Adaptive Control of aAdaptive Control of aMulti-Bias S-Parameter Measurement Multi-Bias S-Parameter Measurement

SystemSystem

Dr Cornell van NiekerkDr Cornell van Niekerk

Microwave Components GroupMicrowave Components Group

University of StelleboschUniversity of Stellebosch

South AfricaSouth Africa

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Presentation OverviewPresentation Overview

• Introduction & Background InformationIntroduction & Background Information

• Equivalent Circuit Non-Linear ModelingEquivalent Circuit Non-Linear Modeling

• Adaptive Algorithm RequirementsAdaptive Algorithm Requirements

• Defining the Safe Operating Area (SOA) of a DeviceDefining the Safe Operating Area (SOA) of a Device

• S-Parameter Driven Adaptive Measurement AlgorithmsS-Parameter Driven Adaptive Measurement Algorithms

• DC Driven Adaptive Measurement AlgorithmsDC Driven Adaptive Measurement Algorithms

• Results & ConclusionsResults & Conclusions

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Introduction & BackgroundIntroduction & Background

• Interest is in algorithms required for construction of device CAD modelsInterest is in algorithms required for construction of device CAD models

• Focus is on small-signal equivalent circuit extraction proceduresFocus is on small-signal equivalent circuit extraction procedures

• Have developed robust multi-bias extraction algorithms for GaAs FETsHave developed robust multi-bias extraction algorithms for GaAs FETs

• Focus is shifting to bulk Si MOSFET devicesFocus is shifting to bulk Si MOSFET devices– Diagnostic applications for monitoring technology developmentDiagnostic applications for monitoring technology development– Starting point for construction of equivalent circuit based nonlinear CAD Starting point for construction of equivalent circuit based nonlinear CAD

modelsmodels

• Local interest is packaged power FETs, especially LDMOS devicesLocal interest is packaged power FETs, especially LDMOS devices– Apply modeling to off-the-shelf devices, scalability therefore not an issueApply modeling to off-the-shelf devices, scalability therefore not an issue– Do require accurate modeling of extrinsic networksDo require accurate modeling of extrinsic networks

• Model extraction algorithms constrained not to use device design Model extraction algorithms constrained not to use device design informationinformation

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Multi-Bias Decomposition-Based ExtractionMulti-Bias Decomposition-Based Extraction

• Algorithm is formulated to Algorithm is formulated to overcome the ill-conditioned overcome the ill-conditioned nature of problemnature of problem

• Combines data from multiple Combines data from multiple bias points into one integrated bias points into one integrated problem solverproblem solver

• Decomposition-based optimizer Decomposition-based optimizer used to efficiently handle large used to efficiently handle large number of parametersnumber of parameters

• Have been hybridized with Have been hybridized with analytic extraction proceduresanalytic extraction procedures

• Fast, robust and starting value Fast, robust and starting value independentindependent

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Moving to Bulk Si MOSFET DevicesMoving to Bulk Si MOSFET Devices

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Nonlinear Equivalent Circuit Modeling ProcessNonlinear Equivalent Circuit Modeling Process

Measure Multi-Bias S-Parameters &

DC Data

Extract Small-Signal Circuit Modelsfrom the Multi-Bias S-Parameter Data

Construct Nonlinear Circuit Modelfrom Equivalent Circuit Data

and DC Measurements

Verify Nonlinear Model thru Design&

Nonlinear Measurements

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Equivalent Circuit ModelsEquivalent Circuit Models

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Typical Multi-Bias S-parameter & DC Measurement Typical Multi-Bias S-parameter & DC Measurement SystemSystem

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Why Create an Adaptive Measurement Algorithm?Why Create an Adaptive Measurement Algorithm?

• Nonlinear measurement-based models require large volumes of dataNonlinear measurement-based models require large volumes of data

• This implies the use of computer controlled measurement setupsThis implies the use of computer controlled measurement setups

• Want more bias points in areas where the device characteristics Want more bias points in areas where the device characteristics change rapidlychange rapidly

• For larger devices, a high uniform density of bias points is not practicalFor larger devices, a high uniform density of bias points is not practical

• An adaptive control procedure with following qualities is required:An adaptive control procedure with following qualities is required:– Must ensure equipment & device safetyMust ensure equipment & device safety– Must exploit all available measured data (DC & S-Parameter data)Must exploit all available measured data (DC & S-Parameter data)– Decisions should be based on direct analysis of data (technology Decisions should be based on direct analysis of data (technology

independence)independence)– Make provision for finite programming & measurement resolution of DC Make provision for finite programming & measurement resolution of DC

sourcessources

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Who is the competition?Who is the competition?

• Most extensive work done by Fan & Root (Agilent)Most extensive work done by Fan & Root (Agilent)– [1] S. Fan, et. al. “Automated Data Acquisition System for FET [1] S. Fan, et. al. “Automated Data Acquisition System for FET

Measurements and its Application,” ARFTG Conference, pp. 107-119Measurements and its Application,” ARFTG Conference, pp. 107-119– [2] D.E. Root, et. al. “ Measurement-Based Large-Signal Diode Modeling [2] D.E. Root, et. al. “ Measurement-Based Large-Signal Diode Modeling

Systems for Circuit and Device Design,” IEEE Transactions on Microwave Systems for Circuit and Device Design,” IEEE Transactions on Microwave Theory and Techniques, Vol. 41, No. 12, Dec. 1993, pp. 2211-2217Theory and Techniques, Vol. 41, No. 12, Dec. 1993, pp. 2211-2217

• Ref [1] only uses DC data – adaptive exploration of IRef [1] only uses DC data – adaptive exploration of IDSDS(V(VDSDS) curves) curves

• Ref [2] uses AC data via previously extracted diode small-signal Ref [2] uses AC data via previously extracted diode small-signal modelmodel

• Majority of work on adaptive sampling procedures is focused on EM Majority of work on adaptive sampling procedures is focused on EM analysis procedures to reduce the number of time consuming analysis procedures to reduce the number of time consuming simulations requiredsimulations required

• Techniques developed for EM simulations not directly applicable to Techniques developed for EM simulations not directly applicable to measurement examples due to measurement noisemeasurement examples due to measurement noise

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Components of an Adaptive Measurement SystemComponents of an Adaptive Measurement System

• Define a fine measurement grid – minimum bias point separationDefine a fine measurement grid – minimum bias point separation– All bias points to be measured must fall on the fine gridAll bias points to be measured must fall on the fine grid– Fine grid is a square defined by min/max bias voltagesFine grid is a square defined by min/max bias voltages– Easy way to handle DC source programming/measurement uncertaintiesEasy way to handle DC source programming/measurement uncertainties

• Experimentally determine Save Operating Area (SOA) of deviceExperimentally determine Save Operating Area (SOA) of device– SOA limits defined by max/min VSOA limits defined by max/min VGSGS, V, VDSDS, I, IGSGS, I, IDSDS, P, PDSDS

– Boundaries to be determined experimentally using minimum of Boundaries to be determined experimentally using minimum of measurementsmeasurements

– Establish fine grid bias points that fall inside the SOAEstablish fine grid bias points that fall inside the SOA

• S-Parameter Driven Refinement AlgorithmS-Parameter Driven Refinement Algorithm– Start with an initial selection of measurements, and refine selection by Start with an initial selection of measurements, and refine selection by

placing N new bias points based on analysis of S-parameter dataplacing N new bias points based on analysis of S-parameter data

• DC Driven Refinement AlgorithmDC Driven Refinement Algorithm

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Determining the Safe Operating Area (SOA)Determining the Safe Operating Area (SOA)

• Measure an approximate value Measure an approximate value of threshold voltage Vof threshold voltage VTT

• User defined list of VUser defined list of VGSGS bias bias voltages, with most in device voltages, with most in device active regionactive region

• Explore IExplore IDSDS(V(VDSDS) curves at each ) curves at each VVGSGS bias using large ∆V bias using large ∆VDSDS to find to find SOA limitsSOA limits

• Linear extrapolation is used to Linear extrapolation is used to check if a projected check if a projected measurement will exceed a SOA measurement will exceed a SOA limitlimit

• Key to procedure is lots of Key to procedure is lots of safety checkssafety checks

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S-Parameter Driven Refinement ProcedureS-Parameter Driven Refinement Procedure

• SOA procedure provides initial set of measurements for refinement SOA procedure provides initial set of measurements for refinement procedureprocedure

• Adaptive procedure places N new bias points so as to best capture Adaptive procedure places N new bias points so as to best capture nonlinear behavior of devicenonlinear behavior of device

• Analyze the device S-parameters to determine the position of new Analyze the device S-parameters to determine the position of new bias pointsbias points

• Higher density of bias points in regions where any of 4 S-parameters Higher density of bias points in regions where any of 4 S-parameters are experiencing large variations with biasare experiencing large variations with bias

• Change in S-parameters signifies change in model parameter valuesChange in S-parameters signifies change in model parameter values

• During measurement phase it is not important to know which During measurement phase it is not important to know which parameter has changed, just that change has occurredparameter has changed, just that change has occurred

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Increasing Diversity in Selected S-Parameter DataIncreasing Diversity in Selected S-Parameter Data

• Need to define the Need to define the differences between S-differences between S-ParametersParameters

• S-Parameter curves S-Parameter curves change in:change in:– LengthLength– PositionPosition– Shape & OrientationShape & Orientation

• Require a geometric Require a geometric abstraction to describe S-abstraction to describe S-ParametersParameters

• S-Parameter CentroidsS-Parameter Centroids

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S-Parameter Driven Refinement ProcedureS-Parameter Driven Refinement Procedure• Identify adjacent bias points – makes use of Delaunay triangulationIdentify adjacent bias points – makes use of Delaunay triangulation

• Calculate distance between centroids of adjacent bias pointsCalculate distance between centroids of adjacent bias points

• Place new bias points between bias points with largest centroid separationPlace new bias points between bias points with largest centroid separation

• Safety checks for duplicate bias pointsSafety checks for duplicate bias points

• Fine measurement grid introduces refinement limitationsFine measurement grid introduces refinement limitations

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DC Driven Refinement AlgorithmDC Driven Refinement Algorithm

• For complete characterization, both the DC & AC characteristics For complete characterization, both the DC & AC characteristics must be consideredmust be considered

• Can use existing procedures, such as those proposed by Fan & Can use existing procedures, such as those proposed by Fan & RootRoot

• Simple alternative is to use difference between linear and spline Simple alternative is to use difference between linear and spline interpolation models of Iinterpolation models of IDSDS(V(VGSGS,V,VDSDS))

• Place new measurements where difference between interpolation Place new measurements where difference between interpolation models is largestmodels is largest

• Draw back is that boundaries of SOA needs to be well definedDraw back is that boundaries of SOA needs to be well defined

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Illustration of Adaptive Bias Point Selection (1)Illustration of Adaptive Bias Point Selection (1)

• GaAs HEMTGaAs HEMT

• 50mV Fine grid50mV Fine grid

• 9 Initial measurements 9 Initial measurements defining boundaries of the SOAdefining boundaries of the SOA

• 100 iterations of the S-100 iterations of the S-parameter refinement parameter refinement algorithmalgorithm

• 463 newly selected bias points463 newly selected bias points

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Illustration of Adaptive Bias Point Selection (2)Illustration of Adaptive Bias Point Selection (2)

• Bulk Si MOSFET deviceBulk Si MOSFET device

• Physical gate length ≈ 70 nmPhysical gate length ≈ 70 nm

• 20 20 μμm total gate widthm total gate width

• 2 gate fingers2 gate fingers

• 50 mV x 100 mV fine grid50 mV x 100 mV fine grid

• 28 initial measurements, 28 initial measurements, determined with SOA exploration determined with SOA exploration algorithmalgorithm

• 80 iterations of S-parameter 80 iterations of S-parameter refinement algorithmrefinement algorithm

• 292 newly selected bias points292 newly selected bias points

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Nonlinear Modeling Verification (GaAs FET)Nonlinear Modeling Verification (GaAs FET)

• Table-based model Table-based model implemented in Agilent ADS implemented in Agilent ADS circuit simulatorcircuit simulator– Table-based model used linear Table-based model used linear

interpolationinterpolation

• Reference model was Reference model was constructed using all the data, constructed using all the data, in other words, every point on in other words, every point on the fine gridthe fine grid

• 2nd model was constructed 2nd model was constructed using adaptively sampled data – using adaptively sampled data – 50% data reduction50% data reduction

• NNMS Nonlinear measurements NNMS Nonlinear measurements were performedwere performed– Device biased in class-AB modeDevice biased in class-AB mode– Fundamental excitation is 5 GHzFundamental excitation is 5 GHz– Single tone power sweep Single tone power sweep

driving FET into compressiondriving FET into compression

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Modeled & Measured Nonlinear ResultsModeled & Measured Nonlinear Results

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Conclusions & FutureConclusions & Future

• Incorporates both S-parameter & DC data into decision making Incorporates both S-parameter & DC data into decision making processprocess

• Captures both VCaptures both VDSDS and V and VGSGS switch-on regions switch-on regions

• Procedure is technology independentProcedure is technology independent

• It has a high emphasis on device and equipment safetyIt has a high emphasis on device and equipment safety

• Makes provision for equipment measurement limitationsMakes provision for equipment measurement limitations

• Future work will focus on characterizing LDMOS power devicesFuture work will focus on characterizing LDMOS power devices

• Extensions include the incorporation of designer knowledge into the Extensions include the incorporation of designer knowledge into the adaptive measurement procedureadaptive measurement procedure