RF GaN Device Models and Techniques

RF GaN Device Models and Techniques
Raj Sodhi, EEsof DES Marketing, Keysight Dr. Ujwal Radhakrishna, MIT and Notre Dame Univ Dr. Yogesh Chauhan, IIT Kanpur Dr. Roberto Tinti and Mark Dunn, EEsof Keysight Thank you for that nice introduction. Hello and welcome. I’m Raj Sodhi. <click> It’s a pleasure to be here and an honor. In what follows, I will be talking about RF GaN modeling for 5G and other applications. ===================== 82?partnerref=XYZ Due to their high-power handling capability and linearity, GaN devices continue to advance in market acceptance for 5G, radar, and power electronics. GaN technology outperforms other RF technologies because it can simultaneously offer the highest power, gain, and efficiency combination at a given frequency. Three Key Learnings: The basic theory of operation of gallium nitride devices and why they are so well- suited for a host of modern applications. A survey of the various non-linear GaN models currently available and under proposal. An overview of the CMC approved GaN models and a description of a high-level extraction methodology including the use of non-linear measurements for more accurate parameter determination. Speakers Raj Sodhi Application Engineer Keysight Technologies Raj Sodhi is a Keysight applications developer who has worked in both R&D and marketing over the past 20 years. Most recently, in signal sources and signal analyzer teams. Before joining Keysight, he worked in various start-up environments with a focus on analog and RF design. Raj pioneered sensor calibration algorithms while at PNI Corporation. He also worked at Skyworks solutions designing PAs and front-end modules for the low-cost cell phone market. Raj earned his BSEE and MSEE degrees at MIT.

RF GaN Modeling Webinar
Agenda: Gallium-Nitride devices, technology and business drivers GaN model survey: Angelov-GaN, MVSG, ASM-HEMT and DynaFET models Model parameter extraction Chapter 4: In this segment, I’ll discuss model parameter extraction at a high level. RF GaN Modeling Webinar

GaN Power RF - 5Y Overview
RF GaN market to grow from $380M to $1.3B, 23% CAGR Market share: from 20% today to 50% by 2023 From IDM to Fabless/Foundry model You might wonder “What’s the big deal about GaN?” well, it’s the fastest growing subsegment in the RF industry. Here we show a study by a French research company Yole. The RF GaN market is expected to grow from $380 million to $1.3 billion over the next five years, representing a 23% compound annual growth rate. What are the contributors for this growth? - 5G communication. Base stations and wireless backhaul account for 50% of the market. - Aerospace and defense, where high-power radar systems are increasingly starting to use GaN Noting the figure on the left, we see that today, the industry is dominated by IDM’s or integrated device manufacturers. By 2023, we expect a balance between integrated device manufacturers, design houses and pure-play foundries. Source: “RF Power Market and Technologies 2017: GaN, GaAs and LDMOS” -

GaN HEMT Devices High mobility  RF applications
Technology Overview Typical structure of RF/Microwave GaN on Si HEMT* High mobility  RF applications SiC substrate to reduce substrate loss and keep device cool. High breakdown  Power electronics applications P-GaN or cascode structure to get normally OFF Field plates for increase breakdown voltage These devices have both high mobility and high breakdown voltage, which makes them well-suited for both high frequency and high power applications. In each of these domains, we have various device processing tricks to improve the usability of these devices. RF: Most of the current RF devices are built on SiC substrate to reduce substrate loss and keep the device cool. <click> There are efforts underway to save money by using a silicon substrate but there are tradeoffs. We can find GaN-on-Silicon wafers up to 8” in diameter today. PE: Remember that 2D electron gas from the earlier slide? It’s great that we get this for free. In fact, we get it without any bias. That means that most devices are normally on which presents a circuit safety problem in power electronics applications. <click> Panasonic has been the pioneer in creating normally off devices. Another approach is to put a MOSFET in series with the GaN device in a cascode structure. To increase the breakdown voltage, the gap between the gate and the drain is often increased. And then, to optimize the electric field distribution, field plate structures are often added to reduce the coupling between gate and drain. However this comes at a cost to switching speed, due to the additional capacitance. Reference: *GaN transistors on Si for switching and high-frequency applications, Tetsuzo Ueda and al. Japanese Journal of applied Physics, 2014 2DEG SiC for RF Tetsuzo Ueda et al., Japanese Journal of applied Physics, 2014

Compact models: Link between devices and circuits
Tool for GaN-system design: MVSG and ASM-HEMT models Technology CAD Al0.26Ga0.74N GaN New material system New device design I, Q = function( V, T, f ) Analytical physical models Device characterization I, Q = function( V, T, f, a, b ) Calibrated physical model Circuit design and fabrication

Available GaN Models DynaFET MVSG ASM-HEMT Most accurate model
Institute/Company Authors Year Notes EEHEMT Agilent/Keysight Eric Arnold 1998 empirical model, based evolution of EEFET model Angelov Chalmers Univ Iltcho Angelov 1992 "A new empirical nonlinear model for HEMT and MESFET devices" Angelov-GaN 2008 extension of Angelov Model for GaN DynaFET Jianjun Xu, David Root et al. 2014 Uses data to train artificial neural network (ANN) ASM-HEMT IITK & USF Y. S. Chauhan & S. Khandelwal 2015 physics based surface potential model MVSG MIT Ujwal Radhakrishna 2013 physics based virtual source model EEHEMT EEHEMT Derivative 1 EEHEMT Derivative 2 EEHEMT Derivative 3 DynaFET Most accurate model While this may not be a chronological history, the main models are presented here. The EEHEMT model is an empirical model, which evolved from the Curtice model. The early adopters of GaN adopted the EEHEMT model, creating proprietary derivatives of the model to suit their needs. In a similar way, when the Angelov GaN model took hold, many companies and universities created their own derivatives. The DynaFET model which came out in 2014 gives fantastic fitting accuracy, both large signal and small signal. It does require significant commitment from the company, as the nonlinear vector analyzer measurements are time-consuming and challenging. In 2018, 2 new physics-based models were accepted as industry-standard by the compact model coalition (CMC). Both were formulated for PE and RF applications. Angelov-GaN Chalmers Derivative 1 Chalmers Derivative 2 Chalmers Derivative 3 MVSG Accepted by Compact Model Coalition (CMC) in 2018 ASM-HEMT

From physical modeling to industry standard
From research to industry adoption: ASM-HEMT and MVSG model chosen as a GaN industry standard models Phase-I Phase-II Phase-III Phase-IV 8 candidates Accuracy and convergence Physical nature 4 candidates Sponsors: ADI, TI, Toshiba Data: Triquint, Toshiba 2 candidates Model evaluation Feedback Model support Version release Status of the GaN HEMT Standardization Effort at the Compact Model Coalition, S. D. Mertens, 2014 IEEE Compound Semiconductor Integrated Circuit Symposium (CSICS), La Jolla, CA, 2014, pp. 1-4.

GaN Compact Model Comparison
Empirical Physics-based ANN-based Models Angelov-GaN, EEHEMT ASM-HEMT MVSG DynaFET CMC Standard Scalable, W/L/NF * Early availability during process development Does not require process info Simple extraction flow Good DC/S-par fit Large signal across different bias Simulation robustness Here we show a table of the different models, empirical, physics-based and ANN- based. There are numerous models presented here, and we can support them all. The question is “which model would be right for you?” If you do not have process information, then we would recommend an empirical model such as Angelov-GaN. These are easier to fit and give pretty good results. If you do have process information, then a physics-based model will yield better accuracy under large signal conditions. Why is that? When you use the device under large signal operation, that often puts the device in a region where the device was not measured or characterized. So we have to rely on the quality of the model to accurately extrapolate to this region. And usually, a physics based model is better at doing that than an empirical model. Here’s another huge benefit of the physics based model: Say you are a fab house. And you have evolved your process recipe to increase the mobility and reduce the contact resistance. With an empirical model, you would have non-physical parameters, and you would need to extract the entire model again for a different device. Instead with a physics based model, you can predict what will happen at the circuit level with your new mobility and contact resistance. Lastly, in the 3rd column, we have the ANN based models, or the DynaFET. This will lead to the best possible accuracy. But’s it’s hard to do. It’s hard to get started, so it may require a service agreement to help you get started. Raj’s picture of ASM-HEMT * limited

Angelov GaN Model Empirical Model Updated to include better fits on harmonics, frequency dispersion, capacitance, etc. Strengths: Simple extraction method Industry workhorse for 10+ years. Limitations: Extracted from S-pars  might not work well for large signal Workaround - adapt model card for target bias point The Angelov-GaN model is at its core an empirical model. So it’s about finding the best equation to fit the measured data. Over the years, this model has been updated to include better gate diode current, trapping. Industry workhorse for many years. Limitations: derived from SS S-pars. And remember from our previous discussion, it might not work well for large signal applications. As a commonly used workaround, one may adapt the model card for a target bias point. For 5G, we will require model cards that are accurate across multiple bias points. Next I’ll cover the DynaFET model. Ids = Ipk(1+tanh(y))(1+lVds)tanh(aVds) y = P1(Vgs – Vpk) + P2(Vgs – Vpk)2 + P3(Vgs – Vpk)3

MIT Virtual Source GaN (MVSG) Model
Charge-based Physics Model ID/W=Qi(x0)vxo VS VG VD Leff x EC vxo xo Qi(xo) Rs Rd VS point vSi vDi xo Leff Lg x 2DEG 2DEG Qinv,s Qinv,d Source and drain end charge Let’s first talk about the MVSG model or the “MIT Virtual Source GaN model.” The origins of the MVSG model come from the work of MIT researchers trying to figure out how to model carrier transport in short channel silicon CMOS devices. In August 2009, a researcher named Khakifirooz laid down the foundation for just such a model. Some years later, an MIT PhD student named Ujwal Radhakrishna extended this basic framework from CMOS and customized it for GaN specific effects like field plates, trapping, access regions, thermal, etc. … You might ask “what do we mean by virtual source?” The virtual source is a point along the channel underneath the gate where potential energy barrier that electrons must cross is maximum. You can see the little diagram there on the right. In other words, it’s at the peak of the electron conduction band profile, and it’s just inside the channel. OK so how do we calculate current in this device? <click> It’s about calculating charge, and then moving it. Let's break down this seemingly innocent looking expression. <click> vsat is the saturation velocity, or the maximum velocity that charge can travel through the channel. <click> The Q term is the virtual source charge. For long channel GaN devices, the Q terms include the charge terms at the source and drain end of the channel. <click> The "F" term is actually a function that allows us to gracefully transition from non- velocity saturation to velocity saturation condition. <click> Here’s the expression for the F term. As you can see, it depends on the channel charge and a fitting parameter Beta. <click> Next, here is the expression for the channel charge at the source side. <click> notice that it depends on the intrinsic voltage at the source. <click> Similarly, here is the expression for the channel charge at the drain side. <click> And similarly, this depends on the intrinsic voltage at the drain. BTW, we are collaborating with the originator of the MVSG model to develop an RF extraction flow. So now let’s move on to the ASM model. <end> Saturation velocity Function for transition from non-velocity-saturation to vel sat Source: Dr Ujwal Radhakrishna, MIT RF GaN Modeling Webinar

ASM-HEMT Model Surface Potential Physics Model
= Surface potential along the 2DEG Solve for y and charge on the channel Derive unified expression for Id(Vgs, Vds) Velocity Saturation Short Channel Effects Channel Length Modulation Self-Heating Effects 2DEG Charge Fermi Level Surface Potential (From previous slide: “Alright, let’s move on to the ASM model.”) The ASM model is a physics-based surface potential model. Taking the example of the famous CMOS BSIM-CMG model, the ASM model developers worked really hard to solve for the surface potential along the 2D electron gas channel. The expression for Id depends on surface potential as a proxy for the sheet charge that you saw in the previous slide. The genius of this model was to combine the equations of different regions of operation into one unified equation that describes drain current everywhere. More mathematical gymnastics followed with the addition of effects like velocity saturation, short channel effects and channel length modulation. We have an ongoing relationship with the originator of this model, Dr. Sourabh Khandelwal. We are currently funding research on a new formulation of this model that does not require process information. This would be great for many power electronics engineers that may only have access to a packaged device. Capacitance 𝐼 𝑑 = 𝜇 𝑒𝑓𝑓 𝜃 𝑠𝑎𝑡 2 𝜓 𝑑𝑠 2 𝑊 𝐿 𝐶 𝑔 𝑁 𝑓 𝑉 𝑔𝑜 − 𝜓 𝑠 + 𝜓 𝑑 𝑉 𝑡ℎ 𝜓 𝑑𝑠 Dasgupta, Ghosh, Chauhan and Khandelwal, “ASM-HEMT: Compact model for GaN HEMTs”, IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC), June 2015 RF GaN Modeling Webinar

The DynaFET Model Thermal Model Trapping Model
Dynamic self-heating, trapping, ANN IG QG ID QD Thermal Model The DynaFET model is a measurement-based model that works with Advanced Design Systems. Jianjun Xu teamed up with David Root to create this model. They looked across the modeling landscape and saw how are numerous models would unsuccessfully try to bridge the gap between small signal and large signal applications. The two biggest problems were dynamic self-heating and trapping. So they said, “why can’t we just use a rich set of large signal data for the extraction? That will exercise both effects to give us the information we need.” The result? Probably the most accurate GaN model in the world. At the heart of this model, the DynaFET uses artificial neural networks or ANN’s to describe device charges and currents. OK, what are ANN’s? ANNs are a framework of machine learning algorithms loosely based on how the brain works - they can smoothly approximate any nonlinear function in terms of a network of highly interconnected processing functions called neurons. A machine learning training algorithm identifies the weights between nodes to optimally and smoothly approximate the nonlinear current and charge functions. Trapping Model Artificial Neural Network

DynaFET Modeling With the combination of the NVNA data and the ANN at its core, the DynaFET model accurately models all aspects of the device: DC IV, transducer power gain, fundamentals and harmonics, PAE, etc. So this is a highly accurate model that quite naturally captures trapping and self- heating effects. We have an extraction package that works with the ADS model in IC- CAP, so the extraction of the ANN weights is now turn-key. DynaFET model shows excellent agreement in all operation regions of the GaN HEMT. Universal applicable modeling approach.

Device Modeling Seminar
Lots of Parameters Here, we show the ASM model card, which has 199 parameters. This may seem scary, but we have developed the extraction procedure and know how to navigate the different categories of parameters: basic, quantum, capacitance, field plate, access region, etc. If you happen to have a lot of experience developing your own extraction flows, that’s no problem. If you need help, we can engage our services team to provide a custom flow for your devices and modeling training. Device Modeling Seminar

Sources of Data S-Parameters C-V DC-IV, Pulsed-IV

C-V Data Derived from Cold FET S-parameters
Params: CGS, CGD, CDS Possibly CG

High Level Extraction Flow
DC Linear Condition IdVg at low Vds 2) IdVg at high Vds 3) Full Output Curves Here, I illustrate some of the steps in a high-level extraction flow for the ASM model of DC drain current. As much as we would love for this to be a science, there is an art to dialing in 199 parameters. Generally, we start with first-order effects and then add second-order effects, like temperature dependence. The trick is to bias and measure the device in specific regions to isolate the effect of only a small number of parameters. Now these values can be calculated or inferred. Some parameters can be extracted in a closed-form manner, others via tuning and still others through optimization. Models are becoming increasingly complex, so we find that as parameter values change, the sensitivity of the remaining parameters may change. Some parameters have no effect on the curves until other parameters have been set. There might be multiple effects to describe a physical phenomenon, where one effect dominates all the others. You can think of the collection of all the steps as a recipe. We call it an extraction flow. And due to the complexity of the model and interactions between model parameters, the extraction flow tends to be an iterative flow rather than a straight line start-to-finish linear flow. ================================ prev notes === Using IdVg data at VDS = 0.1 V, we can if the data using some first-order terms like VOFF, which sets the cutoff voltage. It’s analogous to the threshold voltage in CMOS devices. U0, UA and UB primarily impact the maximum current possible and the shape of the subthreshold. NFACTOR impacts the slope in the sharply rising region of this curve. Then we look at IdVg at higher Vds, and observe how the curves evolve as a function of Vds. ETA0 and CDSCD try to capture the spread with respect to Vds in the high sloping region of the curve. NS0ACCS and NS0ACCD help you get to the target current at high Vgs. You will probably need to reoptimize VOFF was to have accounted for the Vds dependence. Next we look at the output curves. LAMBDA helps capture the channeling modulation as a function of Vds, so just like in a MOS device it will you some indication of Rds at low Vgs. But then you might observe that the curve at high Vgs as a negative slope vs. Vds. Self heating parameters like RTH0, UTE, AT and KT1 help capture this effect. Source and drain contact resistances RSC and RDC impact the shape of the knee in the IdVd curves. 1) Khandelwal et. al – “Nonlinear RF modeling of GaN HEMTs with industry-standard ASM GaN Model (Invited)”, BCICTS, Oct 2018. 2) Takashi Eguchi Params: VOFF, NFACTOR U0, UA, UB Params: LAMBDA, RSC, RDC RTH0, UTE, AT, KT1 Params: ETA0, CDSCD, VSAT, VSATACCS NS0ACCS, NS0ACCD VDSCALE, VOFF (again) Khandelwal, et al, BCICTS, 2018 Device Modeling Seminar

High Level Extraction Flow, part 2
RF Core GaN Model The drain equations for the ASM, MVSG and Angelov models only apply to the intrinsic device. They assume that you will manage and customize all the parasitic components surrounding the intrinsic device. The networks representing the parasitic components play an increasingly important role at higher frequencies. So when you make an on wafer S-parameter measurement, it is kind of like peeling an onion from Level 3 (in green) to Level 2 to Level 1. Let’s assume that you have completed an on wafer calibration that brings you right up to the edge of the device structure. These extrinsic measurements will get you to the level-3 measurements. We need to go from level-3 to level-2. The Gate and Drain manifold structures are simply passive structures, so this is relatively easy. Using either EM simulations or by using measurements of the structures alone, we will de-embed their effects. Next, we need to go from level-2 to level-1. To do this, we use measurements where the device is biased in a cold FET condition: Vds = Vgs = 0. Note that with Vgs = 0, the device is strongly ON, but since Vds = 0, there is negligible current flow. Based on what model you use for the networks between level II and level I, one can derive the two port parameter conversions and data manipulations required to tease out the elements in this network. This can get fairly complex fairly quickly, depending upon how complex the parasitic networks are. We would need a tool that can handle these array manipulations and keep the data organized. =============================================== Prev Notes: De-embed gate and drain manifolds to get to Level-2 Cold FET Z11  Zeg-parameters Now you have intrinsic device S-pars  Y-params imag(Y12)  CGDO, CGDL imag(Y22)  CDSO, CJ0 imag(Y11)  CGSO real(Y-pars)  Gm and Gds Dispersion in Gds  CTH0 Nonlinear Circuit Simulation and Modeling, Cambridge University press Khandelwal, et al, BCICTS, 2018 Device Modeling Seminar

Okay, let’s see what it looks like to set parameters. Here you can see we are playing with this one term RDC, and it seems to affect the shape of the knee in the DC IV curves. Next, we play with a mobility term called U0, which affects the maximum current and all the IdVd curves. Then we also look at Rth0 or the thermal resistance of the part. UTE has a similar effect on the high Vd region. We hit the optimize button to get all these parameters optimized at once. Now, if we want to add another plot to be considered for parameter tuning, we simply select that plot. Okay let’s say we’ve done a bunch of tuning and we ended up in a very bad space. We want to go back to an earlier parameter set. Luckily, we can do that by cycling through the model parameter history pull down. RF GaN Modeling Webinar

Large-Signal Model Validation
ADS Schematic for simulation of load-pull contours 22 dBm 10 GHz Pout & PAE load pull contours for 10 mA/mm Pout & PAE load pull contours for 100 mA/mm [1] S. A. Ahsan et al., IEEE J. Electron Devices Society, Sep., [2017]

To Recap… GaN technologies will continue to grow.
Leverage example extraction flows for the latest ASM-HEMT and MVSG models With an industry standard tool – IC-CAP We can help. keysight.com/find/eesof-iccap keysight.com/find/eesof-innovations-in-eda keysight.com/find/free_trials In summary… GaN technologies will grow in market share in the next 5 years. RF GaN modeling is challenging, but it’s extremely important for companies to move quickly on new designs. Getting this wrong is easy to do, and costly. Fortunately, we can help with both model extraction flow and training. <click> My call to action is: Let’s work together to enable better designs with better models! Here are some links to help you get started in IC-CAP if you have never used IC-CAP before. I’m happy to report that we worked with both professors Khandelwal and Radhakrishna to develop new extraction flows for the ASM and MVSG model. These are now ready as a part of our early-access program, and you’ll get to see a live demo shortly.

Appendix

Active Source Injection Setup
Synchronized Bias Supplies Keysight E5270B or 4142B Input power Active Load Pull A21 power A21 phase gate bias drain Temperature control PA Bias T Dynamic Load-lines NVNA waveforms To provide inputs to the DynaFET model extraction program, we need a rich set of data, and I mean rich. The required data set involves exercising the device at numerous bias points, operating temperatures, input power levels and output load impedances. For each one of these measurement conditions, we measure incident and reflected power, both real and imaginary, at the input and the output of the device. And we do this at a fundamental frequency of 100 MHz and 20 harmonics thereof going up to 2 GHz. Just to give you an idea, for my device, it led to about half a gigabyte of data per temperature. That’s a lot of data right? <click> If we convert the harmonic data into time domain waveforms, we can plot dynamic load-lines, or instantaneous drain current versus drain voltage. The graph on right represents 50,000 waveforms. Why 50000? It so happens that trapping and self heating have hysteretic effects. In other words, if you sweep one way versus another, that will lead to different curves. So by varying everything, we capture the trap behavior from all angles, which is important for the model. <end> DynaFET characterization performed at various: (1) DC biases (2) Input powers (3) Load Impedances, magnitude and phase (4) Ambient temperatures (5) Fundamental frequency + 20 harmonics

Verilog-A File: mvsg_cmc.va
DUT Test Circuit Model Circuit Page Verilog-A File: mvsg_cmc.va

DUT Test Circuit Automatically added by IC-CAP

Modeling Challenges: Trapping and Current Collapse
Need Trap Models I would like to highlight some of the challenges in modeling these GaN devices. Let's say we bias a device as a class A amplifier. As we increase the input power, the voltage swing at the gate is going to lead to ever larger voltage swings at the drain. Imagine a voltage swing of 30 V on top of a quiescent bias of 28 V. With peak drain voltages approaching 60 V, we expect the average current to go up in a straightforward fashion as shown in the red line. But in fact, that does not happen! Instead, here's the measured data, shown in blue. <click> All the DC IV curves slump downwards, and so the average current dips down as a function of input power. Now it appears as if we are now in class AB operation. We call this phenomenon current collapse. <click> What is the explanation for this current slump? Current collapse is commonly understood to be caused by trapped electrons, <click> either deep in the GaN layer or along the access region between the gate and the drain. It turns out this is quite challenging to model. Evidence of this behavior is not contained in S-parameters or DC I-V data. The only way to capture this effect is under large signal simulation conditions. Current collapse Jardel, et al - "An Electrothermal Model for AlGaN/GaN Power HEMTs Including Trapping Effects to Improve Large-Signal Simulation Results on High VSWR", MTT, Dec 2007 Agnihotri et al (IITK), INDICON 2015

Drain Lag But: Which I-V curves to use?
Pulsed IV measurements Id (mA) [-0.2, 4] [-0.8, 6] “knee walkout” Here we are showing PIV measurements. In these measurements, we sit at a Q bias point and then pulse very quickly to a target measurement point to read the device current. And then, we quickly return to the Q bias point. In this case, the Q bias point has Vgate = -0.2 V and Vdrain = 4 V. Now let’s see what happens if we change the Q bias point. <click> Here the Q drain voltage, where the device sits most of the time is now 6 V and the gate voltage is -0.8 V. Notice the dramatic shift in IV curves! We have this phenomenon called knee walkout. And for every other Q bias point, there is a different set of IV curves. It is mind-blowing to try to capture this infinity of IV curves. What’s happening here? The answer is that we quickly capturing charge when we increase drain voltage and decrease gate voltage. This is what we call FAST CAPTURE. But when we go the other way, dropping Vds and increasing Vgs, the time constant is much slower. So the trap state doesn’t have enough time to respond in the short time in which we make the measurement. We call this time period SLOW EMISSION. The RF manifestation of this knee walkout is a change in PAE and output power, and this is one of the biggest challenges in RF GaN modeling. Vds (V) But: Which I-V curves to use? How to relate PIV curves to model coupling terms (trapping model)? Quiescent Bias Point

GaN HEMTs: Understanding carrier transport
GaN (3 nm) Al0.26Ga0.74N (18 nm) GaN 1.2μm RF-GaN HEMT HV-GaN HEMT Source access region Intrinsic transistor Drain access region vIg vg vIg vS vSi vDi vD DD transport DD transport DD transport Physics-based GaN HEMT transport and charge model: Experimental verification and performance projection - U. Radhakrishna, L. Wei, DS. Lee, T. Palacios, D. Antoniadis, 2012 International Electron Devices Meeting. MIT Virtual Source GaNFET‐High Voltage (MVSG‐HV) model: A physics based compact model for HV‐GaN HEMTs - U. Radhakrishna, T. Imada, T. Palacios, D. Antoniadis, Physica status solidi (c) 11 (3‐4), , 2014.

Nonlinear source/drain access region resistance model
Nonlinear variation of source/ drain access resistances with Ids. Geometrically scalable

MIT Virtual Source GaNFET compact model
The “Virtual Source” model: A simple, physical short channel FET model – Saturation Example Qixo Above-threshold ID/W=Qi(x0)vxo VS VG VD Leff x EC vxo xo Qi(xo) Rs Rd Below-threshold VDSAT b=1.4 b=1.8 Example of function Fsat Saturation Linear Few Physical/Measured Device Parameters -Khakifirooz et al., IEEE TED 2009

MVSG model: Modeling device current
vG VS point Source and drain end charge vSi vDi xo Leff Lg x Saturation velocity 2DEG 2DEG Qinv,s Qinv,d Function for transition from short to long gate lengths Accuracy Convergence MVS approach V, T, f dependence Extensive calibration Circuit evaluation Charge-based Nodal symmetry Low node-count Verilog-A Collaborations with Analog Devices Inc, Fujitsu Semiconductor Corp., Toshiba Inc. High voltage GaN HEMT compact model: Experimental verification, field plate optimization and charge trapping- U. Radhakrishna, D. Piedra, Y. Zhang, T. Palacios, D. Antoniadis, 2013 IEEE International Electron Devices Meeting, , 2013.

Rd/s Model Validation with Measurement
Effect of high access region resistance at high Vg Self Heating Ids-Vds reverse Ids-Vds gds The non-linear Rs/d model shows correct behavior for the higher Vg curves in the Id - Vd plot; the S-P based model can accurately capture the reverse output characteristics.

Modeling of Field-Plates in HEMTs
Affects capacitance and breakdown behavior.

Field-Plate Capacitance Modeling

IC-CAP – DUTS/Setups/Transforms
Perhaps serendipitously, we do have such a tool to help organize the data.  In IC- CAP, we do this by creating hierarchical containers called DUTs and setups. As you may see from the tree representation on the left <click>, we group all of our modeling activities into categories, for example we have a DUT called “DC_MODELING.” Different data slices are put into setups <click> such as “ig_vgs_Input” or “id_vgs_Transfer_lin.” These data slices are chosen to isolate regions of device operation where a small set of parameters have the maximal impact. However, that doesn’t mean that other parameters do not have an effect, and that’s why any extraction exercise may need to be iterative. In other words, you may need to dial in the same parameters over and over again as you dial in other parameters. Within each setup, <click> we may choose to set different groups of parameters using transforms. In this case, we have one such transform highlighted aimed at setting the parameters VOFF and NFACTOR. RF GaN Modeling Webinar

Extraction Tuning Optimization What are transforms? The transforms are the nuts and bolts of the model parameter extraction flow. In general, there are 3 types of transforms <click> to tease out the parameters: An Extraction step, where we can directly calculate parameters from the shape of the data. A Tuning step, where we manually adjust a parameter value to see its effect An Optimization step, where a computer algorithm takes over the fine tuning of parameters In each parameter extraction step, <click> we need to simply choose which plots should be employed to change which parameters. <click> These are entered as a list of plots that have already been set up on the plot tab, and so we just call them by name. <click> Next, we choose which parameters we want to be brought to bear on these plots, and these are also called out by name. <end> IC-CAP – Transforms Choose plots + relevant parameters RF GaN Modeling Webinar

RF Model & Extraction (i)
Three step methodology De-embed manifolds Extract the intrinsic core model - Using low frequency Y-parameters Extract Inductances - Using high frequency Y-parameters Measure S-parameters including de-embedding structures Convert to Y-parameters Extract L, C, gm, gds, Rg etc.

RF GaN Device Models and Techniques

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