Adaptive Control of a Multi-Bias S-Parameter Measurement System Dr Cornell van Niekerk Microwave Components Group University of Stellebosch South Africa

University of Stellenbosch, Department of Electronic Engineering2 Presentation Overview Introduction & Background Information Introduction & Background Information Equivalent Circuit Non-Linear Modeling Equivalent Circuit Non-Linear Modeling Adaptive Algorithm Requirements Adaptive Algorithm Requirements Defining the Safe Operating Area (SOA) of a Device Defining the Safe Operating Area (SOA) of a Device S-Parameter Driven Adaptive Measurement Algorithms S-Parameter Driven Adaptive Measurement Algorithms DC Driven Adaptive Measurement Algorithms DC Driven Adaptive Measurement Algorithms Results & Conclusions Results & Conclusions

University of Stellenbosch, Department of Electronic Engineering3 Introduction & Background Interest is in algorithms required for construction of device CAD models Interest is in algorithms required for construction of device CAD models Focus is on small-signal equivalent circuit extraction procedures Focus is on small-signal equivalent circuit extraction procedures Have developed robust multi-bias extraction algorithms for GaAs FETs Have developed robust multi-bias extraction algorithms for GaAs FETs Focus is shifting to bulk Si MOSFET devices Focus is shifting to bulk Si MOSFET devices –Diagnostic applications for monitoring technology development –Starting point for construction of equivalent circuit based nonlinear CAD models Local interest is packaged power FETs, especially LDMOS devices Local interest is packaged power FETs, especially LDMOS devices –Apply modeling to off-the-shelf devices, scalability therefore not an issue –Do require accurate modeling of extrinsic networks Model extraction algorithms constrained not to use device design information Model extraction algorithms constrained not to use device design information

University of Stellenbosch, Department of Electronic Engineering4 Multi-Bias Decomposition-Based Extraction Algorithm is formulated to overcome the ill-conditioned nature of problem Algorithm is formulated to overcome the ill-conditioned nature of problem Combines data from multiple bias points into one integrated problem solver Combines data from multiple bias points into one integrated problem solver Decomposition-based optimizer used to efficiently handle large number of parameters Decomposition-based optimizer used to efficiently handle large number of parameters Have been hybridized with analytic extraction procedures Have been hybridized with analytic extraction procedures Fast, robust and starting value independent Fast, robust and starting value independent

University of Stellenbosch, Department of Electronic Engineering5 Moving to Bulk Si MOSFET Devices

University of Stellenbosch, Department of Electronic Engineering6 Nonlinear Equivalent Circuit Modeling Process Measure Multi-Bias S-Parameters & DC Data Extract Small-Signal Circuit Models from the Multi-Bias S-Parameter Data Construct Nonlinear Circuit Model from Equivalent Circuit Data and DC Measurements Verify Nonlinear Model thru Design & Nonlinear Measurements

University of Stellenbosch, Department of Electronic Engineering7 Equivalent Circuit Models

University of Stellenbosch, Department of Electronic Engineering8 Typical Multi-Bias S-parameter & DC Measurement System

University of Stellenbosch, Department of Electronic Engineering9 Why Create an Adaptive Measurement Algorithm? Nonlinear measurement-based models require large volumes of data Nonlinear measurement-based models require large volumes of data This implies the use of computer controlled measurement setups This implies the use of computer controlled measurement setups Want more bias points in areas where the device characteristics change rapidly Want more bias points in areas where the device characteristics change rapidly For larger devices, a high uniform density of bias points is not practical For 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 safety –Must exploit all available measured data (DC & S-Parameter data) –Decisions should be based on direct analysis of data (technology independence) –Make provision for finite programming & measurement resolution of DC sources

University of Stellenbosch, Department of Electronic Engineering10 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 Measurements and its Application,” ARFTG Conference, pp –[2] D.E. Root, et. al. “ Measurement-Based Large-Signal Diode Modeling Systems for Circuit and Device Design,” IEEE Transactions on Microwave Theory and Techniques, Vol. 41, No. 12, Dec. 1993, pp Ref [1] only uses DC data – adaptive exploration of I DS (V DS ) curves Ref [1] only uses DC data – adaptive exploration of I DS (V DS ) curves Ref [2] uses AC data via previously extracted diode small-signal model Ref [2] uses AC data via previously extracted diode small-signal model Majority of work on adaptive sampling procedures is focused on EM analysis procedures to reduce the number of time consuming simulations required Majority of work on adaptive sampling procedures is focused on EM analysis procedures to reduce the number of time consuming simulations required Techniques developed for EM simulations not directly applicable to measurement examples due to measurement noise Techniques developed for EM simulations not directly applicable to measurement examples due to measurement noise

University of Stellenbosch, Department of Electronic Engineering11 Components of an Adaptive Measurement System Define a fine measurement grid – minimum bias point separation Define a fine measurement grid – minimum bias point separation –All bias points to be measured must fall on the fine grid –Fine grid is a square defined by min/max bias voltages –Easy way to handle DC source programming/measurement uncertainties Experimentally determine Save Operating Area (SOA) of device Experimentally determine Save Operating Area (SOA) of device –SOA limits defined by max/min V GS, V DS, I GS, I DS, P DS –Boundaries to be determined experimentally using minimum of measurements –Establish fine grid bias points that fall inside the SOA S-Parameter Driven Refinement Algorithm S-Parameter Driven Refinement Algorithm –Start with an initial selection of measurements, and refine selection by placing N new bias points based on analysis of S-parameter data DC Driven Refinement Algorithm DC Driven Refinement Algorithm

University of Stellenbosch, Department of Electronic Engineering12 Determining the Safe Operating Area (SOA) Measure an approximate value of threshold voltage V T Measure an approximate value of threshold voltage V T User defined list of V GS bias voltages, with most in device active region User defined list of V GS bias voltages, with most in device active region Explore I DS (V DS ) curves at each V GS bias using large ∆V DS to find SOA limits Explore I DS (V DS ) curves at each V GS bias using large ∆V DS to find SOA limits Linear extrapolation is used to check if a projected measurement will exceed a SOA limit Linear extrapolation is used to check if a projected measurement will exceed a SOA limit Key to procedure is lots of safety checks Key to procedure is lots of safety checks

University of Stellenbosch, Department of Electronic Engineering13 S-Parameter Driven Refinement Procedure SOA procedure provides initial set of measurements for refinement procedure SOA procedure provides initial set of measurements for refinement procedure Adaptive procedure places N new bias points so as to best capture nonlinear behavior of device Adaptive procedure places N new bias points so as to best capture nonlinear behavior of device Analyze the device S-parameters to determine the position of new bias points Analyze the device S-parameters to determine the position of new bias points Higher density of bias points in regions where any of 4 S-parameters are experiencing large variations with bias Higher density of bias points in regions where any of 4 S-parameters are experiencing large variations with bias Change in S-parameters signifies change in model parameter values Change in S-parameters signifies change in model parameter values During measurement phase it is not important to know which parameter has changed, just that change has occurred During measurement phase it is not important to know which parameter has changed, just that change has occurred

University of Stellenbosch, Department of Electronic Engineering14 Increasing Diversity in Selected S-Parameter Data Need to define the differences between S-Parameters Need to define the differences between S-Parameters S-Parameter curves change in: S-Parameter curves change in: –Length –Position –Shape & Orientation Require a geometric abstraction to describe S- Parameters Require a geometric abstraction to describe S- Parameters S-Parameter Centroids S-Parameter Centroids

University of Stellenbosch, Department of Electronic Engineering15 S-Parameter Driven Refinement Procedure Identify adjacent bias points – makes use of Delaunay triangulation Identify adjacent bias points – makes use of Delaunay triangulation Calculate distance between centroids of adjacent bias points Calculate distance between centroids of adjacent bias points Place new bias points between bias points with largest centroid separation Place new bias points between bias points with largest centroid separation Safety checks for duplicate bias points Safety checks for duplicate bias points Fine measurement grid introduces refinement limitations Fine measurement grid introduces refinement limitations

University of Stellenbosch, Department of Electronic Engineering16 DC Driven Refinement Algorithm For complete characterization, both the DC & AC characteristics must be considered For complete characterization, both the DC & AC characteristics must be considered Can use existing procedures, such as those proposed by Fan & Root Can use existing procedures, such as those proposed by Fan & Root Simple alternative is to use difference between linear and spline interpolation models of I DS (V GS,V DS ) Simple alternative is to use difference between linear and spline interpolation models of I DS (V GS,V DS ) Place new measurements where difference between interpolation models is largest Place new measurements where difference between interpolation models is largest Draw back is that boundaries of SOA needs to be well defined Draw back is that boundaries of SOA needs to be well defined

University of Stellenbosch, Department of Electronic Engineering17 Illustration of Adaptive Bias Point Selection (1) GaAs HEMT GaAs HEMT 50mV Fine grid 50mV Fine grid 9 Initial measurements defining boundaries of the SOA 9 Initial measurements defining boundaries of the SOA 100 iterations of the S-parameter refinement algorithm 100 iterations of the S-parameter refinement algorithm 463 newly selected bias points 463 newly selected bias points

University of Stellenbosch, Department of Electronic Engineering18 Illustration of Adaptive Bias Point Selection (2) Bulk Si MOSFET device Bulk Si MOSFET device Physical gate length ≈ 70 nm Physical gate length ≈ 70 nm 20 μm total gate width 20 μm total gate width 2 gate fingers 2 gate fingers 50 mV x 100 mV fine grid 50 mV x 100 mV fine grid 28 initial measurements, determined with SOA exploration algorithm 28 initial measurements, determined with SOA exploration algorithm 80 iterations of S-parameter refinement algorithm 80 iterations of S-parameter refinement algorithm 292 newly selected bias points 292 newly selected bias points

University of Stellenbosch, Department of Electronic Engineering19 Nonlinear Modeling Verification (GaAs FET) Table-based model implemented in Agilent ADS circuit simulator Table-based model implemented in Agilent ADS circuit simulator –Table-based model used linear interpolation Reference model was constructed using all the data, in other words, every point on the fine grid Reference model was constructed using all the data, in other words, every point on the fine grid 2nd model was constructed using adaptively sampled data – 50% data reduction 2nd model was constructed using adaptively sampled data – 50% data reduction NNMS Nonlinear measurements were performed NNMS Nonlinear measurements were performed –Device biased in class-AB mode –Fundamental excitation is 5 GHz –Single tone power sweep driving FET into compression

University of Stellenbosch, Department of Electronic Engineering20 Modeled & Measured Nonlinear Results

University of Stellenbosch, Department of Electronic Engineering21 Conclusions & Future Incorporates both S-parameter & DC data into decision making process Incorporates both S-parameter & DC data into decision making process Captures both V DS and V GS switch-on regions Captures both V DS and V GS switch-on regions Procedure is technology independent Procedure is technology independent It has a high emphasis on device and equipment safety It has a high emphasis on device and equipment safety Makes provision for equipment measurement limitations Makes provision for equipment measurement limitations Future work will focus on characterizing LDMOS power devices Future work will focus on characterizing LDMOS power devices Extensions include the incorporation of designer knowledge into the adaptive measurement procedure Extensions include the incorporation of designer knowledge into the adaptive measurement procedure