# Using Signal Envelope Detection for Online and  Offline RF MEMS Switch Testing

Authors: E. Simeu; H. N. Nguyen; P. Cauvet; S. Mir; L. Rufer; R. Khereddine
Journal: VLSI Design
 (2008)

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## Abstract

The test of radiofrequency (RF) integrated circuits at their ever-increasing operating frequency range requires sophisticated test equipment and is time-consuming and, therefore, very expensive. This paper introduces a new method combining low-frequency actuation signal as test stimuli and signal envelope detection applied on the RF output signal in order to provide a low-cost mean for production testing of RF MEMS switches embedded in system-in-package (SiP) devices. The proposed approach uses the principle of alternate test that replaces conventional specification-based testing procedures. The basic idea is to extract the high-frequency characteristics of the switch from the signal envelope of the response. Output parameters like “on” and “off” transition time are extracted at low frequency and used in a regression process to predict RF conventional specifications like S-parameters. The paper also provides a set of recursive estimation algorithms suitable for online testing. In this context, “on” and “off” transition time estimated from the output low-frequency envelope is used as test metrics and is concurrently updated using recursive algorithms. Validation results obtained on a capacitive RF switch model are presented.


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## Body

## 1. Introduction

The trend toward more portable  solutions, with more embedded functions,
drives the demand for the system-in-package (SiP) technology. Cellular handset
designs represent a good example of this global trend. Today, a cellular mobile
must offer multiband and multimode in addition to Bluetooth networking, global
positioning system (GPS), and wireless local area network (WLAN), not to mention the
user applications such as games, audio, and video (TV on Mobile). The International
Technology Roadmap for Semiconductors defines a system-in-package as any combination
of semiconductors, passives, and interconnects integrated into a single package
[1]. This definition clearly
indicates that an SiP can combine different die technologies and applications
with active and passive components to form a complete system or subsystem,
whereas a system-on-chip (SoC) is created from one single die. Consequently, an SiP
usually includes logic and memory components, but it also increasingly
includes analog, mixed-signal and RF components, and micro-electromechanical
systems (MEMS). These various components are interconnected by
wire-bond, flip-chip, stacked-die technology, or any combination of above. The
final packaged SiP is looking like any conventional SoC package. Figure
1 shows an example of interconnection of multiple
die components in an SiP for Global system for mobile communication (GSM) application.
As shown in Figure 2, different type of carriers can be
used to incorporate the whole package made of the dies and their interconnections.Figure 1
Multiple die components and interconnections on an SiP for GSM application.Examples of carrier style of an SiP package: (a) Leadframe, (b) Laminate, (c) Silicon-Based.
(a)(b)(c)SiP offers a unique advantage over SoC in its ability of
integrating MEMS with circuitry to provide a fully functional system, not just
acting as a simple sensor or actuator. The need for various RF bands in mobile
phones is a key driver for the introduction of components that can switch the
received or transmitted RF signal to the appropriate blocks.These new heterogeneous integrated systems pose many challenges from a test perspective.
Generally, because of factors such as cost, quality, and reliability, an SiP must be
created from bare dies with a high-quality level, in other terms, from
known-good-dies (KGD). The manufacturing process, and particularly the packaging step,
is more complex, potentially increasing the likelihood of failing devices. Indeed,
MEMS packaging process is a critical operation on its own because of the requirements
for the cavity sealing. Therefore, assembling MEMS into an electronic system puts
more requirements on mass production. Last but not least, the test strategy and its
consequences on methods and design-for-test (DfT) features must be carefully
considered.In this paper, we aim at developing a low-cost production test approach for RF
MEMS switches embedded in SiP devices. Since the RF MEMS switch is embedded together
with other devices, the most important difficulty is to obtain measures at the output of
the switch in order to detect and diagnose potential faults. In the approach we
present, the output of the switch is monitored by means of an envelope detector. The
low-frequency output of this detector is then used to predict the actual
high-frequency performances of the switch. This approach relaxes the constraints on
sampling speed and timing accuracy when applied to very high-speed RF components.The rest of the paper is structured as follows. First, Section2 briefly reviews some previous works on
RF MEMS testing. Behavioral modeling of capacitive switches is discussed in
Section 3. Emphasis is given to dynamic modeling of
a series capacitive switch. Section 4 deals with the
proposed testing scheme. Different switching parameters are presented and the way of
extracting them from the output envelope for offline and online testing purposes is
discussed. Monte Carlo simulation and nonlinear regression are discussed in
Section 5. The simulation results show accurate
prediction of S-parameters using the estimated transient time.
Section 6 suggests a set of recursive algorithms for
online monitoring of transition times, allowing online testing schemes for RF
MEMS switches. Conclusions are given in Section 7.


## 2. Previous Works

Work on RF integrated circuit testing has been largely triggered by the need of testing RF
transceivers. This problem can be illustrated by an example strategy of a
path-based testing approach, where a receiver or transmitter channel is tested
from antenna to digital baseband as a whole. At a first glance, we might expect
that testing the system parameters, such as bit error ratio (BER) for the
receiver path or error vector magnitude (EVM) for the transmitter, would ensure
a sufficient coverage at a reasonable cost. This is not the case because such a
test requires expensive RF instruments, and the test of the RF switch cannot
guarantee all the specifications and, more importantly, the quality and the
reliability of the component, thus of the whole system.Because of the long evaluation time needed and expensive RF ATE (automated test
equipment) required by elaborate performance tests, the production testing of high
performance SiP is a major component of the total manufacturing cost. Indeed, ICs
production testers with RF capabilities supporting frequencies of GHz range
are prohibitively expensive. Further increase of the costs is due to the key
problem with testing RF dies coming from the fact that many complex RF specification
values need to be determined during production test at high operating
frequencies [2, 3].
Testing a significant number of devices in this way is therefore prohibitive
and impractical. RF MEMS switches are among the most test demanding RF devices.In recent years, alternate tests have been considered as a promising solution for
low-cost test of RF components [4–6]. In
contrast to the previous work using classical functional testing methods, the
test specifications of the device-under-test (DUT) are not measured directly
using conventional methods. Instead, in alternate testing approaches, the test
specification values of the DUT are computed directly from the observed test
response when a specially crafted stimulus is applied.Previous works on RF MEMS switches [7, 8] have exposed the advantages of testing them at
lower test frequency instead of performing the measurements by investigating them at
the intended signal frequency. In [7], the reliability
assessment of a capacitive shunt switch is considered. It is shown that a low
frequency of 10.7 MHz is already sufficient for detecting the main features
of the switch cycle such as pull-in voltage, rise- and fall-times of the switching
action and the difference between the on-capacitance and the off-capacitance
(ΔC).
A square wave of 10 kHz is used as actuation signal. The output signal is
bandpass filtered around the switched voltage frequency. A diode detector is used
for demodulation followed by an amplifier. Although some failure mechanisms may be
related to GHz frequencies, it is shown in [7]
that many failure modes can be investigated at this lower test frequency. Therefore,
the test procedure requires only standard components that are less expensive and
easier to use in order to build a measurement sufficiently rich to investigate
important properties of the switching behavior, such as rise- and fall-times and
stability. Taking the test of an RF MEMS switch as illustration example, in
[8] the authors study the monitoring of an SiP
integrating MEMS devices. Similarly as in [7], a
capacitive shunt switch is used here to illustrate the proposed scheme.


## 3. Behavioral Model of Capacitive Switches

A number of different criteria can be used to classify MEMS switches. Taking into
account the actuation mechanism, the electrostatic, electromagnetic, or
electrothermal principle can be considered. The electrostatic principle is the most
convenient for the fabrication that can be compatible with typical RF technologies.
Switches based on the electrostatic principle are also smaller and faster.Depending on the circuit configuration, switches can be of series or parallel
(shunt) types. These two configurations are  illustrated in Figure3. The switching element can be designed as a beam
fixed on both ends, as a cantilever or as a membrane. The choice of the form of the
element is driven by the demand on the switch stiffness that defines the necessary
switching voltage or by the facility to be used in certain switch configurations.Figure 3
Parallel and series configurations of RF MEMS capacitive switches.The type of switching contacts distinguishes two different approaches
to the switch application. There are switches creating a resistive (metal to metal) contact or a capacitive (contact-less) one. The first group allows DC applications
and can be used up to 60 GHz, whereas the second group covers the frequency
range from 6 to 120 GHz. An important parameter describing the quality of
capacitive switches is the capacity ratio in the “on” and “off” states.
Typical key specifications of today switches include(i)
CON/COFF>100,(ii)
actuation voltage <20 V,(iii)
number of switching cycles to failure >109,(iv)
tdown~5 microsecond,(v)
tup~3 microsecond.
To allow investigation of phenomena such as pull-down, release, power handling,
and source noise effects, a behavioral model of the switch is needed with the
control voltage   Vc that can be an arbitrary continuous function of time.


### 3.1. Low-Frequency Dynamic Analysis

There are a number of physical mechanisms available for switch actuation.
Among these, the electrostatic (or capacitive) conversion is one of the most mature.
This is probably because actuators using this conversion are commonly produced with
surface micromachining technology that is compatible with integrated circuit
fabrication process.The electrostatic mechanism is based on the interaction between the mechanical and
electrical quantities in a condenser having one electrode fixed and the other
electrode movable. A voltage applied between both electrodes causes the
displacement of the movable electrode. In a transversal capacitive transducer,
the movable electrode is displaced off-plane, perpendicularly to the electrode
surface. In order to avoid the electrical contact between actuating electrodes,
the dielectric layer between these electrodes must be introduced.The original distance between the electrodesH defined in Figure
4 varies by the displacement
x when the transducer is in function. The instantaneous
transducer capacity is (1)C=εAH−x+(td/εd), where A = Ww is the area of
electrodes, ε the
permittivity of air, td and εd are the thickness and the relative dielectric constant of the dielectric
layer, respectively. Supposing that the voltage does not change during the
movement of the movable electrode, the electrostatic force is given by
(2)Fe=εAU22(H−x+(td/εd))2. The dynamic
model of the cantilever beam is useful for determining the switching time of the switch, the frequency
bandwidth of the mechanical part, and the effect of damping. In case of small
displacements, the dynamic response of the MEMS beam is given by (3)md2xdt2+bdxdt+kx=εAU22(H−x+(td/εd))2, where x is the beam displacement,
m is the beam mass, the spring
constant k due to a
uniform force applied over the entire beam is given by
(4)k=2Ew3(Ta)3. An approximate formula for the damping coefficient for
a cantilever beam is given in [9] by
(5)b=3μS22πH3, where S is the area of the beam, a, T, H, and w are
the design parameters defined in Figure 4,
and E and μ
are Young's modulus and the coefficient of viscosity, respectively.Design parameter of a cantilever beam switch: (a) side view and (b) perspective view.
(a)(b)(c)


### 3.2. High-Frequency Equivalent Circuit

A capacitive series switch with resistive (metal-to-metal) contact is shown in
Figure5(a). The corresponding circuit model of the
RF section is shown in Figure 5(b). For a cantilever
switch with two contact areas, the capacitance is composed of a series capacitance
(Cs) between the transmission lines and the switch metal and a
parasitic capacitance (Cp) between the open ends of the
transmission lines. The total series capacitance is Cs/2 with
(6)Cs=εAsH−x+(td/εd), where As is the contact area in both
sides. It is seen that the capacitance Cs has a strong dependence on the displacement x.
The MEMS series switch resistance between the contact areas is dependent on its
length and width. The transmission line loss must be added to obtain the total switch
resistance which is assumed constant. In the down-state position, it is possible
to model the transmission lines by a series inductance. For simplicity,
the inductance of the switch is supposed negligible in the rest of the paper and the
impedances Z01 and Z02  represent incoming and outgoing
transmission line sections.(a) RF capacitive switch showing the input and output RF transmission line and the switching contact placed on the free end of the cantilever, and (b) circuit model corresponding to the RF behavior.
(a)(b)


### 3.3. Complete Model of the Switch

The equations governing the complete behavior of the
switch can be readily solved with a nonlinear simultaneous differential
equation solver such as Matlab/Simulink. The complete model of the series
switch is given in Figure6. For the simulation of this model, the physical
parameter values given in Figure 4 have been
considered.Figure 6
Matlab/Simulink dynamic model of the RF switch.


## 3.1. Low-Frequency Dynamic Analysis

There are a number of physical mechanisms available for switch actuation.
Among these, the electrostatic (or capacitive) conversion is one of the most mature.
This is probably because actuators using this conversion are commonly produced with
surface micromachining technology that is compatible with integrated circuit
fabrication process.The electrostatic mechanism is based on the interaction between the mechanical and
electrical quantities in a condenser having one electrode fixed and the other
electrode movable. A voltage applied between both electrodes causes the
displacement of the movable electrode. In a transversal capacitive transducer,
the movable electrode is displaced off-plane, perpendicularly to the electrode
surface. In order to avoid the electrical contact between actuating electrodes,
the dielectric layer between these electrodes must be introduced.The original distance between the electrodesH defined in Figure
4 varies by the displacement
x when the transducer is in function. The instantaneous
transducer capacity is (1)C=εAH−x+(td/εd), where A = Ww is the area of
electrodes, ε the
permittivity of air, td and εd are the thickness and the relative dielectric constant of the dielectric
layer, respectively. Supposing that the voltage does not change during the
movement of the movable electrode, the electrostatic force is given by
(2)Fe=εAU22(H−x+(td/εd))2. The dynamic
model of the cantilever beam is useful for determining the switching time of the switch, the frequency
bandwidth of the mechanical part, and the effect of damping. In case of small
displacements, the dynamic response of the MEMS beam is given by (3)md2xdt2+bdxdt+kx=εAU22(H−x+(td/εd))2, where x is the beam displacement,
m is the beam mass, the spring
constant k due to a
uniform force applied over the entire beam is given by
(4)k=2Ew3(Ta)3. An approximate formula for the damping coefficient for
a cantilever beam is given in [9] by
(5)b=3μS22πH3, where S is the area of the beam, a, T, H, and w are
the design parameters defined in Figure 4,
and E and μ
are Young's modulus and the coefficient of viscosity, respectively.Design parameter of a cantilever beam switch: (a) side view and (b) perspective view.
(a)(b)(c)


## 3.2. High-Frequency Equivalent Circuit

A capacitive series switch with resistive (metal-to-metal) contact is shown in
Figure5(a). The corresponding circuit model of the
RF section is shown in Figure 5(b). For a cantilever
switch with two contact areas, the capacitance is composed of a series capacitance
(Cs) between the transmission lines and the switch metal and a
parasitic capacitance (Cp) between the open ends of the
transmission lines. The total series capacitance is Cs/2 with
(6)Cs=εAsH−x+(td/εd), where As is the contact area in both
sides. It is seen that the capacitance Cs has a strong dependence on the displacement x.
The MEMS series switch resistance between the contact areas is dependent on its
length and width. The transmission line loss must be added to obtain the total switch
resistance which is assumed constant. In the down-state position, it is possible
to model the transmission lines by a series inductance. For simplicity,
the inductance of the switch is supposed negligible in the rest of the paper and the
impedances Z01 and Z02  represent incoming and outgoing
transmission line sections.(a) RF capacitive switch showing the input and output RF transmission line and the switching contact placed on the free end of the cantilever, and (b) circuit model corresponding to the RF behavior.
(a)(b)


## 3.3. Complete Model of the Switch

The equations governing the complete behavior of the
switch can be readily solved with a nonlinear simultaneous differential
equation solver such as Matlab/Simulink. The complete model of the series
switch is given in Figure6. For the simulation of this model, the physical
parameter values given in Figure 4 have been
considered.Figure 6
Matlab/Simulink dynamic model of the RF switch.


## 4. Using Envelope Detection to Extract Low-Frequency Output Parameters

In order to facilitate the embedded test of an RF switch, the evaluation of the
test response needs to be handled by an on-chip analog detector with a low-frequency
output. In this work, we use an embedded envelope detector to capture a low-frequency
signal describing the switching behavior of the device. Figure7 shows an envelope detector embedded in an RF MEMS
serial switch. It consists simply of a diode and resistor-capacitor (RC) filter.Figure 7
Envelope detector embedded in an RF MEMS serial switch scheme.In a positive half cycle of the input signal, the diode is forward biased and the
capacitorCt charges up rapidly to a
peak value of the input signal. When the input signal falls below this value,
the diode becomes reverse biased and the capacitor discharges slowly through
the load resistor Rt. The discharging process continues
until the next positive half cycle. Thereafter the charging-discharging cycle is
continued. The values of Rt and Ct
are to be chosen appropriately to perform the required envelope detection.
The envelope detector output signal is shown in Figure
8.Figure 8
Test signal simulation: (a) switch actuation control signal, (b) RF output signal,
(c) detected envelope of the output signal.An RF input signal of 1 GHz is applied to the incoming transmission line of the
switch and a simple pulse signal (Figure8(a)) is applied to its electrodes as actuation input.
The RF output signal obtained on the outgoing
transmission line is plotted in Figure 8(b). The corresponding envelope detector
output is represented in Figure 8(c).For an actuation signal applied to the switch (functional, or a special test
actuation), easily measurable output parameters must be extracted from the observed
low-frequency test response. Conventional performances (such asS-parameters) can be
predicted from this estimation using a nonlinear regression equation. The following
output parameters can be extracted from the low-frequency test response.(i)
Transition time. This is given by the time to switch from the
“off” to the “on” state and vice versa.
The conventional definition of the transition times is the time required for the
output RF signal to rise from 10% to 90% of its value for
off-to-on transition and 90% to 10% for on-to-off transition.
However, since the corresponding values will be difficult to evaluate, we will use
the definition ofton and toff given in Figure
9. These values are easily obtained from the output envelope as shown in Figure 9.(ii)
Off/on voltage ratio. This parameter is given by the ratio of the test signal amplitudes in
the “off” and “on” states. A good approximation
of this ratio is given byAoff/Aon, where
Aon and Aoff are, respectively, the switch on- and the switch off- envelope amplitudes defined in Figure 9.(iii)
Actuation voltages. The switch-on voltageVon is the minimal voltage required to switch
the device from the “off” to the “on” state. The switch-off voltage Voff has an analog definition.Figure 9
Low-frequency output parameters:ton is the
total delay time of a rising edge; toff
is the total delay time of a falling edge; Aon
is the envelope amplitude in the “on” state; and Aoff is
the envelope amplitude in the “off” state.


## 5. Computing S-Parameters from Low-Frequency Output Parameters

Monte Carlo simulation was combined with a nonlinear regression analysis to
generate mappings from the low-frequency output parameters to high-frequencyS-parameters. A Monte Carlo simulation is first done to generate a sample of 200
switches. A Gaussian distribution is applied for each physical parameter given in
Figure 6, with a variation coefficient of 0.4
(standard deviation = 0.4 × nominal value).For each of the 200 switches, theS-parameters are computed at an operating
frequency of 1 GHz. The alternate test stimulus is a simple low-frequency
pulse signal. The feature extraction algorithm uses the signal envelope to compute
an estimation of the transition time (Ton and
Toff). These samples are fed into a regression model
generated by 100 instances of the switch Matlab/Simulink model. The regression
procedure aims at finding the best fitting curve to the set of 100 points by
minimizing size of the offsets (the residuals) of the points from the curve.
The least square regression methods use the sum of the squares
of the offsets instead of the offset absolute values because this allows the
residuals to be treated as a continuous differentiable quantity. Our experimental
procedure aims at providing a fitting function for the independent variable
X which is a vector of  component Ton
and Toff that estimates high-frequency parameter
Sij for a given x = (ton, toff). The linear least squares
fitting technique is the simplest and the most commonly used form of regression
method that provides a much simpler analytic form for the fitting parameters than
other nonlinear regression fitting algorithm such as MARS
[10] which is used in
[4, 5, 11] for alternate
test. In addition, the fitting technique can be easily generalized from a linear
fitting to any nonlinear expression provided that the parameters to be estimated
appear linearly in the fitting function. In fact, the functional relationship
between the Sij and x = (ton,
toff) is assumed to be a combination of nonlinear expressions as
exponential (ex), power (xn), and logarithm (lnx), with
additive or multiplicative constants. The data are first transformed in such a way
that the resulting line is a straight line. For this reason, standard forms for
exponential, logarithmic, or power laws in the nonlinear fitting function expression
are first explicitly computed.The accuracy of the regression result is validated by a separate set of 100
instances that are generated independently from the training set.
Figure10 shows the predicted versus actual
specifications of 100 instances in this validation set for S12
and S12 specification parameters. As shown in Figure 10,
the specification S12 is predicted with a relatively good accuracy using
only the transition
times.PredictedS-parameter for RF MEMS Switch.
(a)(b)


## 6. Recursive Estimation-Based Online Monitoring of Transition Times

In this section, we are suggesting three recursive estimation algorithms
that can be used for online monitoring of low-frequency test metrics extracted
from the switching envelope signal. All the switching characteristics can be
used as test metrics in a procedure of low-frequency offline testing as well as
in online monitoring scheme. In the offline testing case, a special test stimulus
is applied on the actuation input of the switch. The test decision is taken by
comparing the switching characteristics estimated from the output envelope to
their nominal values. In the context of online testing scheme, the switching
characteristics are estimated concurrently to the normal operation of the
switch by means of a recursive identification algorithm. The value of each of
the test metrics is updated at each operation. For the switching cycleK, an
estimation
θ^(t) of the mean value of the switching test metric
θ(t) is given by (7)θ^(t)=1t∑i=1tφ(i), where θ^(t) is the mean value of the parameter for the t first switching cycles and φ(i) is the value
of the test metric measured for the switching cycle i. Since new data
appears recursively, we must avoid repeating all calculations at each stage.
This involves the use of a recursive formula that expresses θ^(t) in terms of θ^(t−1).Theorem 1.
A recursive form of (7) expressing θ^(t) in terms of θ^(t−1) is (see proof in the appendix) (8)θ^(t)=t−1tθ^(t−1)+ϕ(t)t.In the context of online testing, it is necessary to adapt the estimator quickly when appears
a change in the device. Weighting factors are introduced in the mean value
calculation to give more weight to the new data compared with the older. The
weighting or forgetting factors allow then to forget old information. The mean
value is then given by(9)θ^λ(t)=λtϕ(0)+λt−1ϕ(1)+⋯+λt−iϕ(i)+⋯+λϕ(t)+ϕ(t)λt+λt−1+⋯+λt−i+⋯+λ+1=∑i=0tλt−iϕ(i)∑i=0tλi,where the
forgetting factor λ verifies: 0<λ<1.Theorem 2.
When the time value t is great enough, a recursive
form of (9) expressing θ^λ(t) in terms of θ^λ(t−1) is given by (see proof in the appendix)
(10)θ^λ(t)=λθ^λ(t−1)+(1−λ)ϕ(t).In another recursive estimation algorithm, only the data included in a sliding  window are used in the estimation formula. All the past data that are out of this sliding window are
not taken in the updating of the test metric estimation value. The mean value of the
test metric is then estimated through a sliding window of widthK as shown in Figure
11 (for K=3).Figure 11
Sliding window recursive estimation.In this case, the mean value estimation is given by the following definition
equation:(11)θ^K(t)=1K∑i=0K−1ϕ(t−i), where K is the width of the estimation window.Theorem 3.
A recursive form of (11) expressing θ^K(t) in terms of θ^K(t−1) is given by (see proof in the appendix) (12)θ^K(t)=θ^K(t−1)+ϕ(t)−ϕ(t−K)K.To test the efficiency of the proposed online testing
scheme, many fault injections have been simulated on the RF MEMS switch Matlab/Simulink
model of Figure5. Among the recursive algorithms proposed above, the sliding
window method seems to be a good trade-off between the complexity of online
fault detection scheme and its efficiency in terms of fault latency and fault
coverage.Figure12 gives an example of parametric fault
detection using the estimation of off-to-on transition time. A parametric fault
corresponding to a 50% decrease of the electrode area (A = Ww) was injected in the behavioral model of the switch. It is evident
that this is a typical parametric manufacturing fault. However, this type of
fault may also occur in an operating application when the hermetic packaging
requirements of RF MEMS switch are no longer guaranteed. It is clear from this
figure that the injected fault is quickly detected since the Ton mean value estimated before the fault injection is soon significantly different
than the mean value obtained after the fault injection.Figure 12
Simulation of recursive estimation for online fault detection using the sliding window algorithm.


## 7. Conclusions

The main objective of this work is to find a way of testing RF MEMS switches that leads to a low-cost
implementation and that speeds up the whole process of conventional functional
testing of RF devices embedded in SiP packages. We have suggested a testing
scheme of capacitive RF MEMS switches based on an envelope detector that
provides a low-frequency output. Simulation results show that this signal
provides important properties of the switching behavior, such as transition
times, actuation voltage, and switching voltage ratio. From data provided by Monte Carlo simulation, we have implemented a
multivariate regression algorithm to build nonlinear mappings that link the low-frequency
test metrics to conventional specifications parameters of RF devices such asS-parameters that are available at RF operating frequency only. As a
consequence, the conventional high-frequency specifications of the switch are
predictable from the low-frequency signal envelope. The results obtained using
the proposed methodology show that the high-frequency specifications can be
predicted with good accuracy from the transition time measurement. In the
proposed methodology, the test is performed using a low-frequency envelope signal without requiring access to high-frequency information,
thus eliminating the need for expensive RF ATE. For online testing purposes, the
paper also provides a set of recursive algorithms aiming at low-cost online
monitoring of low-frequency test features extracted from the switching envelope
signal, instead of testing the RF device at its typical operating frequency.



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*Source: 294014-2008-06-04.xml*