6.2.1 Physically Based Transistor Models

The physics of semiconductor devices involves a complicated combination of quantum mechanics, solid state physics, material science, electromagnetism, and nonequilibrium thermodynamics. In the early 1950s, William Shockley derived the time-dependent terminal currents at the drain and gate of a field-effect transistor (FET) starting from the Poisson and current continuity partial differential equations [1]. The explicit solutions are shown in (6.1)–(6.4). Shockley made several simplifying assumptions along the way, such as field-independent charge carrier mobility (constant μμ), the gradual channel approximation (the electric field is primarily perpendicular to the active channel), and neglecting the effects of self-heating. A simple derivation can be found in [2] (see also [3]).

IDSt=IDSDCVGStVDSt−dQGDVGDtdt(6.1)

IGt=dQGSVGStdt+dQGDVGDtdt(6.2)

IDSDCVGSVDS=WtotμqNDaεLVDS−232εqNDa2VDS+ϕ−VGS32−ϕ−VGS32(6.3)

QGSV=QGDV=QV=−WtotL2qεNDϕ−V+const(6.4)

Here W^totWtot is the total gate width, LL is the gate length, aa is the channel depth, qq is the magnitude of the electron charge, εε is the dielectric constant of the semiconductor, N_DND is the doping density of the semiconductor (assumed uniform), and ϕϕ is the built-in potential. The time-dependent drain and gate currents, (6.1) and (6.2), are evidently decomposable into distinct contributions from a voltage-controlled current source and two nonlinear two-terminal charge-based capacitors. That is, the solution of the physical partial differential equations leads to a rigorous representation, at the device terminals, in terms of standard lumped nonlinear circuit theory.

The nonlinear equivalent circuit corresponding to (6.1) and (6.2) is given in Figure 6.1. The functional forms (explicit nonlinear functions) of the constitutive relations for the current and charge sources are given by the expressions (6.3) and (6.4).

Figure 6.1 Physics to circuit representation.

While the equivalent circuit of Figure 6.1 is usually justified based on its “resemblance” to the physical structure of the device (see also Figure 5.1), the above discussion provides a deeper explanation and justification. Moreover, in addition to the topology of the equivalent circuit, the details of the constitutive relations are obtained from this analysis.

The electrical performance of any circuit designed using the FET, in combination with other electrical components, can therefore be simulated by solving the set of ordinary nonlinear differential equations of circuit theory, using the methods of Chapter 2, rather than the partial differential equations of physics. In a very important sense, the nonlinear equivalent circuit representation of the transistor is a behavioral model suitable for efficient simulation of components and circuits at higher complexity levels of the design hierarchy [4]. This is quite fortunate, since it is not at all practical to solve the detailed partial differential equations of physics, at the transistor level, for a circuit composed of many transistors.

The constitutive relations, the particular functions for the I–V and Q–V relations (6.3) and (6.4), are closed-form nonlinear expressions involving the controlling terminal voltages, V_GSVGS and V_GDVGD, for the FET. We point out here that the domain of voltages over which these constitutive relations are defined is limited to 0 ≤ V_DS ≤ V_DSsat0≤VDS≤VDSsat where VDSsat=qNDa22ε−ϕ−VGS. At V_DS = V_DSsatVDS=VDSsat, the channel is pinched off and the slope of the I–V curve (the output conductance) is zero. This condition also determines the threshold voltage, V_TVT, as that value of V_GSVGS required to pinch off the channel when the drain-source voltage is zero. We have VT=ϕ−qNDa22ε.

The domain of the model can be extended by defining the channel current to be constant, at the value I_DS(V_GS,  V_DSsat)IDSVGSVDSsat, for values of V_DS>V_DSsatVDS>VDSsat. The terminal voltages, V_GSVGS and V_GDVGD, are also limited to values less than the built-in potential, ϕϕ, at which the constitutive relations would become singular.

The notion that a model, well-defined initially in a limited domain, must have its domain properly extended for robust simulation, is a common and important consideration for successful nonlinear device modeling. We will discuss this in more detail in Section 6.2.5.3.

An important benefit of good physically based models is that the parameters entering the nonlinear constitutive relations tend to be meaningful. This is true for the Shockley model, where parameters include physical constants (e.g., q, the electron charge magnitude) and geometrical dimensions of the device (e.g., the gate width, W, the gate length, L, and the channel depth, a). There are material properties such as εε, the dielectric constant of the semiconductor, and the mobility µ, of the charge carriers. Also entering the constitutive relations is the device design parameter, N_D, the doping density of the semiconductor. Another important feature of the Shockley model, shared by many physically based models, is that the same parameters that enter the I–V constitutive relation (6.3) also enter the Q–V constitutive relations (6.4), coherently linking the resistive and reactive parts of the model through the common underlying physics.

A characteristic of the current-voltage constitutive relation (6.3) for the channel current is that it is a fully two-dimensional function that can’t be de-composed into a product of two one-dimensional functions. That is, generally speaking, we have the condition expressed by (6.5).

On the other hand, transistor technology has evolved rapidly since Shockley’s analysis. The basic physical assumptions that led to (6.3) and (6.4) no longer apply, and it is generally not possible to arrive at simple closed form expressions for the current and charges as functions of the controlling voltages. Ironically, due to the phenomenon of charge-carrier velocity saturation (beyond the Shockley theory based on constant mobility), expressions like (6.5) can be more accurate for modern FET devices than the Shockley model suggests. Indeed, for simplicity of analysis, the approximation I_DS(V_GS,  V_DS)≈g(V_GS) ⋅ f(V_DS)IDSVGSVDS≈gVGS⋅fVDS is used in the design example of Chapter 7.

Physically based models are predictive as long as the particular transistor technology is consistent with the physical assumptions. For example, where the Shockley model applies, the doping density could be changed and the resulting device characteristics would follow from (6.3) and (6.4) just by changing the numerical value for N_D. Of course this also means device technologies based on different principles of operation require distinct physical models, even within the same class of transistors (e.g. FETs). For example, a Si MOSFET and a GaAs pHEMT will have different physical models, despite some semi-quantitative similarity in their measured performance characteristics.

6.2.2 Empirical Models

To deal with the limitations of physically based models discussed in Section 6.2.1.1, so-called empirical models were introduced [10–17] and still play a dominant role in microwave and RF nonlinear design applications, especially in the III-V semiconductor systems (e.g., GaAs, GaN, and InP).

Empirical models assume that the same equivalent circuit topology, suggested by basic physical considerations, is capable of representing the device dynamic characteristics, but that the I–V and Q–V constitutive relations can be treated as generic and independent functions to be chosen based on simple measurements and ease of parameter extraction.

For example, a classical empirical FET model due to Curtice [17] defines constitutive relations by equations (6.6)–(6.9) for the same equivalent circuit as shown in Figure 6.1.

QGSV=−CGS0ϕη+11−Vϕη+1(6.7)

CGSV=CGS01−Vϕ−η(6.8)

For our purposes, V₁V1, in (6.6), can be taken to be the gate-source voltage, V_GSVGS, and V₂V2 the drain-source voltage, V_DSVDS.¹ Contrary to the general case, (6.5), and neglecting the V_DS-dependence of V₁, the channel current (6.6) takes the form of a product of two univariate functions. The polynomial dependence on V₁V1 is chosen for convenience. The corresponding parameters, A₀ − A₃A0−A3 have no physical significance. The parameter, γγ, is related to the voltage at which velocity saturation becomes important – a phenomenon not taken into account by the Shockley theory.

To simulate with the model, numerical values must be specified for each of the model parameters in all of the constitutive relations (6.6)–(6.9), typically by relating them to measurements on a reference device. This is the goal of parameter extraction. Obviously, the specific form of the intrinsic model constitutive relations can influence the parameter extraction strategy – the methodology by which the parameters are assigned numerical values. For the above model, {A_n}An define the gate-voltage dependence of the channel current, γγ determines where the I–V curves saturate with drain bias, and C_GS0, C_GD0, ϕ, and ηCGS0,CGD0,ϕ,andη are parameters determining the charge variation of the two capacitors as functions of their respective controlling voltages.

It is also clear from (6.6)–(6.9), that the channel current and charge storage (nonlinear capacitance) model are less strongly coupled to one another in this model than in the Shockley model. In particular, the built-in potential, ϕϕ, does not even enter the channel current constitutive relation, while it plays a determinant role in the input capacitance model for C_GSCGS.

Equation (6.7) is a standard physically based bias-dependent junction model, with corresponding capacitance value given by (6.8) with typical parameter values in the range 0 < ϕ < 1,  0 < η ≤ 0.50<ϕ<1,0<η≤0.5. Equation (6.9) describes a capacitance with a fixed value independent of bias. Notice that despite having a symmetric equivalent circuit topology, Figure 6.1, the different functional forms for Q_GS(V)QGSV and Q_GD(V)QGDV makes the Curtice model quite asymmetric with respect to the source and drain. Compare this to the fully drain-source symmetric Shockley charge model where the two nonlinear capacitors have the same Q–V functional form given by (6.4). The significance of these statements will be discussed in the context of symmetry principles and their consequences, presented in Section 6.5.

6.2.3 Quasi-Static Models

Both the Shockley model of (6.1)–(6.4) and Curtice model of (6.6)–(6.9) are quasi-static models. That is, the constitutive relations depend only on the instantaneous values of the controlling variables (voltages in this case). There is no explicit time or frequency dependence in the relationships. This means, for example, that the I_DSIDS current source will respond, instantaneously, to the applied intrinsic voltages, no matter how quickly the voltages may change with time. Similarly, the charge functions will respond, instantaneously, to the time-dependent applied intrinsic voltages.

An important consequence of a true quasi-static model, be it empirical or physically based, is that the resistive part of the intrinsic high-frequency RF and microwave model is fully determined and follows completely from the DC model characteristics. However, in real RF and microwave applications, the physical transistor is almost never operated in a true quasi-static condition. The RF signal is almost always much faster than the important thermal dynamic response, something we have not yet considered. For FETs, we will examine electro-thermal modeling in Section 6.6.

6.2.4 Empirical Models and Parameter Extraction

Since the constitutive relations of empirical models are chosen to fit the data, any deficiency of fit – at least for the I–V model fit to DC data – can be rectified by increasing the complexity of the function. For example, higher order polynomial terms can be added to the Curtice model I_DSIDS constitutive relation (6.6). A problem with this approach, however, is that whenever the constitutive relations are changed, the parameter extraction process may also need to be modified.

Additionally, errors in the measurements will generally affect the model, since the parameter extraction process usually finds the best fit to the experimental data. The reader may wish to review, at this point, the more detailed discussion in Chapter 1, Section 1.5.1 about fitting static transfer functions.

For microwave FET devices of various types, several empirical models, notably the EEFET model family [14] and the Angelov (Chalmers) model family [15] and [16] are popular. The latter has evolved over 20 years to include many important dynamic effects such as self-heating and other dispersive or non-quasi static effects.

6.2.5 Nonlinear Model Constitutive Relations

6.2.5.1 Good Parameter Extraction Requires Proper Constitutive Relations

Even a simple intrinsic model constitutive relation, like that of Eq. (6.6), can be extracted improperly leading to disastrous results. The straightforward way to extract parameters in (6.6) is to measure I_DSIDS versus V₁V1, say at fixed V₂V2, for which the tanh(.) term is effectively unity, and solve for the {A_n}An coefficients using a robust least-squares fitting process. Negative consequences of this direct approach appear when the constitutive relation (6.6) is evaluated outside the range of bias used to extract the coefficients. The resulting channel current may take physically unreasonable values for reasonable values of V₁V1. This is illustrated in Figure 6.2 [18–21]. The polynomial model may never pinch off (even if the device does), or the current can become large and negative and therefore unphysical in other cases.²

Figure 6.2 Problems caused by naïve parameter extraction methods for poorly defined constitutive relations. Data (x), models (lines). Top: Evaluation of polynomial beyond domain of validity (dashed lines). Bottom: proper extension of constitutive relations.

6.2.5.3 Regularizing Poorly Defined Constitutive Relations: An Example

The solution to making (6.6) well-defined can be obtained by enforcing additional constraints on the model – namely that it pinches off and attains a maximum value – to make it physically reasonable for all values of the independent variables. Specifically, the model channel current should be constrained to be zero for all values of voltages at or below a value of V₁ = V_TV1=VT, the threshold voltage. Continuity of the channel current and its first derivative at V_TVT means IDSV1=VTV2=∂IDSV1=VTV2∂V1=0. Therefore, the cubic polynomial factor in (6.6) has a double root at V₁ = V_TV1=VT, allowing us to factor out (V₁ − V_T)²V1−VT2 from the general cubic expression, making it easier to obtain the remaining polynomial coefficients. We can also assert that there is a value, V₁ = V_maxV1=Vmax, where the current attains its maximum value and is constant for all higher values of V₁V1. These conditions enable us to reformulate (6.6) in terms of the three new parameters V_T, V_max, I_maxVT,Vmax,Imax as given in (6.10).

IDV1V2=0V1<VTImaxV1−VT2Vmax−VT3Vmax−VT+2Vmax−V1tanhγV2VT≤V1≤VmaxImaxtanhγV2V1>Vmax(6.10)

Equation (6.10) satisfies all the constraints required for a well-defined and reasonable constitutive relation [18–21],³ provided only V_T < V_maxVT<Vmax and I_max and γImaxandγ are positive. Moreover, the new parameters now have a clear interpretation in terms of minimum and maximum values of the model current (see Exercise 6.1). However, this formulation of the model with corresponding parameter extraction methodology has used up all of the original fitting degrees of freedom in Eqn. (6.6). Distortion figures of merit, such as IM3, are completely determined by the three parameters, V_T, V_max, I_maxVT,Vmax,Imax. For real transistors, variations in the doping density can result in devices with identical values of V_T, V_max, I_maxVT,Vmax,Imax but with different shapes of the I–V curves between zero and I_maxImax. Therefore, more general and flexible constitutive relations than those of (6.10) are required to independently and accurately model intermodulation distortion. See also [22].

It should be clear from the above discussion of the channel current constitutive relation that the charge constitutive relation (6.7) also needs to be extended for values of V_GSVGS approaching ϕϕ and beyond. This is easily accomplished by linearizing (6.7) at some fixed voltage, V₀ < ϕV0<ϕ. The result is given by Eq. (6.11).

QGSV=−CGS0⋅ϕη+11−Vϕη+1V<V0−CGS0⋅ϕη+11−V0ϕη+1+CGS0⋅1−V0ϕηV−V0V≥V0(6.11)

6.2.5.5 Comments on Optimization-Based Parameter Extraction

For constitutive relations more complicated than (6.10), parameter extraction generally involves a simulation-optimization loop. An example of the general flow is given in Figure 6.3. However, such direct approaches can be slow. The model must be evaluated and parameters updated many times before a good result is obtained. Gradient-based optimization schemes may be sensitive to the initial parameter values or get stuck in local minima in the cost function (the error function between the desired value and actual value of the simulation at particular values of the model parameters). There are other techniques, such as simulated annealing [23] and genetic algorithms [24] that can help find a global solution to the nonlinear optimization problem, but these techniques are usually much slower and more complex.

Figure 6.3 Optimization-based parameter extraction flow.

The parameters in (6.11) must be constrained not to take specific values during optimization where the constitutive relations might become singular (e.g., for η =  − 1η=−1). Modern parameter extraction software usually allows the user to restrict the parameter values to specific ranges during the iterative optimization process.

Advanced nonlinear models (see, e.g., [25]) have complicated nonlinear electrical constitutive relations. Most of these electrical nonlinear constitutive relations have nonlinear thermal dependences as well. It is usually best to extract parameters of such models by using an iterative scheme where dominant electrical parameters are extracted from specific subsets of data to which those parameters exhibit high sensitivities. A good flow is necessary for good, global fits to the data and also to get physically reasonable parameter values, which can then be scaled to model devices of other sizes without the need for an additional comprehensive extraction.

Of course, a given model with fixed, a priori closed form constitutive relations may never give sufficiently accurate results, for any possible set of parameter values. The model may be flawed, or just too simple to represent the actual behavior of the device. We turn now to other approaches that are more flexible.

6.2.6 Table-Based Models

Table models are usually classified as extreme forms of empirical models since the constitutive relations are not physically based but depend directly on measured data. In fact, there are no fixed, a priori model constitutive relations with parameters to be extracted at all. Table models are examples of “nonparametric” models. The data are the constitutive relations. The idea is simple enough, at least for the I–V constitutive relations of a quasi-static model. For the resistive model, just measure the DC I–V curves, tabulate the results, and interpolate as needed during simulation to evaluate the constitutive relations and their derivatives during the solution of the circuit equations. For the nonlinear reactive model, much more analysis needs to be done. We will address this in Section 6.2.9.

6.2.6.1 Nonlinear Rereferencing: Extrinsic-Intrinsic Mapping between Device Planes⁴

The intrinsic model nonlinear constitutive relations are defined on the set of intrinsic voltages, VGSint and VDSint, after accounting for the voltage drop across the parasitic resistances when voltages VGSext and VDSext are applied at the extrinsic device terminals (see Figure 6.4). Measured I–V data, on the other hand, are defined on the extrinsic voltages that correspond to the independent variables of the characterization. The relationship between extrinsic and intrinsic DC voltages is simple, given the resistive parasitic element values, previously extracted, as explained in Chapter 5, and the simple equivalent circuit topology. The equations are given in (6.12) [26]. An important issue for table models, however, is that the extrinsic voltages at which the measurements are taken are usually defined on a grid, but the resulting intrinsic voltages, explicitly computed by substitution using (6.12), do not fall on a grid, and therefore cannot often be directly tabulated. This is shown in Figure 6.5.

VGSintVDSint=VGSextVDSext−Rg+RsRsRsRd+R⋅IGDCIDDC(6.12)

If the measured extrinsic I–V data is fit or interpolated, equation (6.12) can be interpreted as a set of implicit nonlinear equations for the extrinsic voltages, VGSextandVDSext, given specified intrinsic voltages VGSintandVDSint, [18,26]. Solving (6.12) in this sense enables the data to be re-gridded on the intrinsic space so that the terminal currents can be tabulated as functions of the intrinsic voltages.

Figure 6.4 Extrinsic and Intrinsic device planes for nonlinear constitutive relations re-referencing

Figure 6.5 Extrinsic (gridded) and corresponding intrinsic (nongridded) voltage domain of a FET.

Modeling the measured I–V data as functions of the intrinsic voltages reveals characteristics quite different from the model expressed in terms of extrinsic data. This is shown in Figure 6.6. In part (a) of the figure, the modeled I–V curves as functions of the applied (extrinsic) voltages VGSext and VDSext are plotted. In part (b), intrinsic I–V modeled constitutive relations, defined on VGSint and VDSint are plotted. There is a big difference between Figure 6a and b, especially around the knee of the curves. This process also makes clear that errors in parasitic extraction – where the values of the terminal resistances are determined – can distort the characteristics that we would otherwise attribute to the intrinsic model.

Figure 6.6 FET model I–V constitutive relations expressed as functions of (a) extrinsic and (b) intrinsic voltages.

Alternatively, one can tabulate the extrinsic I–V data, as measured on the original grid, and treat the coupled equations (6.12) as additional model equations to be solved dynamically during simulation. This allows the simulator to sense the intrinsic voltages and look up the associated interpolated values of the measured I–V curves consistent with the solution of (6.12). This saves the post-processing step of regridding during the parameter extraction but adds the two nonlinear equations (6.12) to the model, thus increasing simulation time.

Table-based models can be both accurate and general. The same procedure and modeling infrastructure can be used to model devices in very different material systems (e.g. Si and GaAs) and manufacturing processes [26]. An example of the same table-based model applied to Si and GaAs transistors is given in Figure 6.7. For physically based models, each transistor would have very different constitutive relations requiring different parameter extraction procedures.

Figure 6.7 Table-based I–V models (−) and measurements (×) for Si MOSFET and GaAs pHEMT transistors.

6.2.6.2 Issues with Table-Based Models

A critical issue with table-based models is the nature of the interpolation algorithms that define the constitutive relations as differentiable functions on the continuous domain containing all the discrete data points stored in the tables. The interpolator needs to define the partial derivatives continuously, and extrapolate appropriately – the same conditions that apply to all constitutive relations. At relatively small signal levels, it has been shown that simulations of harmonic distortion can become inaccurate when the amplitude (in volts) of the signal is comparable to or smaller than the distance between voltage data-points at which the constitutive relations are sampled [27]. This is demonstrated in Figure 6.8. At small voltage swings associated with low power signals, simulation results depend on the mathematical properties of the interpolant between data points rather than the underlying data itself. For larger signals, the corresponding applied voltage swings average out the local characteristics of the interpolant, and the table-based model simulations become quite accurate. It can sometimes help to increase the density of the data points. For some spline schemes, however, this can make the interpolant oscillate nonphysically between data points. Ultimately, there is a practical limit on taking too many data points leading to increased measurement time, file size, and interpolation of noise [27].

Figure 6.8 Distortion predictions from table-based models (solid lines) at (a) large and (b) small-signal amplitudes compared to empirical model (black dots) and measured data (dashed lines).

Various types of spline-based models have been explored in the literature. Methods involving B-splines have been applied in [28], but unphysical behavior due to spline oscillations between knots can still sometimes occur. Variation-diminishing splines [29] can tame oscillations, but their low polynomial order precludes their use for intermodulation simulation. “Smoothing splines” [30] can have a variable spline order and trade off accuracy for smoothing noise.

For good large-signal simulation results with table-based models, it is necessary to acquire data over the widest possible range of device operating conditions. This range should include regions up to or just into breakdown, high power dissipation, and forward gate conduction, since these phenomena are critical to limiting the large-signal RF device performance. A portion of the domain of static measurements is shown in Figure 6.9 for a GaAs FET. The boundary illustrates the major mechanisms that constrain the data to the interior of the boundary. The precise shape depends on the detailed device-specific characteristics and the compliance limits on currents, set by the user, on the measurement equipment [31,32]. Covering a wide range of device operation during characterization reduces the likelihood of uncontrolled extrapolation during simulation and possible poor convergence as a consequence. Unfortunately, these extreme operating conditions can stress the device to the point of changing its characteristic during characterization [27]. This is especially true for static operating conditions at which DC I–V and S-parameters are measured. An excellent model of a degraded device can be obtained unless care is used. A delicate balance must be maintained between complete characterization and device safety. It is therefore very important to defer stressful static measurements until as late as possible in the characterization process [27].

Figure 6.9 Data domain for pHEMT device with compliance boundaries.

6.2.7 Models Based on Artificial Neural Networks (ANNs)

Many of the problems of table models, including issues of gridding, ragged boundaries, and poor interpolation properties, can be obviated by replacing the tables with artificial neural networks (ANNs) [33–36]. An ANN is a parallel processor made up of simple, interconnected processing units, called neurons, with weighted connections and biases that constitute the parameters [37,38]. A schematic of an ANN is given in Figure 6.10. Each neuron represents a simple univariate nonlinear “sigmoid” function, with range zero to unity, monotonically increasing, and infinitely differentiable with respect to its argument. The layer structure and interconnectedness of the neurons – specified by the weights – endows the overall network with powerful mathematical properties. The Universal Approximation Theorem states that any nonlinear function, in an arbitrary number of variables, can be approximated arbitrarily well by such a network [37].

Figure 6.10 ANN illustrating sigmoid function, layer structure, and mathematical formula.

ANNs provide a powerful and flexible way to approximate the required model multivariate constitutive relations by smooth nonlinear functions from discretely sampled scattered data. ANNs provide an alternative to using multivariate polynomials, rational functions, or other more conventional basis sets to approximate the data. There are now powerful third party software tools [38,39] available to train the networks, that is, extract the weights and biases that form the parameters of the resulting function, such that the network approximates the measured nonlinear constitutive relationship well.

A key benefit of ANNs is the infinite differentiability of the resulting constitutive relations, providing a smooth approximation also for all the partial derivatives necessary for good low-level distortion simulation.⁵ Another key benefit is that ANNs can be trained on scattered data. In particular, they can be trained directly on the scattered intrinsic I − V^intI−Vint data without the need for regridding. An example of an ANN I–V constitutive relation trained on the nongridded intrinsic voltage space of a pHEMT device is presented in Figure 6.11. Hard constraints on model constitutive relations, such as required by discrete symmetry properties, can also be accommodated by ANN technology. Symmetry conditions are discussed in Section 6.5. An example of an ANN-based FET model with drain-source exchange symmetry is presented in [34].

Figure 6.11 (a) ANN FET I–V model (x) and data (circles). (b) Nongridded intrinsic voltage domain.

The mathematical form of an ANN nonlinear constitutive relation is a very complicated expression, typically involving many transcendental functions – even nested transcendental functions if there are multiple hidden layers. The expression can take many lines of mathematical symbols just to write out explicitly. However, from the point of view of the simulator, it is just a closed-form nonlinear expression like that of (6.10). The implementation of an ANN-based model in the simulator requires the values of the neural-based constitutive relations and their partial derivatives at all values of the independent variables, just like any conventional compact model. The parameters (weights and biases) can be placed in a datafile and read by the simulator for each model instance. The partial derivatives can be efficiently computed by evaluating a related neural network, called the adjoint network, obtained from the original network and weights [40].

6.2.8 Extrapolation of Measurement-Based Models

Conventional parametric empirical models, when properly formulated, are well-defined everywhere, even far outside the training data used to extract the parameters. As we have discussed, this can be achieved by defining the constitutive relations first in a bounded domain and then appropriately extending this domain, often by linearization.

More of a challenge is how to systematically extend the domain of table-based or ANN models. Table models based on polynomial splines can extrapolate very poorly, causing failures in convergence of simulation. An example of a table-model extrapolation is shown in Figure 6.12a. The symbols indicate the actual data points. The solid lines correspond to the table-based model. Within the region of actual data, the model fits extremely well. At high drain voltages, the model extrapolates and the model curves cross in a nonphysical way. Eventually, the drain current of the model becomes negative (nonphysical) and the model does not converge robustly.

Figure 6.12 Extrapolation of measurement-based FET models. Data (+), model (lines): (a) without guided extrapolation, (b) with guided extrapolation.

ANN models don’t diverge as rapidly as polynomials, but their extrapolation properties are also poor from the perspective of simulation robustness. Successful deployment of table-based or ANN models can depend on a good “guided extrapolation” to help the simulator find its way back into or near the training region should it stray far from the region at any particular iteration. The method reported in [41] defines a compact domain containing the training region in terms of the convex hull constructed from the data points themselves [42]. Inside this region, the table or ANN is evaluated. Outside the boundary, an algorithm is applied to smoothly extend the current constitutive relation in a way that sharply increases the model branch conductances outside the training range. An example is shown in Figure 6.12b. This method increases the robustness of DC convergence and also the maximum power levels at which the model converges in large-signal harmonic balance analysis. Transient analysis becomes more robust as well. Other methods of extrapolation of ANN models can be found in [35,43].

6.2.9 Large-Signal Model Connection to Small-Signal Data

This section shows how to establish a connection between a large-signal model and small-signal measurements, in particular, linear S-parameter measurements. This is important for a variety of reasons, but primarily for parameter extraction based on readily available, calibrated data obtained from vector network analyzer instruments.

6.2.9.1 Linearization of Large-Signal Models around a DC Operating Point

We already encountered this procedure in Chapter 3 where we derived an S-parameter model from a simple nonlinear model of a FET. Here we derive small-signal models from large-signal models more generally, but still assuming a simple lumped description. We assume the following model for the intrinsic nonlinear two-port given in (6.13). Here the index, i, goes from 1 to 2, labeling the port currents and port voltages. Equation (6.13) allows gate leakage terms in parallel to the junction capacitances at the gate, terms that can become important as the controlling voltage values approach breakdown and forward conduction during the RF time-dependent large-signal excitation.⁶

Iit=IivccsV1tV2t+ddtQiV1tV2t(6.13)

We now assume the time-varying port voltages have a DC component, ViDC, and a small RF sinusoidal component, Virf, a complex phasor at the angular frequency ωω. We write the excitations as (6.14), substitute them into (6.13), compute the port current response to first order in the RF amplitudes, and finally evaluate the result in the frequency domain.

Vit=ViDC+Virfejωt+Virf∗e−jωt(6.14)

The result is (6.15), describing the port current complex phasors, I_iIi, associated with frequency ωω, as a linear combination of the port voltage phasors, Vjrf, at the same frequency. The small-signal model admittance matrix, Y_{i, j}Yi,j, is simply expressed in terms of the partial derivatives of the port current and charge constitutive relations from the linearization of (6.13), according to (6.16). Here we introduce the real conductance and capacitance matrices, GG and CC, respectively, into which the complex admittance naturally separates.

Ii=∑jYijV1DCV2DCωVjrf(6.15)

YijV1DCV2DCω=∂Ii∂VjV1DC,V2DC+jω∂Qi∂VjV1DC,V2DC≡Gij+jωCij(6.16)

If we identify ports 1 and 2 with the gate and drain (common source configuration with the specified ordering), then we can write the matrix (6.16) in terms of elements indexed with the letters corresponding to gate and drain, as in (6.17).

YijCSV1DCV2DCω=G11G12G21G22+jωC11C12C21C22=∂IGvccsVGSDCVDSDC∂VGS∂IGvccsVGSDCVDSDC∂VDS∂IDvccsVGSDCVDSDC∂VGS∂IDvccsVGSDCVDSDC∂VDS+jω∂QGVGSDCVDSDC∂VGS∂QGVGSDCVDSDC∂VDS∂QDVGSDCVDSDC∂VGS∂QDVGSDCVDSDC∂VDS(6.17)

From the analysis of Chapter 5, we had an independently derived expression for the measured intrinsic admittance matrix, expressed in terms of the ECPs based on the specific linear model topology considered. We reproduce equation 5.18, here for convenience as (6.18).

YijCSV1DCV2DCω=G11G12G21G22+jωC11C12C21C22=00GmGDS+jωCGS+CGD−CGDCm−CGDCDS+CGD(6.18)

Equating (6.17) and (6.18), matrix element by matrix element, we can identify simple linear combinations of the measured linear ECPs at any particular bias point with specific partial derivatives of the large-signal model constitutive relations evaluated at the corresponding bias. Even more generally, without any reference to the linear model equivalent circuit topology, the correspondence of (6.17) and (6.18), predicts that the intrinsic admittance matrix element values, obtained from the measured S-parameter data, should equal the partial derivatives of the large-signal model constitutive relations at the corresponding DC bias condition. This conclusion follows quite generally from the quasi-static large-signal modeling assumption that the intrinsic device can be modeled as a parallel combination of lumped nonlinear current sources and nonlinear charge sources. This assumption will be tested and analyzed extensively throughout the rest of this section.

6.3.1 Introduction to Charge Modeling

Nonlinear charge modeling has been shown to be critical for accurate simulation of bias-dependent high-frequency S-parameters [44], intermodulation distortion and ACPR in FETs [45,46], and harmonic and intermodulation distortion in III-V HBTs [47–50]. FET models with identical equivalent circuit topologies and identical I–V constitutive relations, differing from one another only by the form of their Q–V constitutive relations, can result in differences of 5–10 dB or more in simulated IM3 and differences of more than 5 dB in simulations of ACPR [45]. Moreover, conventional nonlinear charge models, based on textbook junction formulae, tend to show significant discrepancy compared to actual measured device characteristics.

The charge modeling problem can be simply stated as the specification of the nonlinear constitutive relations defining the independent charges at the (intrinsic) terminals of the circuit model, as functions of the relevant independent controlling variables, usually voltages. The charge-based contribution to the current at the i^th terminal is then the total time derivative of the charge function, Q_i. This is expressed in (6.19). If we choose one of the three terminals as a reference to define a particular 2-port description, we can interpret Q_i as the charge function at the ith independent port.

Iit=dQiV1tV2tdt(6.19)

Here V₁V1 and V₂V2 are the two independent intrinsic port voltages, which, for a FET, can be taken to be V_GSVGS and V_DSVDS. For an HBT, V₁V1 and V₂V2 can be taken to be V_BEVBE and V_CEVCE. I₁I1 is the gate (base) current and I₂I2 is the drain (collector) current for the FET (HBT) cases, respectively. Charge constitutive relations contribute to the current model through the time derivative operator in (6.19). This makes it apparent that charge plays an increasingly important role as the stimulus frequency increases.

The Curtice charge model with branch elements given by (6.7) and (6.9) can be cast in terms of terminal charges at the gate and drain, respectively, according to (6.20). Included in (6.20) is an additional nonlinear charge-based capacitor to model the drain-source capacitive coupling [11]. We see that the total terminal, or port charge function, is just the sum of the charges associated with all branch charges attached to the terminal with an appropriate reference direction. The arguments of (6.20) are time-varying intrinsic voltage differences.

QGVGSVDS=QGSVGS+QGDVGDQDVGSVDS=−QGDVGD+QDSVDS(6.20)

For a three-terminal intrinsic model with gate and drain charges, Kirchhoff’s Current Law (KCL), constrains the model to also have a charge function associated with the source node equal and opposite of the sum of the drain and gate charges. That is, we must have (6.21). Strictly speaking, the right-hand-side of (6.21) can be any constant, but we can set the constant to zero since the charges only enter the circuit equations in terms of a total time derivative. In the port description, we only need to define two independent port charge functions, Q₁ and Q₂.

∑i=G,D,SQi=0(6.21)

We note that (6.21) is a circuit-level expression of physical charge conservation, a consequence of KCL and is to be carefully distinguished from the independent modeling concept that we will refer to by terminal charge conservation, to be introduced in Section 6.3.4.

The Shockley charge model (6.4) and the Curtice charge models (6.7) and (6.8), evidently fit into the form (6.20). In both the Curtice and Shockley models, the gate charge expressions separate into the sum of two functions, each defined on one variable only. For the Curtice model, this is also the case for the drain charge as evident from (6.20). It is a consequence of the idealized simplicity of these models that there are only two one-dimensional functions, Q_GS(V)QGSV and Q_GD(V)QGDV, defining the entire two-port Shockley charge model, and three one-dimensional functions, Q_GS(V)QGSV, Q_GD(V)QGDV, and Q_DS(V)QDSV, defining the entire two-port Curtice charge model. The more general equation (6.19) is defined in terms of two arbitrary functions of two variables, Q_G(V₁,  V₂)QGV1V2 and Q_D(V₁,  V₂)QDV1V2, neither of which needs to be separable in terms of sums of univariate functions.

6.3.2 Measurement-Based Approach to Charge Modeling

Unlike DC I–V curves, charge constitutive relations cannot be measured directly. To specify the charge model, it is most convenient to establish a relationship between the model nonlinear charge constitutive relations and simpler quantities that can be directly obtained from bias-dependent S-parameter data. In fact, these relationships have been established by equating the imaginary parts of (6.17) and (6.18).

Assuming a particular two-port configuration, we obtain the following matrix equation, which relates the four nonlinear functions of bias constituting the imaginary parts of the admittance matrix, to the partial derivatives of the two model nonlinear charge functions, Q_iQi, evaluated at the operating point. Here indices i and j range from 1 to 2, the number of ports. For devices like MOSFETs, with additional terminals (e.g. bulk), i and j range from 1 to 3. The right-hand equality defines a capacitance matrix, C_ijCij, used for notational simplicity.

ImYijV1V2ωω=∂QiV1V2∂Vj≡CijV1V2(6.22)

The assumption that the middle and right-hand side of (6.22) are independent of frequency is necessary for consistency with (6.19). In Chapter 5 we showed examples of measured device data consistent with the left-hand side of (6.22) being independent of frequency. In practice, with a good intrinsic and extrinsic equivalent circuit topology, and good parasitic extraction, (6.22) is approximately true for frequencies approaching the cutoff frequency of the device. For higher frequencies, the intrinsic model topology needs to be augmented, such as adding additional elements in series with the nonlinear capacitors, to deal with “non quasi-static effects.” We described such R_GS and R_GD elements, for example, in Chapter 5, Section 5.6.5.2 for the small-signal model. For the large-signal model, however, this is a more difficult problem, and we won’t deal with it further here.

The measured admittance parameters can be obtained by simple linear transformations of the (properly de-embedded) S-parameters according to (6.23), which is equivalent to 3.36 (see also [51]). The measured common source capacitance matrix then follows by taking the imaginary part of (6.23) and dividing by angular frequency.

Yij=1Z0I−SI+S−1ij(6.23)

At this point, modeled and measured intrinsic imaginary admittance functions, or equivalently, the capacitance matrix elements, C_ijCij, can be directly compared.

However, it is more customary to compare modeled and measured small-signal responses in terms of linear equivalent circuit elements. There are many different intrinsic linear equivalent circuit representations that lead to the same intrinsic capacitance matrix. An alternative will be presented in Section 6.3.3. Defining equivalent circuit elements therefore requires a specific choice of equivalent circuit topology.

The linear equivalent circuit shown in Figure 5.27 maps, via the imaginary part of 5.18, the elements into the four independently measured capacitance matrix elements defined by 5.18. We solve these equations for the ECP values in (6.24). When applied to the transformed S-parameter measurements using (6.23), equations (6.24) define “measured” equivalent circuit elements. When applied to the linearized model admittances, (6.24) results in “modeled” equivalent circuit elements.

CGS=C11+C12CGD=−C12CDS=C22+C12Cm=C21−C12(6.24)

We note that there are four capacitance-equivalent circuit elements defined by Eq. (6.24). This is not surprising given that there are four functions, C_ijCij, corresponding to the four imaginary parts of the two-port admittance matrix. However, there are only three nodes in the equivalent circuit diagram of Figure 6.13. Historically, it was customary to place one capacitance between each pair of nodes. This procedure neglected, entirely, the fourth element, C_mCm, called the transcapacitance. As shown in Chapter 5, its contribution to the small-signal data is very significant.

Figure 6.13 Linear equivalent circuit model of capacitance part of intrinsic FET model. See also Figure 5.27.

A three-terminal (two-port) intrinsic equivalent circuit with two independent terminal charges generally leads to (at least) one transcapacitance. The equivalent circuit of Figure 6.13 or equivalently (6.24), places the transcapacitance in the device channel branch connecting drain and source, (parallel to the transconductance as is shown in Figure 5.27). It is important to note that the relationship of terminal charge partial derivatives to admittance matrices given in (6.22) is unique and is more fundamental than the set of transformations that define linear equivalent circuit elements in Eq. (6.24).

6.3.3 Nonuniqueness of Equivalent Circuits

Here we demonstrate another way of looking at transcapacitances, starting from an equivalent circuit that looks a little different from that of Figure 6.13.

We consider the topology given by Figure 6.14. Another possibility is given in [52].

Figure 6.14 Alternative linear equivalent circuit for capacitance model of a FET.

The elements in this circuit are defined by how they produce port currents, which is given by (6.25). Note that there are two capacitances and two transcapacitances in this description corresponding to the equivalent circuit of Figure 6.14, whereas the equivalent circuit of Figure 6.13 had three capacitances and one transcapacitance.⁷

IDG=CDGdVDGdt+XDGdVSGdtISG=CSGdVSGdt+XSGdVDGdt(6.25)

The common gate capacitance matrix corresponding to (6.25) is given in (6.26).

ImYijCGω=CijCG=CDGXDGXSGCSG(6.26)

The forms of (6.25) and (6.26) are very simple and symmetric, and lend themselves to easier expressions for drain-source exchange symmetry. There is also a unique identification of the ECPs of Figure 6.14 and the common-gate imaginary part of the admittance parameters. This is even simpler than the linear combinations of common source Y-parameters needed to identify the ECPs of Figure 6.13 using (6.24).

Again we note that at a general bias condition, X_SG≠X_DGXSG≠XDG and so (6.26) is still not reciprocal, just like (6.24). If the device model has a nonreciprocal capacitance matrix in any port configuration, the conclusion is that the two-port model admits a transcapacitance.

From the point of view of the terminal charges, the results work out exactly the same, independent of where the transcapactiance element is placed in the equivalent circuit. The gate, drain, and source charges will have precisely the same functional forms on a fixed set of port voltages whether recovered from common gate Y-parameters using the topology of Figure 6.14 or common source Y-parameters using the topology of Figure 6.13.

Using (6.24), the measured capacitances can be compared to theory. An example of measured capacitances, as functions of the bias conditions, is given by Figure 6.15 for a GaAs pHEMT. Several facts are immediately apparent from the figures. The value of the transcapacitance, C_mCm, is generally zero or negative. This follows from the discussion in Chapter 5 related to equation (5.21). C_GSCGS depends not just on V_GSVGS, the voltage across the element, but also on the other independent voltage, V_DSVDS. This is qualitatively different from the Shockley and Curtice models, where the model C_GSCGS is completely independent of V_DSVDS. This also means that C_GSCGS cannot be modeled by a standard two-terminal nonlinear capacitor,⁸ for any functional dependence on the (single) voltage, V_GSVGS, across the element. The feedback capacitance, C_GDCGD, depends on both V_GSVGS and V_DSVDS, in a more complicated way than V_GD = V_GS − V_DSVGD=VGS−VDS. That is, C_GDCGD also cannot be modeled by a standard two-terminal nonlinear capacitor, despite the familiar looking symbol in the linear equivalent circuit. Moreover, the V_GSVGS dependence of C_GDCGD, for large V_DSVDS, when the device is in the saturation region of operation, is exactly the opposite of the Shockley model’s prediction. That is, the feedback capacitance is actually much larger when the device is pinched off (V_GS =  − 2VVGS=−2V) than when the channel is open (V_GS =  − 1.2VVGS=−1.2V) and conducting current. More elaborate physical theories, which yield results closer to measured characteristics of modern FETs, lead to equations sufficiently complicated that they can be expressed, usually, only in approximate form [53]. A more recent approach, based on a decomposition of the charge model into simple one-dimensional depletion charges and a two-dimensional drift charge defined in terms of voltage and current, has been proposed in [54] and is presented in Section 6.3.9.

Figure 6.15 Bias-dependence of GaAs pHEMT capacitance matrix elements. V_GS ranges from −2 V to −1.2 V in 0.2 V steps. The arrows point in the direction of increasing V_GS.

6.3.4 Terminal Charge Conservation

The above development, beginning with (6.19), started from large-signal model equations and then computed the small-signal responses that can be compared easily to measured S-parameter (Y-parameter) data. In what follows, we present a treatment based on trying to reverse the above flow. That is, we seek to solve the inverse problem to determine the functional form of the large-signal model constitutive relations, Q_iQi, that enter (6.19), directly from the measured bias-dependence of the small-signal characteristics. Unfortunately, but not surprisingly, this inverse process is generally ill-posed. However, under certain specific and verifiable conditions, a practical inverse modeling process can be constructed for transistors manufactured in a variety of material systems. This enables the generation of accurate nonlinear circuit simulation models for devices of great practical utility, from simple DC and linear (S-parameter) measurements.

Equation (6.22), with the left-hand side referring to measured data, can be interpreted as the mathematical statement of the inverse problem. That is, the measured bias-dependences of the intrinsic admittance elements are equated to the partial derivatives of the respective model terminal charges. The mathematical problem then becomes determining the conditions under which (6.22) can be solved for the model terminal charge functions, Q_iQi.

The necessary and sufficient conditions for the terminal charges to be recovered from bias-dependent capacitance matrix elements defined from measured data using Equations (6.22), are succinctly expressed by (6.27) [26,31,55].

∂CijV1V2∂Vk=∂CikV1V2∂Vj(6.27)

Through definitions (6.22), equations (6.27) are constraints on pairs of bias-dependent measured admittances, one pair per row of the admittance matrix labeled by the index i. If (6.27) is satisfied, then the terminal charges can be constructed directly from the measured capacitance matrix elements by a path-independent contour (line) integration expressed by (6.28).

This result is unique up to an arbitrary constant that has no observable consequences and so the constant can be set equal to zero. Moreover, the partial derivatives of this charge reduce exactly to the measured bias-dependent capacitance measurements. That is,

∂Qimodel∂Vj=Cijmeas(6.29)

If (6.27) is not exactly satisfied for fixed index, i, then, strictly speaking, there is no function, Qimodel, that is consistent with (6.29). In this case, the line integral using measured capacitance functions in Eq. (6.28) produces charge functions that depend on the path chosen for the contour. Different contours produce models that fit some capacitances versus bias better than others, with no perfect fits of all capacitances possible.

Equations (6.27), expressing the equality of mixed partial derivatives with respect to voltages of different capacitance functions with the same first index, can be interpreted as meaning that pairs of capacitance functions attached to the i^th node form a conservative vector field in voltage space [31]. An alternate but mathematically equivalent representation of this concept is presented in [56]. Capacitance functions that obey (6.27) are said to obey “terminal charge conservation at the i^th node.”⁹ We use the nomenclature terminal charge conservation to distinguish it from the fundamental physical law of charge conservation that is embodied in circuit theory by Kirchoff’s Current Law (KCL) [compare (6.21) that embodies KCL]. In contrast, terminal charge conservation is a constraint that can, but needs not, be imposed by the modeler to approximate the behavior of a device. Physical charge conservation is a fundamental physical law and a requirement of any circuit model that is consistent with KCL. An example of a nonlinear model not consistent with terminal charge conservation, and its consequences, is presented in Section 6.3.7.

Any model starting from Eq. (6.19) has model capacitance functions that conserve terminal charge at each node. This is true because the model capacitances are derived in (6.22) starting from model charges and then (6.27) follows from the derivative properties of smooth functions. However, starting from independent measurements and trying to go back to model charges via (6.29) requires the constraints (6.27) to be satisfied by the measured Cijmeasdata.

The degree to which actual bias-dependent admittance data is consistent with the modeling principle of terminal charge conservation was investigated in [44] and [57]. For small GaAs FETs, it was found to hold extremely well at the gate, and slightly less well at the drain. For larger, high-power GaN HEMTs, these relationships don’t seem to hold quite as well, and more elaborate models are required (see Section 6.6). The applicability of terminal charge conservation to III-V HBTs was discussed in [58] and [60] and found to be extremely useful in accurate modeling of delays and “capacitance cancellation” effects in such transistors.

6.3.5 Practical Considerations for Nonlinear Charge Modeling

The parameterization of line-integral (6.28) for two distinct paths, shown in Figure 6.16, are written explicitly in (6.30) and (6.31), respectively. Path independence means the same charge function can be computed from completely independent sets of bias-dependent data along the two paths of Figure 6.16.

QGVgVd=∫Vg0Vg(C11V¯gVd0dV¯g+∫Vd0Vd(C12VgV¯ddV¯d(6.30)

QGVgVd=∫Vg0Vg(C11V¯gVddV¯g+∫Vd0Vd(C12Vg0V¯ddV¯d(6.31)

Figure 6.16 Two different paths in voltage space for line-integral calculation of terminal charges.

There are several issues with respect to implementing (6.30) and (6.31) directly on measured data. The measured capacitance data is defined only at the discrete voltages (points of in Figure 6.16) so the integrals have to be done numerically. If the data is not on a rectangular grid, interpolation along some of the paths may be required (as in in Figure 6.16 along the V_GSVGS direction). Fundamentally, if (6.27) is not exactly satisfied due to measurement errors or the neglect of effects like temperature and traps (to be considered later), the different paths effectively trade-off model fidelity of ImY₁₂ImY12 and ImY₁₁ImY11 versus bias, respectively. Integration error accumulates for large paths so the charge value at points far away from the starting point of integration (V_G0VG0 and V_D0VD0) will be less accurate. The integration is also difficult to perform along paths to or near the ragged boundary of the data domain (see Figure 6.16).

Despite these practical difficulties, table-based models using full two-dimensional nonlinear gate charge functions constructed directly from small-signal device data have found their way into practical commercial tools [59]. Table-based charge models can be much more accurate than closed form empirical models, where the complex two-dimensional nature of the Q-V constitutive relations have not received as much attention as have I–V relations which are directly measureable.

6.3.6 Charge Functions from Adjoint ANN Training.

There are now robust methodologies to train ANNs to construct Q-V constitutive relations directly from knowledge of the desired function’s partial derivatives as represented by measured capacitances [33,40]. This has been a major breakthrough for practical measurement-based charge modeling of transistors.

All the practical problems described above of computing multidimensional charge functions by line integration of suitably decomposed small-signal data are ameliorated by using the adjoint ANN training approach [40]. This method directly results in a neural network that represents the Q_i(V_GS,  V_DS)QiVGSVDSfunctions from information only about their partial derivatives, as represented by the bias-dependent measured capacitances defined by (6.29). A diagram of the training method is given in Figure 6.17. If (6.27) is not exactly satisfied by the device data, the adjoint method still returns a charge function that generally gives a much better global compromise between the capacitances than the typical line-integral methods. The training can take place directly on the intrinsic nongridded intrinsic bias data, and the ragged boundary presents no difficulty. Validation of the adjoint ANN approach to simultaneously fit the detailed two-dimensional FET input capacitance behavior with bias is shown in Figure 6.18. The validation for the independent fit for the drain capacitances is shown in Figure 6.19.

Figure 6.17 Adjoint ANN training of model gate terminal charge function from C₁₁ and C₁₂ data.

Figure 6.18 Validation of ANN-based gate charge model fitting, (a) C₁₁, (b) C₁₂. Data (symbols), model (lines); (c) gate charge function, Q_G.

Figure 6.19 Validation of ANN-based drain charge model fitting, (a) C₂₁, (b) C₂₂. Data (symbols), model (lines); (c) gate charge function, Q_D.

With current-voltage and charge-voltage nonlinear constitutive relations modeled by ANNs, the improvement in simulation accuracy over spline-based table models can be demonstrated. A comparison is shown for the case of a GaAs pHEMT device in Figure 6.20. At moderate to high power levels, where the voltage swings are comparable or greater than the distance between discrete data points, the ANN and table-based models are nearly identical and compare well with measurements. At low power levels, the distortion simulation of the table-based model is determined by the numerical properties of the interpolating functions. Piecewise-cubic splines, used in this case, don’t do a good job for high-order distortion at low signal levels, hence the ragged variation of distortion with power. However, the ANN model is very well-behaved at all power levels, and has the correct asymptotic dependence as the power decreases.

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