difference between the test surface profile(s) and/or etch/deposition rates and the approximate prediction(s) is evaluated according to some criterion, such as an accuracy of range of 10%, applied in block 116.
In general, on the first pass, in which the approximate prediction(s) is computed using preliminary test values of unknown surface parameters, the residual is not sufficiently small to satisfy the criterion, and the calibration procedure advances to block 118, in which the values of the unknown surface parameters are adjusted so as to minimize the discrepancy. The adjusted values are then resubmitted to the profile simulator of block 112 and the plasma model of block 108, for another iteration that render the approximate profile prediction(s) and of the comparison in block 116. Iteration continues until the discrepancy between the test surface profile(s) and the approximate prediction(s) is adequately minimized.
Thus, both block 116 and block 118 effect a calculation of optimum values of the unknown surface parameters test values appearing in the initial mathematical surface profile model. After the surface parameters suitable for the current set of conditions test process(es) are determined, a different set of conditions test process(es) in block 104 is selected from block 102 and its respective surface parameters are determined using the same process of block 104 through block 120 until all surface parameters are determined.
For example, physical sputtering yield, energy scaling, and energy threshold coefficients may be obtained by fitting to etch rate data and final profile SEM (Scanning Electron Micrograph) data collected after wafer processing in a chemically inert plasma such as an argon discharge with the ion flux and energy distribution to the separately measured or modeled. Coefficients representing thermal etch rate may be obtained by best fit to etch rate data collected under conditions for which there is no energetic ion bombardment. If sufficient gas phase reactant density can be achieve the surface will saturate and the dependence of etch rate on adsorption probability can be eliminated leaving saturated etch rate as the only unknown parameter. Patterned features including overhang structures may be etched in order to isolate surface reaction probabilities which determine transport throughout the feature. In order to calibrate models for deposited material, deposition and etch rates on blanket or patterned wafers processed under conditions favoring deposition may be collected. Incomplete film stacks may be used to isolate the mechanisms leading to redeposition. For chemically assisted sputtering, also referred to as ion-neutral synergy etching, data may be collected from wafers processed under conditions where the ratio of ion energy flux to etchant neutral flux is varied. For large (small) enough ion energy flux to etchant neutral flux ratios the etch rate is dependent only on the etchant flux (incident ion energy flux). Model fits to this etch rate and profile data combined with ion and/or neutral flux measurements and/or models can be used to determine unknown coefficients including neutral sticking coefficient, neutral desorption and/or recombination rates, average etch product stoichiometry, and yield per incident ion as a function of energy and angle. For all the sets of experiments above subsets of the complete process chemistry may be used to further isolate and calibrate specific reactions.
In one presently alternative specific embodiment, the approximate profile prediction generated by the profile simulator in block 112 comprises a series of frames, each computed after a sufficiently small time increment, and block 114 only compares frames of the approximate profile prediction that correspond to cumulative exposure times roughly equal to the time of the snapshots in the test surface profile. If the test profile includes multiple snapshots at different exposure times and/or at a different test value of one or more input parameters other than time, block 116 compares each test snapshot with the appropriate frame of the profile prediction and the system operates to minimize the residual over the entire pairwise comparison.
As will now be evident to those of ordinary skill in the art, many configurations departing from the procedure shown in FIG. 1 1B fall within the scope of the invention. For example,
The operation of the illustrated system is directed by a central-processing unit (“CPU”) 406. The user interacts with the system using a keyboard 408 and a position-sensing device (e.g., a mouse) 410. The output of either device can be used to designate information or select particular areas of a screen display 412 to direct functions to be performed by the system.
The main memory 404 contains a group of modules that control the operation of CPU 406 and its interaction with the other hardware components. An operating system 414 directs the execution of low-level, basic system functions such as memory allocation, file management and operation of mass storage devices 402. At a higher level, an analysis module 416, implemented as a series of stored instructions, directs execution of the primary functions performed by the invention, as discussed below. Instructions defining a user interface 418 allow straightforward interaction over screen display 412. User interface 418 generates words or graphical images on display 412 to prompt action by the user, and accepts user commands from keyboard 408 and/or position-sensing device 410. The main memory 404 also includes one or more database 420, in general containing any of the test or process values of input parameters including input variables, the desired profile, the test surface profile and rough preliminary test values in the plasma model and profile simulator.
It must be understood that although the modules of main memory 404 have been described separately, this is for clarity of presentation only; so long as the system performs all necessary functions, it is immaterial how they are distributed within the system and its programming architecture.
The test surface profile(s) is produced experimentally, as is well known in the art, by subjecting one or more test substrates to a test process(es) in a plasma reactor and measuring the resulting surface profile using, for example, scanning electron microscopy. The desired and test surface profiles may be supplied to the hardware system in electronic format or as graphic hardcopy, in which case the image(s) is processed by a digitizer 398 before numerical comparison with the approximate prediction. The digitized profile is sent as bitstreams on the bus 400 to a database 420 of the main memory 404. The test surface profile may be stored in the mass storage device 402 as well as in database 420.
As noted above, execution of the key tasks associated with the present invention is directed by analysis module 416, which governs the operation of CPU 406 and controls its interaction with main memory 404 in performing the blocks necessary to provide a final mathematical surface profile model including calibrated optimum test values in the initial surface profile model; and, by further processing based on the final surface profile model and a desired surface profile, to determine process values of one or more input variables governing a plasma process sequence appropriates appropriate for creating the desired profile on a process substrate; or, by inserting process values of the input variables into the final mathematical model, to predictively calculate a process surface profile to be created on a process substrate by a plasma process sequence defined by the process values.
In particular, the hardware system depicted in
Turning now to FIG. 1 1B, by executing the plasma modeling and profile simulation of blocks 108 and 112, respectively, the module 416 establishes the initial mathematical surface model predicting the profile created by the test process. In block 116, the module 416 (
The analysis module uses these optimum values of the input variables for computation of process values, which can be loaded into a plasma reactor for production of a device including the desired profile, or for profile prediction as described above.
It will therefore be seen that the foregoing represents a highly extensible and advantageous approach to plasma processing of semiconductor devices. The terms and expressions employed herein are used as terms of description and not of limitations, and there is no intention, in the use of such terms and expressions, of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. For example, the various modules of the invention can be implemented on a general-purpose computer using appropriate software instructions, or on a network of computers, or a multiprocessor computer or as hardware circuits, or as mixed hardware-software combinations (wherein, for example, plasma modeling and profile simulation are performed by dedicated hardware components).
While embodiments and applications of this invention have been shown and described, it would be apparent to those skilled in the art having the benefit of this disclosure that many more modifications than mentioned above are possible without departing from the inventive concepts herein. The invention, therefore, is not to be restricted except in the spirit of the appended claims.
|Cited Patent||Filing date||Publication date||Applicant||Title|
|US4663513||Nov 26, 1985||May 5, 1987||Spectra-Physics, Inc.||Method and apparatus for monitoring laser processes|
|US5107105||Nov 7, 1990||Apr 21, 1992||Ricoh Company, Ltd.||Method for measuring an unknown parameter of a thin film and apparatus therefor|
|US5225740||Mar 26, 1992||Jul 6, 1993||General Atomics||Method and apparatus for producing high density plasma using whistler mode excitation|
|US5270222||Dec 31, 1990||Dec 14, 1993||Texas Instruments Incorporated||Method and apparatus for semiconductor device fabrication diagnosis and prognosis|
|US5290382||Dec 13, 1991||Mar 1, 1994||Hughes Aircraft Company||Methods and apparatus for generating a plasma for "downstream" rapid shaping of surfaces of substrates and films|
|US5399229||May 13, 1993||Mar 21, 1995||Texas Instruments Incorporated||System and method for monitoring and evaluating semiconductor wafer fabrication|
|US5422139||May 25, 1993||Jun 6, 1995||Balzers Aktiengesellschaft||Method for a reactive surface treatment of a workpiece and a treatment chamber for practicing such method|
|US5642296||Jul 29, 1993||Jun 24, 1997||Texas Instruments Incorporated||Method of diagnosing malfunctions in semiconductor manufacturing equipment|
|US5654903 *||Nov 7, 1995||Aug 5, 1997||Lucent Technologies Inc.||Method and apparatus for real time monitoring of wafer attributes in a plasma etch process|
|US5679599||Jun 22, 1995||Oct 21, 1997||Advanced Micro Devices, Inc.||Isolation using self-aligned trench formation and conventional LOCOS|
|US5711843 *||Feb 21, 1995||Jan 27, 1998||Orincon Technologies, Inc.||System for indirectly monitoring and controlling a process with particular application to plasma processes|
|US5737496 *||Jun 6, 1995||Apr 7, 1998||Lucent Technologies Inc.||Active neural network control of wafer attributes in a plasma etch process|
|US5819073||Sep 6, 1996||Oct 6, 1998||Kabushiki Kaisha Toshiba||Simulation method and simulator|
|US5861752||Apr 15, 1997||Jan 19, 1999||Klick; Michael||Method and apparatus for determining of absolute plasma parameters|
|US5866437 *||Dec 5, 1997||Feb 2, 1999||Advanced Micro Devices, Inc.||Dynamic process window control using simulated wet data from current and previous layer data|
|US5869402||May 10, 1995||Feb 9, 1999||Matsushita Electric Industrial Co., Ltd.||Plasma generating and processing method and apparatus thereof|
|US5871805||Apr 8, 1996||Feb 16, 1999||Lemelson; Jerome||Computer controlled vapor deposition processes|
|US5900633||Dec 15, 1997||May 4, 1999||On-Line Technologies, Inc||Spectrometric method for analysis of film thickness and composition on a patterned sample|
|US5933345||May 6, 1996||Aug 3, 1999||Pavilion Technologies, Inc.||Method and apparatus for dynamic and steady state modeling over a desired path between two end points|
|US5949678 *||Dec 15, 1994||Sep 7, 1999||Telefonaktiebolaget Lm Ericsson||Method for monitoring multivariate processes|
|US5963710||Mar 27, 1997||Oct 5, 1999||Fujitsu Limited||Method and apparatus for automatically generating internal representation|
|US5966312 *||Nov 7, 1997||Oct 12, 1999||Advanced Micro Devices, Inc.||Method for monitoring and analyzing manufacturing processes using statistical simulation with single step feedback|
|US5966527 *||Oct 28, 1996||Oct 12, 1999||Advanced Micro Devices, Inc.||Apparatus, article of manufacture, method and system for simulating a mass-produced semiconductor device behavior|
|US6041734||Dec 1, 1997||Mar 28, 2000||Applied Materials, Inc.||Use of an asymmetric waveform to control ion bombardment during substrate processing|
|US6110214||Aug 23, 1996||Aug 29, 2000||Aspen Technology, Inc.||Analyzer for modeling and optimizing maintenance operations|
|US6136388||Dec 1, 1997||Oct 24, 2000||Applied Materials, Inc.||Substrate processing chamber with tunable impedance|
|US6151532 *||Mar 3, 1998||Nov 21, 2000||Lam Research Corporation||Method and apparatus for predicting plasma-process surface profiles|
|US6162709||Jan 11, 2000||Dec 19, 2000||Applied Materials, Inc.||Use of an asymmetric waveform to control ion bombardment during substrate processing|
|US6301510||Jun 30, 2000||Oct 9, 2001||Lam Research Corporation||Method and apparatus to calibrate a semi-empirical process simulator|
|US6577915 *||Jun 30, 2000||Jun 10, 2003||Lam Research Corporation||Applications of a semi-empirical, physically based, profile simulator|
|US6804572 *||Jun 30, 2000||Oct 12, 2004||Lam Research Corporation||Enhanced process and profile simulator algorithms|
|US6830650||Jul 12, 2002||Dec 14, 2004||Advanced Energy Industries, Inc.||Wafer probe for measuring plasma and surface characteristics in plasma processing environments|
|US20040107906||Aug 22, 2003||Jun 10, 2004||Applied Materials, Inc.||Plasma immersion ion implantation apparatus including a plasma source having low dissociation and low minimum plasma voltage|
|EP0602855A1||Dec 3, 1993||Jun 22, 1994||AT&T Corp.||Active neural network control of wafer attributes in a plasma etch process|
|1||Bailey D. III et al., Scaling of Si and GaAs Trench Etch Rates with Aspect Ratio, Feature Width, and Substrate Temperature, J. Vac. Sci. Technol. B 13(1), Jan./Feb. 1995, pp. 92-104.|
|2||Barone et al., "Molecular Dynamics Simulations of Plasma-Surface Chemistry", Plasma Sources Sci. Technol. 5, 1996, pp. 187-192.|
|3||Chang et al., "Plasma-Surface Kinetics and Feature Profile Evolution in Chlorine Etching of Polysilicon", J. Vac. Sci. Technol. A 16(1), Jan./Feb. 1998, pp. 217-224.|
|4||Gottscho et al., "Microscopic Uniformity in Plasma Etching", J. Vac. Sci. Technol. B 10(5), Sep./Oct. 1992, pp. 2133-2147.|
|5||Hamaguchi et al., "Liner Conformality in Ionized Magnetron Sputter Metal Deposition Processes", J. Vac. Sci. Technol. B 14(4), Jul./Aug. 1996, pp. 2603-2608.|
|6||Hamaguchi, "Mathematical Methods for Thin Film Deposition Simulations", Thin Films, vol. 22, pp. 81-115.|
|7||Han et al., "Profile Modeling of High Density Plasma Oxide Etching", J. Vac. Sci. Technol. B 13(4) Jul./Aug. 1995, pp. 1893-1899.|
|8||Li et al., "Simulation and Experimental Analysis of Planarization of Refractory Metals Using A Multi-Step Sputter/Sputter Etch Process", Journal of Electronic Materials, vol. 23, No. 11, 1994, pp. 1215-1220.|
|9||Liao et al., "Simulations of Metal Thin Film Thermal Flow Processes", Vac. Sci. Technol. B 14(4), Jul./Aug. 1996, pp. 2615-2622.|
|10||Moyne et al., "Adaptive Extensions to a Multibranch Run-to-Run Controller for Plasma Etching", J. Vac. Sci. Technol. A 13(3), May/Jun. 1995, pp. 1787-1791.|
|11||Rietman, "Neural Networks in Plasma Processing", J. Vac. Sci. Technol. B 14(1), Jan./Feb. 1996, pp. 504-510.|
|12||Singh et al., "The Effect of Surface Transport on the Evolution of Film Microstructure in Plasma Etching and Deposition", Elsevier Science, 1997, pp. 37-40.|
|13||Thallikar et al., "Experimental and Simulation Studies of Thermal Flow of Borophosphosilicate and Phosphosilicate Glasses", J. Vac. Sci. Technol. B 13(4), Jul./Aug. 1995, pp. 1875-1878.|
|14||Vahedi et al., "Capacitive RF Discharges Modelled by Particle-in-Cell Monte Carlo Simulation I: Analysis of Numerical Techniques", Plasma Sources Sci. Technol. , 1993, pp. 261-272.|
|15||Vahedi et al., "Capacitive RF Discharges Modelled by Particle-in-Cell Monte Carlo Simulation II: Comparisons with Laboratory Measurements of Electron Energy Distribution Functions", 1993, Plasma Sources Sci. Technol. 2, pp. 273-278.|
|Citing Patent||Filing date||Publication date||Applicant||Title|
|US7571078 *||May 20, 2004||Aug 4, 2009||Sap Ag||Time dependent process parameters for integrated process and product engineering|
|US7895547||May 1, 2008||Feb 22, 2011||International Business Machines Corporation||Test pattern based process model calibration|
|US8160728 *||Sep 14, 2009||Apr 17, 2012||The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration||Methods of determining complete sensor requirements for autonomous mobility|
|US20050261888 *||May 20, 2004||Nov 24, 2005||Martin Chen||Time dependent process parameters for integrated process and product engineering|
|US20090276736 *||May 1, 2008||Nov 5, 2009||International Business Machines Corporation||Test Pattern Based Process Model Calibration|
|US20110066284 *||Sep 14, 2009||Mar 17, 2011||Nasa Hq's.||Methods of determining complete sensor requirements for autonomous mobility|
|U.S. Classification||700/28, 700/30, 702/81, 438/14, 702/104, 700/52, 438/5, 700/31, 700/34, 703/6, 700/47, 702/85, 703/22, 700/121|
|Apr 9, 2009||FPAY||Fee payment|
Year of fee payment: 8
|Apr 9, 2013||FPAY||Fee payment|
Year of fee payment: 12