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 Engineering
Presentation Overview • Introduction & Background Information • Equivalent Circuit Non-Linear Modeling • Adaptive Algorithm Requirements • Defining the Safe Operating Area (SOA) of a Device • S-Parameter Driven Adaptive Measurement Algorithms • DC Driven Adaptive Measurement Algorithms • Results & Conclusions University of Stellenbosch, Department of Electronic Engineering 2
Introduction & Background • Interest is in algorithms required for construction of device CAD models • Focus is on small-signal equivalent circuit extraction procedures • Have developed robust multi-bias extraction algorithms for Ga. As FETs • 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 – 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 University of Stellenbosch, Department of Electronic Engineering 3
Multi-Bias Decomposition-Based Extraction • Algorithm is formulated to overcome the ill-conditioned nature of problem • Combines data from multiple bias points into one integrated problem solver • Decomposition-based optimizer used to efficiently handle large number of parameters • Have been hybridized with analytic extraction procedures • Fast, robust and starting value independent University of Stellenbosch, Department of Electronic Engineering 4
Moving to Bulk Si MOSFET Devices University of Stellenbosch, Department of Electronic Engineering 5
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 Engineering 6
Equivalent Circuit Models University of Stellenbosch, Department of Electronic Engineering 7
Typical Multi-Bias S-parameter & DC Measurement System University of Stellenbosch, Department of Electronic Engineering 8
Why Create an Adaptive Measurement Algorithm? • Nonlinear measurement-based models require large volumes of data • This implies the use of computer controlled measurement setups • 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 • 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 Engineering 9
Who is the competition? • 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. 107 -119 – [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. 2211 -2217 • Ref [1] only uses DC data – adaptive exploration of IDS(VDS) curves • 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 • Techniques developed for EM simulations not directly applicable to measurement examples due to measurement noise University of Stellenbosch, Department of Electronic Engineering 10
Components of an Adaptive Measurement System • 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 – – – SOA limits defined by max/min VGS, VDS, IGS, IDS, PDS Boundaries to be determined experimentally using minimum of measurements Establish fine grid bias points that fall inside the SOA • 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 University of Stellenbosch, Department of Electronic Engineering 11
Determining the Safe Operating Area (SOA) • Measure an approximate value of threshold voltage VT • User defined list of VGS bias voltages, with most in device active region • Explore IDS(VDS) curves at each VGS bias using large ∆VDS to find SOA limits • Linear extrapolation is used to check if a projected measurement will exceed a SOA limit • Key to procedure is lots of safety checks University of Stellenbosch, Department of Electronic Engineering 12
S-Parameter Driven 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 • 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 • 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 University of Stellenbosch, Department of Electronic Engineering 13
Increasing Diversity in Selected S-Parameter Data • Need to define the differences between S-Parameters • S-Parameter curves change in: – Length – Position – Shape & Orientation • Require a geometric abstraction to describe SParameters • S-Parameter Centroids University of Stellenbosch, Department of Electronic Engineering 14
S-Parameter Driven Refinement Procedure • • • Identify adjacent bias points – makes use of Delaunay triangulation Calculate distance between centroids of adjacent bias points Place new bias points between bias points with largest centroid separation Safety checks for duplicate bias points Fine measurement grid introduces refinement limitations University of Stellenbosch, Department of Electronic Engineering 15
DC Driven Refinement Algorithm • For complete characterization, both the DC & AC characteristics must be considered • Can use existing procedures, such as those proposed by Fan & Root • Simple alternative is to use difference between linear and spline interpolation models of IDS(VGS, VDS) • Place new measurements where difference between interpolation models is largest • Draw back is that boundaries of SOA needs to be well defined University of Stellenbosch, Department of Electronic Engineering 16
Illustration of Adaptive Bias Point Selection (1) • Ga. As HEMT • 50 m. V Fine grid • 9 Initial measurements defining boundaries of the SOA • 100 iterations of the S-parameter refinement algorithm • 463 newly selected bias points University of Stellenbosch, Department of Electronic Engineering 17
Illustration of Adaptive Bias Point Selection (2) • Bulk Si MOSFET device • Physical gate length ≈ 70 nm • 20 μm total gate width • 2 gate fingers • 50 m. V x 100 m. V fine grid • 28 initial measurements, determined with SOA exploration algorithm • 80 iterations of S-parameter refinement algorithm • 292 newly selected bias points University of Stellenbosch, Department of Electronic Engineering 18
Nonlinear Modeling Verification (Ga. As FET) • 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 • 2 nd model was constructed using adaptively sampled data – 50% data reduction • 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 Engineering 19
Modeled & Measured Nonlinear Results University of Stellenbosch, Department of Electronic Engineering 20
Conclusions & Future • Incorporates both S-parameter & DC data into decision making process • Captures both VDS and VGS switch-on regions • Procedure is technology independent • It has a high emphasis on device and equipment safety • Makes provision for equipment measurement limitations • Future work will focus on characterizing LDMOS power devices • Extensions include the incorporation of designer knowledge into the adaptive measurement procedure University of Stellenbosch, Department of Electronic Engineering 21
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