The Agilent Device Modeling flow for Agilent DynaFET GaN ......Nonlinear Ids, Igs, Igd, Cgs, Cgd...

An overview of conventional and new

modeling flows with emphasis on the new

Agilent DynaFET GaN and GaAs FET model

Amplifier Design Flow Seminar

Basking Ridge, NJ, April 2nd

Authors: Roberto Tinti, Jianjun Xu, and David E. Root

Presenter: Roberto Tinti

The Agilent Device

Modeling flow for

nonlinear RF

applications

Acknowledgement

Technical contributors Prof. I. Angelov

J. Xu

M. Iwamoto

J. Horn

T. Nielsen

M. Marcu

J. Verspecht

R. M. Biernacki

C. Gillease

F. Sischka

External collaborators: listed on specific slides

Others

R. Hoft

Agilent Management

Partner Company Management

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3

Agenda

• Overview of the Agilent device modeling flow

• A high level introduction to device modeling

• Two different approaches to device modeling:

• Enhanced conventional extraction of compact models (e.g. Angelov-

GaN)

• Time-domain compact model constructed from NVNA data (new

DynaFET model)

• Summary and conclusions

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4

III-V RF Device Modeling Flow

Measurements Modeling Validation

Agilent N67xx, B1500/5A

Agilent PNA-X & NVNA

Maury Pulsed I-V

IC-CAP

Maury Pulsed IV SW

HF FET models

Angelov / Angelov-GaN

Agilent DynaFET

IC-CAP and ADS IC-CAP and ADS

Load Pull SW

PNA-X & NVNA

Maury Load Pull

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Compact models

• Have an assumed structure: circuit elements and topology

– lumped approximation assumed valid

• Try to model all relevant physics effecting device behavior

– invariably some physics is left out

• Work in all simulation modes (transient, harmonic balance, …)

Coupled nonlinear equivalent circuits

(e.g. Electrical, thermal, trap capture / emission)

Each element has a constitutive relation (I-V, Q-V)

Each constitutive relation has many parameters

1 2( ) ( ( ), ( ))I t Ids V t V t1 2( ) ( ( ), ( ))gc

dI t Q V t V t

dt

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6

Conventional Device (Compact) Modeling Flow

bi

GSbigs0GS

V

V1VCQ

QGS IDS

QGD

CPGS CPDS

CPGD

LG RD LD

RS

LS

RG

G D

S

Intrinsic FET

)Vtanh(γVAI DS

3

0n

n

GSnDS

VNA

DC

Parameter

Extraction

Gate Drain Source

Drawbacks: • Expert intensive (Ph.D.) and Time consuming

• Technology dependent; hard to maintain

• Parameter Extraction (Optimization) not always well-posed

• May never get good results!

• DC & Linear Data not sufficient to infer I-V, Q-V relations

• Don’t know how well model works in nonlinear regime

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The Angelov-GaN model Non linear device model for GaAs and GaN developed at Chalmers by Prof. I. Angelov

• Empirical equivalent circuit model

• Well defined extraction procedure

• Available in all major simulator via Verilog-A implementation

• Include models for all key effects, including dispersion & breakdown

Nonlinear Ids, Igs, Igd, Cgs, Cgd

Idss

Igs

Igd

Ids

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How does Angelov compares to EEHEMT

2014/4/10

Agilent Restricted

EEHEMT Angelov / Angelov-GaN

Model Equation Type

Piecewise

Continuous

Extension for GaN Devices

Asymmetric Capacitance

Modeling

Drain-Voltage Dependent

Capacitance Modeling

Self Heating Effect

(PEFF, MU)

(RTH, CTH)

Frequency Dispersion of

Conductance

Series RC branch

+ Dispersion current

(Separate parameters for DC/AC)

Series RC branch

+ Nonlinear R

Temperature Dependency

Modeling

Convergence in LS

Simulation

Geometry Scaling

Noise Modeling

1. Provide good

performance

when modeling

high-order

derivatives of

Drain current (and

harmonics)

2. Can be

implemented by

applying external

scaling equations

to model

parameters

3. Uses standard

noise model for

MESFET/HEMT

devices (+ 1/f

noise model)

Good modeled

Partially modeled

Not modeled

[1]

[2]

[3]

8

9

Recent improvements of Angelov-GaN over the

Angelov model for GaAs

1. Enhanced drain current model (Ids model #3)

2. Enhanced capacitance model (Cap model #3,4)

3. Temperature and bias dependent Rd and Rs

4. Enhanced dispersion model with back gate approach to model

knee walkout RC/Crf and new Rdel,Cdel

5. Enhanced breakdown model

6. Added breakdown model for GS and GD junctions

7. Added user accessible thermal port

These enhancements are critical to model RF power amplifiers

thermal and dynamic behavior, including gain, PAE and distortion.

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Examples of Angelov-GaN improvements: over GaAs Angelov

PO

A

Y

X

Plot Angelov_GaN_Modeling/ac/spar_vg_vd/Cgs_vs_Vg

Cgs_vs_Vg (spar_vg_vd)

vgx [E+0]

sdata

(Cgs)

[E

-12]

-10 -8 -6 -4 -2 0 22

4

6

8

10

12

Angelov-GaN

(Capmod=3,4)

with Peaking Cap.

PO

A

Y

X

Plot Angelov_GaN_Modeling/ac/spar_vg_vd/Cgs_vs_Vg

Cgs_vs_Vg (spar_vg_vd)

vgx [E+0]

sdata

(Cgs)

[E

-12]

-10 -8 -6 -4 -2 0 22

4

6

8

10

12

14

Angelov

(or Capmod=0,1,2)

No Peaking Cap.

Cgs_vg Cgs_vg

Better fitting for

peaking Cgs

Gate Breakdown

Ids accuracy

is improved

－ Measured

－ Angelov-GaN Sim

－ Angelov Sim

Enhanced capacitance and gate breakdown models

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A closer look at dispersion modeling in the

Angelov-GaN model

• RC circuits at the input and

output, Rc/Crf and Rcin Crfin

• Rc is bias dependent

• back-gate modeling by feeding

a portion of RF back gate

voltage to the gate

Frequency dispersion

• Delayed gate control Rdel/Cdel

network

• Affect high frequency IdVd

waveforms

High Frequency Current slump

Vd=4.5V, Id= 0.15 A Vd=6.3V, Id= 0.1A

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Angelov-GaN typical extraction flow

• DC measurements at

various temperatures

• Pulsed IV measurements

• S-parameters

measurements

• Large signal (Load pull or

NVNA or both)

• Parasitic extraction

• DC & Thermal

• AC

• Final optimization by using

DC, small signal and large

signal data

• Independent Large signal

validation (LP or NVNA)

Measurements Modeling

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Extraction Flow with IC-CAP

Update Measured Data

(inc. de-embedding)

Extract System Series

Resistance

Repeat until getting

medium level fit

Extract CV

parameters

Repeat until getting

medium level fit

LS Verification

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Demonstration of the Angelov extraction flow

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15

Using Pulsed I-V data

• Pulsed I-V data at different T are used to extract temperature dependency parameters

• May be used to extract thermal impedance

• Used to investigate gate/drain lag effects and extract related parameters

Pulsed measurement data can be imported in IC-CAP. A pulsed IV simulation template setup is also available.

Note: Pulsed I-V simulation are obtained

by using the erf_pulse() ADS source.

Time domain simulations are sampled

and data are then combined

programmatically into IV sweep

DC IV vs. PIV

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A brief demonstration on how to import pulse IV

data into IC-CAP and run pulse I-V simulation

MAURY IVCAD

IC-CAP MDM file

IC-CAP

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Benefits of using large signal data in the

conventional extraction

• Get best set of parameter values for given model

• Trade-off fits at large-signal and small-signal – Tune parameters for specific applications

• Better extraction of parameters (such as breakdown and kirk

effect) that are based on behavior in limiting regions of

operation

• Explore limitations of the model

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Measurements

Model

MOSFET

BSIM3.2 model

Using parameter values

extracted from DC + CV

Using parameter values

extracted from NVNA data

Measurements

Model

Vg

Ig

Vg

Ig

a11

b

a11

b

Measurements

Model

Measurements

Model

Conventional parameter extraction using large signal data

[10] E. Vandamme et al, “Large-signal network analyzer measurements and their use in device modeling,” MIXDES 2002, Wroclaw, Poland

• Get optimal parameter

set for given model

• trade-off DC, SP, for

nonlinear performance

• App-dependent tuning

• Explore model limits

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Example of ICCAP comparison of simulated & NVNA

measured results

Extract or Tune parameters

with this capability

Based on NVNA data

Details are model-dependent

Large-signal Validation for free! Evaluate model under realistic operation

See where it needs improvement

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Conclusion about Angelov-GaN model

• Most popular GaN model, widely adopted in the industry,

available in ADS.

• Dynamic thermal and trapping effects are modeled via RC

networks.

• Basic extraction can be done using DC, pulsed IV and S-

parameter data

• Best results are obtained when parameters are tuned to large

signal data (LP or NWNA)

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The need for better models and

richer measurements

Richer

Measurements

Better Models

Some advanced measurements beyond DC and S-parameter • Spectrum analyzer measurements

• Pulsed I-V & Pulsed S-parameters

• Load-pull data in some form

DC and S-parameter data are generally not

sufficiently rich to enable separation of

multiple phenomena (e.g. self-heating and

trapping).

Irony: extracting model parameters for a

nonlinear model using linear S-pars!

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NVNA-based device

characterization

NVNA

Introducing the Agilent DynaFET, a complete

NVNA-based device modeling flow

ADS

IC design

Advanced compact model

constructed from NVNA data

[4] J. Xu, M. Iwamoto, J. Horn, D. E. Root, IEEE MTT-S International Microwave Symposium Digest, May, 2010.

4/2/2014


2_emitC

2

2_emitR

2_captR1_emitC

GSV 1

1_emitR

1_captR

)()( tV tIthC

thR

jT

0T

IG QG ID QD

DSV

GSV

DSV

Self-heating

Trapping

Advanced compact Agilent DynaFET Model

for GaAs and GaN FETs

dsgs V V 21j T

, ,T ,V ,VI 21jdsgsD )(

ANN

[4] J. Xu, M. Iwamoto, J. Horn, D. E. Root, IEEE MTT-S International Microwave Symposium Digest, May, 2010.

[13] O. Jardel et al, IEEE Transactions on MTT., vol. 55, Dec., 2007.

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Model Capabilities

Features

dynamic self-heating and

ambient temperature-dependence

gate-lag and drain-lag independently

modeled (knee walk-out, power slump)

Artificial neural networks (ANNs) for very

general, smooth I-V / Q-V relations

NVNA nonlinear data for model generation

and independent nonlinear validation

Simple geometrical scaling

Benefits

accuracy versus temperature;

thermal memory modeled

power slump, bias current versus Pin, and

PAE well modeled

detailed nonlinear coupling of trap states to

current accurately & smoothly modeled

obtain data over entire operating range

while maintaining device safety;

NVNA data closer to actual use condition

Same as standard expectations for a

compact time-domain transistor model

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NVNA Measurement Setup

The NVNA measurements

are performed at various:

(1) DC biases

(2) RF powers

(3) Load conditions

(4) Ambient temperatures

DUT Tuner

NVNA

Synchronized

Bias Supplies

or Active Injection (Xu et al, IMS2014 [21])

Advanced compact Agilent DynaFET Model

for GaAs and GaN FETs

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Flow #2: Advanced compact model constructed from NVNA data

Dynamic Load-lines

-2 0 2 4 6 8 10 12-50

0

50

100

150

200

250

Vds

Ids

Red line is one example of the

191 waveforms for this trap state th0j RtV tITT )()(

))(())(( tVMax ,tVMin ds2gs1

Creation of auxiliary dynamical variables

NVNA waveforms

4/2/2014


NVNA data for identification of constitutive relations

drain D gs ds j 1 2I t I V t , V t , T t , t , t ( ) ( ( ) ( ) ( ) ( ) ( ))

Auxiliary

Variables

Terminal

Voltages

NVNA

Waveforms

Auxiliary Variable

Generation using

Constitutive Relation

Identification / Fit

F(Vgs,Vds, Aux. variables) Terminal

Voltages

(Vgs,Vds)

Compiled

Model in ADS

th0j RtV tITT )()(

))(( tVMin gs1

))(( tVMax ds2

Auxiliary

Variables

jT 1 2

Auxiliary variables fixed at their

steady-state large-signal values

established by nonlinear

measurements

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Artificial Neural Networks (ANN)

• Universal Approximation Theorem: Fit “any” nonlinear function of any # of variables

• Infinitely differentiable: good for distortion.

• Easy to train (fit) with appropriate SW • Works well on non-gridded data and data with irregular boundaries (intrinsic FET)

An ANN is a parallel processor made up of simple, interconnected

processing units, called neurons, with weighted connections.

baxwsvxxFI

i

K

k

ikkiiK

1 1

1 ),...,(

sigmoid weights biases

x1

...

xk

4/2/2014


kE

Adjust wID

Given kth waveform

(t)meas

drainI

(t)V (t)V dsgs

th0j RtV tITT )()(

))(())(( tVMax ,tVMin ds2gs1

21j T

(t)drainI

ID_ANN

wID

ANN • ability to model highly

nonlinear relationships

with multiple inputs

and outputs.

• Model is simple and

model computation is

very fast

• Infinitely differentiable

and smooth

Advanced compact model constructed from NVNA data

- Neural Network Training of Id

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Intrinsic Model Characteristics ID @ two different trap state values

0 1 2 3 4 5 6

0

20

40

60

80

100

120

140

160

180

0 1 2 3 4 5 6

0

20

40

60

80

100

120

140

160

180

Vds (V)

Ids (mA) Ids (mA)

Vds (V)

fixed Tj and fixed trap state = [-2.0, 8.0] Isothermal DC )080255( .. 21jdsgsD , ,T ,V ,VI )55( ds2gs1jdsgsD V ,V ,T ,V ,VI

D gs ds j 1 2I V , V , T , , ( )

“knee walkout”

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Model Validation - Frequency dispersion of small-signal characteristics

Excellent broad-band, high frequency accuracy

over the entire device operating range

-12 12

-12 12

-0.6 0.6

-12 12

-12 12

-0.2 0.2

Frequency : 0.5 GHz to 50 GHz GaAs

pHEMT

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Simulated (___ )

Measured data (symbols)


- Model validation: Harmonic distortion, gain, bias current vs. power

GaAs

pHEMT

4/2/2014


0 2 4 6 8 10 12-20

0

20

40

60

80

100

120

140

160

180

200

Vds (V)

Id (mA) (.): DC

(o): Measured

Line: Simulated

Load

This Load was not used

in the model extraction

Demonstrates model can go far beyond the range over which

conventional DC and linear S-parameters can be taken


- Model validation: Dynamic loadline

GaAs

pHEMT

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DynaFET modeling flow: components & use model

ADS environment

External party environment Customer responsibility

PNA-X - NVNA DynaFET Characterization module

DynaFET meas. control SW

GUI

Active Source Injection control

DUT

info

input

Measurements

File transfer

Raw Data

Nonlinear

*for model generation

*for validation

DynaFET

natively

compiled model

tuners, DC supplies

external RF source,

bias tees, amps,

prober & probes

thermal chuck

model-file (ANN weights)

Encrypted

Agile

nt

pro

du

ct p

art

s

Custo

me

r

Resp

on

sib

ility

customer circuit

PNA-X NVNA environment

ICCAP environment

DynaFET Package

Characterization

DC I-V

SP

Parasitics Extraction

Extraction/de-embedding

DynaFET Core GUI

Extraction/Training

Validation

DC, SP

1-, 2-tone Pout/Pin

Raw Data DC I-V

SP

DynaFET Three-legged platform

0 1 2 3 4 5 6

0

20

40

60

80

100

120

140

160

180

“knee

walkout”

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Summary

Conventional compact models have fundamental limitations, the

recent commercial availability of NVNA instruments provides new

alternatives:

• NVNA data for compact model parameter extraction/validation Tune parameters, optimal parameter set, validate model, explore limits

• NVNA data & ANN-based construction of advanced compact

Agilent DynaFET Model Very accurate and general model, with self-heating and trapping effects.

These are trends that are becoming useful now and are likely to

become increasingly important in the future.

“Every nonlinear modeling station should use an

NVNA instead of a conventional linear VNA.” - Dr. D. E. Root, Agilent Technologies

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36

References [1] http://www.agilent.com/find/nvna

[2] Chiu et al “Characterization of annular-structure RF LDMOS transistors using Polyharmonic Distortion Model,” IEEE MTT-S International Microwave Symposium Digest, June, 2009, pp 977-980

[3] D. Gunyan et al, “Nonlinear validation of arbitrary load X-parameter and measurement-based device models,” IEEE MTT-S ARFTG Conference, Boston, MA, June 2009

[4] J. Xu, M. M. Iwamoto, J. Horn, D. E. Root, “Large-signal FET model with multiple time scale dynamics from nonlinear vector network analyzer data,” IEEE MTT-S International Microwave Symposium Digest, May, 2010.

[5] J. Horn, “Harmonic load-tuning predictions from X-parameters,” IEEE PA Symposium, San Diego, Sept. 2009

[6] G. Simpson et al, “Load-pull + NVNA = Enhanced X-parameters for PA designs with high mismatch and technology-independent large-signal device models,” IEEE ARFTG Conference, Portland, OR December 2008.

[7] J. Verspecht and D. E. Root, “Poly-Harmonic Distortion modeling,” in IEEE Microwave Theory and Techniques Microwave Magazine, June, 2006

[8] D. E. Root et al “X-parameters: The new paradigm for measurement, modeling, and design of nonlinear RF and microwave components,” Microwave Engineering Europe, Dec. 2008 pp 16-21.

[9] Agilent Advanced Design System documentation: Nonlinear Devices

[10] E. Vandamme et al, “Large-signal network analyzer measurements and their use in device modeling,” MIXDES 2002, Wroclaw, Poland.

[11] http://www.cree.com/products/pdf/CGH40010.pdf

[12] J. Horn, G. Simpson, and D. E. Root “GaN Device Modeling with X-Parameters,” IEEE MTT-S CSIC Symposium , Monterey, CA 2010.

[13] O. Jardel et al, “An electrothermal model for AlGaN/GaN power HEMTs including trapping effects to improve large-signal simulation results on high VSWR,” IEEE Transactions on MTT., vol. 55, Dec., 2007.

[14] J Verspecht, "Black box modelling of power transistors in the frequency domain," INMMiC'96 Conference Record, Duisburg, 1996

[15] J. Verspecht et al, “Extension of X-parameters to include long-term dynamic memory effects,” IEEE MTT-S International Microwave Symposium Digest, 2009. pp 741-744, June, 2009.

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http://www.agilent.com/find/nvna

37

References (2)

[16] D. E. Root, “Future Device Modeling Trends,” IEEE Microwave Magazine, Nov./Dec. 2012, pp 45-59

[17] T. S. Nielsen, M. Dieudonne, C. Gillease, D. E. Root, “Doherty Power Amplifier Design in Gallium Nitride Technology Using a Nonlinear Vector Network Analyzer and X-Parameters,” IEEE Compound Semiconductor Integrated Circuit Symposium (CSICS), October, 2012.

[18] D. E. Root, J. Xu, F. Sischka, M. Marcu, J. Horn, R.M. Biernacki, M. Iwamoto, “Compact and Behavioral Modeling of Transistors from NVNA Measurements: New Flows and Future Trends,” IEEE Custom Integrated Circuits Conference Digest San Jose, Sept. 2012

[19] Roblin, P.; Root, D.E.; Verspecht, J.; Ko, Y.; Teyssier, J.P. , “New Trends for the Nonlinear Measurement and Modeling of High-Power RF Transistors and Amplifiers With Memory Effects,” IEEE Transactions Microwave Theory and Techniques, Volume: 60 , Issue: 6 , part 2: pp. 1964 – 1978, 2012

[20] Root, D. E.; Marcu, M.; Horn, J.; Xu, J.; Biernacki, R. M.; Iwamoto, M., “Scaling of X-parameters for device modeling,” IEEE MTT-S International Microwave Symposium Digest (MTT), June, 2012

[21] J. Xu, R. Jones, S. A. Harris, T. Nielsen, and D. E. Root, “Dynamic FET Model – DynaFET - for GaN Transistors from NVNA Active Source Injection Measurements,” To be presented at 2014 IMS, Tampa, June.

[22] J. Xu, S. Halder, F. Kharabi, J. McMacken, J. Gering, and D. E. Root, Submitted for publication.

[23] D. E. Root, J. Verspecht, J. Horn, and M. Marcu, X-Parameters, Cambridge University Press, 2013

[24] T. S. Nielsen, M. Dieudonne, C. Gillease, and D. E. Root, “Doherty power amplifier design in gallium nitride technology using a nonlinear vector network analyzer and X-parameters,” in IEEE CSICS Dig. , La Jolla, CA, Oct 2012.

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The Agilent Device Modeling flow for Agilent DynaFET GaN ......Nonlinear Ids, Igs, Igd, Cgs, Cgd...

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