# Built-in Test Enabled Diagnosis and Tuning of RF  Transmitter Systems

Authors: Vishwanath Natarajan; Rajarajan Senguttuvan; Shreyas Sen; Abhjit Chatterjee
Journal: VLSI Design
 (2008)

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## Abstract

Built-In RF test is a challenging problem due to the need to measure the values of complex test specifications on-chip with the precision of external RF test equipment. BIT techniques are necessary for guiding system adaptation during field operation. Prior research has demonstrated that embedded RF sensors can generate significant information about RF circuit performance. In this paper, we propose a test methodology that enables efficient BIT and BIT-enabled tuning of RF systems. A test generation approach is developed that co-optimizes the applied test stimulus, the type of embedded sensors, and the system response capture mechanisms for maximal accuracy of the BIT procedure. This BIT technique is also used to perform diagnostic testing of the transmitter. The information gathered from diagnosis is used to tune the transmitter for improved performance. Simulation results demonstrate that BIT-assisted diagnosis and tuning can be performed with good accuracy using the proposed methodology.


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## Body

## 1. Introduction

Wireless communications for both mobile and
in-office (point-to-point communication) applications are undergoing a
revolution due to the proliferation of different communication standards
spanning diverse communication bandwidths.In addition, to the use of scaled CMOS technologies for high frequency wireless
technologies running into the 10’s of GHz (seemingly impossible till a few years ago) is posing daunting
technological challenges both in design and manufacturing test. A complex multiband,
multiradio system may integrate FM radio (100 MHz), RFID (13 MHz), Digital TV
(800–1600 MHz), GPS (1.5 GHz), Bluetooth (2.4 GHz), Wi-Fi (2.4–5 GHz), 802.11
(2.4–5 GHz), Wi-Max (2.5–3.5 GHz), and UWB
(3–10 GHz) using a combination of multiple RF transceivers and use ofsoftware radio design principles [1–3] using
CMOS RF technology. This integration problem is made worse by the rapid
deployment of scaled RF CMOS technologies in high-frequency RF transceivers. As
the authors of [4] point out, individual scaled 90 nm CMOS transistors are
unconditionally stable above 40 GHz. Hence, matching networks, feedback
mechanisms, and loading networks must be designed very carefully to prevent
oscillations in high-frequency circuits. In the future, higher speed circuits
will be possible with scaled devices. However, the resulting circuits will be
increasingly susceptible to manufacturing process variations and coupled noise
from various on-chip sources. The former results in RF circuit nonidealities
due to nonlinear behavior and mismatch effects. The latter is manifested as
signal integrity problems due to
(i)
power and ground bounce effects at the package level,(ii)
On-chip substrate coupling noise between digital and analog functions,(iii)
noise induced by electromagnetic radiation, and(iv)
transient errors.Any
combination of the above can degrade overall quality of service (QoS)
and cause spectral content to spill over into adjacent communication channels. It
is clear that future advanced scaled-CMOS RF front end modules will need to be carefully
tested and calibrated to avoid these problems. In addition, to manage device
degradation in the field, self-test and self-calibration capability must be
designed into the transceiver itself.In the past, there has been significant
work in the area of built-in RF test using embedded sensors.The concept ofalternate test has been used to derive compact
low-cost test procedures [5–8]. Alternate tests make use of a compact
stimulus to estimate multiple specifications of a system with high accuracy
using a single data acquisition. The test is designed in such a way that the
response of the device under test (DUT) to the applied stimulus is strongly
correlated with the test specifications of the DUT of interest under random process
variation effects. The procedure allows accurate diagnosis of the DUT’s test
specifications during field operation without the use of complex RF test
instrumentation.In this work, a formal built-in test
methodology for RF systems is proposed that supports diagnosis and tuning of
the same. The test framework uses special embedded RF sensors for test response
data acquisition from different points of the RF signal path. The proposed
methodology determines thebest stimulus
to apply (generated by software running on the transceiver DSP) and the best embedded sensors [9–11] to use (prior work focused only on one
specific sensor type for an application) for high-quality parametric test and
diagnosis.Theimpact of the data converters specific
to the RF transceiver being tested is considered in the design methodology. The
best sensors to use are those that have the least impact on RF DUT performance, while generating the maximum amount of test information. The volume of data generated by each
sensor and the resulting test time is also considered. Section 2
describes various modeling techniques that underline the design infrastructure.
Section 2.4
describes the test generation algorithm that is used to generate a
test stimulus that maximizes the test information that can be obtained from the
DUT response.The generated test stimulus is stored in the system DSP and used
to test the system periodically during system idle time. Section 2.5
presents
built-in RF test-assisted diagnosis and tuning techniques that improve the
performance of the transmitter. Section 3
presents simulation results for the
BIT assisted diagnosis and performance tuning of an RF transmitter. Conclusions
and future work are discussed in Section 4
.


## 2. Modeling the Components of the RF Test Simulator

The key components of the RF test
simulator are shown in Figure1. The following are key BIT design goals.Figure 1
Building blocks of the proposed test simulator.(i)
Accurate behavioral modeling of
all the components of an RF transceiver (mixer, power amplifier, LNA, etc.).
This is essential to developing a high-quality built-in test solution as use of
behavioral models permits rapid simulation-driven test generation.(ii)
The applied tests are generated by an algorithm thatco-optimizes the test stimulus, the on-chip test stimulus generation and response capture
hardware (test access points, test response sensors) in such a way that the
nonidealities of the hardware (linearity, noise) are taken into consideration
while searching for the “best” built-in test solution (combination of test
stimulus, test access points, and sensors employed for built-in test).


### 2.1. Transmitter Modeling

Although, transistor-level
simulation of all the submodules yields high accuracy of simulation, long simulation
times make this impractical. The primary objective of test generation is to
determine the optimal set of test stimuli [12, 13] rather than to verify the
functionality of the design.As long as the
behavioral modeling approximations preserve the relative “goodness” of one possible test stimulus versus another, it can be used for fast test
generation with little loss in test accuracy as shown in [12, 13].The underlying
assumption is that the variations in specification data and measurement data
follow the same statistical trend under behavioral parameter perturbations as
they would for transistor level parameter perturbations. Figure2 shows the
modeling approach (similar to that proposed in [12, 13]) employed for the RF system—under-test.
The behavioral model of each subblock was constructed as follows.Figure 2
Block diagram of the behavioral model of an RF transmitter.Filter: The transfer
functions of the bandpass filters of the RF subsystem are realized as linear
transfer functions with specified magnitude and phase characteristics.Amplifiers: The
amplifiers of the RF subsystem (e.g., LNA) are realized by implementing a
nonlinear transfer function of the type
(1)y(t)=α0+α1x(t)+α2x2(t)+α3x3(t),
where α0=DCoffset, α1=smallsignalgain, α2, α3=nonlinearitycoefficients.The coefficients are “fitted” to the specified linear (gain) and
nonlinear (harmonics and intermodulation terms) effects of the amplifiers
concerned. Using the time domain input versus output characteristic as
given by (1), the corresponding output frequency spectrum (both magnitude and
phase) is computed. In general, the computation complexity is O(N3),
where N is the number of test tones used for test generation/simulation.Mixers: Mixers are
modeled as nonlinear transfer functions followed by an ideal multiplier. The
nonlinear transfer function is realized in the same manner as discussed earlier
for amplifiers. The frequency mixing operation is realized by the
multiplication operation
(2)y(t)=C×x1(t)×x2(t),
where C represents the conversion gain of the mixer.Oscillator: The peak amplitude value corresponds to the local
oscillator frequency; the amplitudes adjacent to the frequencies fall off
according to the phase-noise characteristics of the local oscillator.In the above, the coefficientsα0, α1, and so forth that are used
to define the linearity of amplifiers are extracted from the input-output (Pin−P0)
relationship of the amplifier as determined by transistor level simulation via
least squares polynomial fitting. Alternatively, they can also be extracted
from the 1 dB compression point and IIP3 specifications of the amplifiers. The
relationship between P1 dB and IIP3 can also be used to obtain the behavioral
parameters of the subsystems. Equation (3) describes the modeling
process mathematically;
(3)α3=P0(P1dB2Pin/0.145)+(3Pin/4),α1=α3P1dB20.145.


### 2.2. Modeling ADC/DAC Nonlinearity

The nonlinearities of the ADCs/DACs used
for data acquisition and stimulus generation (in production test systems)
significantly affect the signal quality of both the applied test stimulus and
the observed test response. The capture characteristics are modeled from the
specified nonlinear characteristics of the PCI6115 data acquisition card (DAQ)
used in the prototype test system. The PCI6115 is used as a low cost
replacement for an ATE to transfer test data to the baseband processor (in our
case, a PC). The INL and DNL characteristics obtained from the datasheets of
the PCI6115 DAQ card are used to characterize the sourcing and digitizing
properties of the data acquisition system used in this work. The frequency
domain inputs and outputs of the developed digitizer model are shown in Figure3. It can be observed that the signal quality of the test response is
significantly affected by the nonlinearities of the sourcer/digitizer modules.Figure 3
Test response before and after the digitizer module.


### 2.3. Modeling Test Response Sensors

In this section, the modeling of three
different types of test response sensors has been discussed.


#### 2.3.1. Envelope Detector Sensor

The envelope detector
employed is shown in Figure4 and its uses as a test response sensor were first
proposed by Han and Chatterjee in [14]. This is a common circuit for AM
demodulation, composed of a diode, a resistor, and a capacitor. Through proper
adjustment of the RC constant value, the requisite envelope detection can be
performed.
The value of the RC time constant should be such that  fo≪1/RC≪fc. This allows the circuit to track the low-frequency
envelope of the RF signal. Considering that the above two frequencies have
large separation, the RC time constant can be picked to make the decoded
envelope immune to process variations.Figure 4
The schematic of the envelope detector.A key consideration is the loading of
the circuit under test (CUT) by the envelope detector. The input impedance of
the envelope detector depends mainly on the bias resistors and the capacitance
of the diode. The bias resistors are relatively large compared to the typical
50 Ohm RF matching impedance. During the normal operating mode, the power for
the envelope detector can be turned off using a switch [14]. Hence, the diode
behaves like an open switch. In general, the capacitance of a diode is several
tens of farads. Therefore, the impedance (loading) of the envelope detector has
negligible effect on the RF DUT performance. Figure 5 shows the transient
output of the envelope detector showing that it follows the RF envelope. The
observed output is a transient waveform and hence it has much more information
compared to the o/p of RMS or peak detectors. However, this added information
comes at the cost of having to process much more data than just single DC RMS
or peak values.Figure 5
Envelope detector output.


#### 2.3.2. RMS Detector Sensor

Figure6 shows the RMS detector
circuit [15] used as the test case in this research. Elements L1 and C1 form
the matching network, providing a 50 Ohm match at 1.5 GHz.Figure 6
Schematic diagram of the RMS detector.The low value of S11 for this detector
(<−45 dB) results in minimal loading of the RF system.
R2
is chosen so that
the circuit provides the “true” RMS value of the input signal to the detector.Though it is debated in [16] that this does not achieve true RMS detection, the
accuracy achieved is good for the purpose of this work.The output of the detector for a multitone input stimulus is plotted
in Figure7 for four different specified combinations of input power levels. It is
observed that both the DC value as well as the ripple voltage increases with the increase in
input power level.Figure 7
RMS detector output for different power levels of multitone input signal.


#### 2.3.3. Peak Detector

The conceptual diagram
of a standard peak detector is shown in Figure8. The biasing and matching
circuits have not been shown for simplicity. The output is a DC voltage
indicating the peak of the applied voltage. As shown in Figure 8, the voltage Vo takes some time to reach its final value. This transient response is ignored
and the final steady-state value is considered as the peak value.Figure 8
Conceptual diagram of the peak detector.Figure9 shows the transient output
voltage for different power levels of a multitone input signal. The voltage attains its steady-state level after a certain amount of time depending on the
input power level. This information is used to model the peak detector.Figure 9
Transient response of the peak detector to multitone input signals of different power levels.


### 2.4. Test Generation

Alternate test procedures have been
extensively used in the past for analog/RF circuits to accurately predict
specifications of interest. In this approach, regression functions such as
those generated by MARS [8] are used to build a mapping function between the
test response and the DUT specifications using a set of devices with random
process variations called the training
set.During the actual test, trained MARS models are used to predict the
specifications of the DUT from the observed test response.It should be noted here that adefect filter is used to screen out
devices with catastrophic faults, and only the devices that “pass” the defect
filter are considered for this study. This defect filter consists of simple
algorithms that analyze the test response of the DUT to determine whether it
has characteristics that are different from the “training set” of devices used
to build the MARS regression model described earlier. The alternate test quality is highly correlated to the sensitivity of the observed
measurement response to changes in the process parameters. For a given DUT,
there are several test generation algorithms [17] available for optimizing the
input test stimulus. In this work, a genetic algorithm-based test generator (as
shown in Figure 10) is used due to the nonlinear nature of the search space to avoid local
convergence. An optimized multitone test input is used as
the test stimulus of choice. The multitone test stimulus is binary coded to
result in a genetic individual of length 100 bits, with each tone represented
by a 5 bit gene sequence.Figure 10
GA-based optimization routine.Crossover and mutation are the two mechanisms by which a new generation of solutions is created
[18]. Figure 11 shows an example case of crossover. In crossover, two parent
genes are crossed over in an arbitrary fashion defined by the crossover
probability to result in a completely new child. In mutation a single bit of the gene sequence is randomly mutated as determined by a
mutation probability to result in a new child. These two
mechanisms are responsible for the creation of new individuals which in-turn are assessed for
their fitness. There is one
another mechanism known as elitism in which genes with a particular fitness
values are classified as elite and are passed on from one generation to
another without any crossover or mutation. This ensures that a “minimum” fitness is
passed on from one generation of solutions to another. Further insights into the
algorithms can be found in [8].Figure 11
Single-point crossover operation for reproduction in the GA.The multitone test
stimulus is a carefully crafted test stimulus with selected number of tones and
varying amplitude levels. A set of 20 tones are uniformly deployed across a
bandwidth of 1 MHz. The amplitude levels are allowed to have 32 (25)
levels of variation ranging from −70 to 10 dBm. Thus, each tone can be coded
into a 5 bit gene sequence to result in a 100 bit gene individual. This
waveform is then converted to the frequency domain by means of the Fourier
transform.Figure12 shows the
frequency domain representation of two multitone signals with 20 component
tones with coherent and noncoherent sampling. The wide-skirts observed in Figure
12 are the outcome of noncoherent sampling of the time domain waveform.
Coherency is mathematically represented [19] as follows:
(4)finfs=nwindownrecord,
where
fin is the input
frequency of the multitone signal; fs is the sampling frequency; nwindow is the number of cycles of the
signal that fits in a sampling window; and nrecord is the number of data points
in the FFT of the signal. The presence of unwanted tones in the test input
degrades the test quality significantly. Hence, it is very critical that the
multitone is coherently sampled. The genetically optimized waveform is then used to obtain the response of the
component receivers. The subsystem specifications are computed by mapping the response to
the subsystem specifications using nonlinear regression techniques.Figure 12
Coherently and noncoherently sampled response.The proposed test
simulator as described above is used to evaluate different BIT alternatives. A
set of candidate BIT solutions is first selected for evaluation. Each candidate
consists of a set of sensors (envelope, rms, or peak) attached to a specified
set of nodes (output of mixer or power amplifier). The test generation
algorithm produces an optimized test stimulus for each BIT candidate. The BIT
candidates are then ranked in order of the accuracy with which the test
specifications of the RF DUT can be predicted from the observed test response.
Since the hardware cost of each BIT solution is also known, the test designer
can then pick the best solution from a cost versus accuracy perspective as
desired by the test customer.


### 2.5. Built-in RF Test-Assisted Diagnosis and Tuning Procedure

The BIT technique
evaluated by the test simulator is used to drive diagnosis and tuning
procedures for the RF transceiver. The diagnosis and tuning performed here restores
the performance of the DUT in the presence of process variation induced performance
degradation. Figure13 describes the proposed approach. A built-in envelope tracking sensor (or other sensors as presented
earlier) at the output of the transmitter is used to capture the transmitter response.
The envelope detector consumes very little area overhead and is robust to
process variations. The sensor captures the envelope of a multisine test
stimulus, and ‘‘response features’’ extracted from this envelope are used to
track the power and nonlinearity specifications (amplitude and phase distortion)
of the power amplifier (PA). The transmitter can be tested during production
testing, or while the system is idle (concurrent testing).Figure 13
The proposed transmitter diagnostic testing and compensation approach.The captured response is a low-frequency
signal that contains information about the linearity of the PA, is sampled, and
fed to the baseband processor. The performance parameters of the transmitter are
then predicted accurately from analysis of the obtained response envelope using
nonlinear regression mapping functions built from calibration experiments.The
test stimulus is designed to exercise the nonlinearities of the PA. Optimized test stimuli is fed
individually to I and Q channels to exercise PA non-linearity.When multiple nonidealities (I/Q mismatch, frequency
offset) are present in the transmitter, a test stimulus optimization algorithm
is used to design the test stimulus to increase the accuracy of transmitter specification prediction from the obtained
response. Using the captured ‘‘response features’’ of the transmitter, the
behavioral performance parameters are predicted. After the PA nonlinearity is determined,
an inverse function to the predicted PA transfer characteristics is computed to
determine the predistortion coefficients (alpha values) and stored in a lookup
table (LUT) in the DSP for correction (tuning). During real-time operation,
the input signal to the transmitter instance is predistorted by the
corresponding polynomial to tune out the effects of nonidealities present in
the transmitter system.


## 2.1. Transmitter Modeling

Although, transistor-level
simulation of all the submodules yields high accuracy of simulation, long simulation
times make this impractical. The primary objective of test generation is to
determine the optimal set of test stimuli [12, 13] rather than to verify the
functionality of the design.As long as the
behavioral modeling approximations preserve the relative “goodness” of one possible test stimulus versus another, it can be used for fast test
generation with little loss in test accuracy as shown in [12, 13].The underlying
assumption is that the variations in specification data and measurement data
follow the same statistical trend under behavioral parameter perturbations as
they would for transistor level parameter perturbations. Figure2 shows the
modeling approach (similar to that proposed in [12, 13]) employed for the RF system—under-test.
The behavioral model of each subblock was constructed as follows.Figure 2
Block diagram of the behavioral model of an RF transmitter.Filter: The transfer
functions of the bandpass filters of the RF subsystem are realized as linear
transfer functions with specified magnitude and phase characteristics.Amplifiers: The
amplifiers of the RF subsystem (e.g., LNA) are realized by implementing a
nonlinear transfer function of the type
(1)y(t)=α0+α1x(t)+α2x2(t)+α3x3(t),
where α0=DCoffset, α1=smallsignalgain, α2, α3=nonlinearitycoefficients.The coefficients are “fitted” to the specified linear (gain) and
nonlinear (harmonics and intermodulation terms) effects of the amplifiers
concerned. Using the time domain input versus output characteristic as
given by (1), the corresponding output frequency spectrum (both magnitude and
phase) is computed. In general, the computation complexity is O(N3),
where N is the number of test tones used for test generation/simulation.Mixers: Mixers are
modeled as nonlinear transfer functions followed by an ideal multiplier. The
nonlinear transfer function is realized in the same manner as discussed earlier
for amplifiers. The frequency mixing operation is realized by the
multiplication operation
(2)y(t)=C×x1(t)×x2(t),
where C represents the conversion gain of the mixer.Oscillator: The peak amplitude value corresponds to the local
oscillator frequency; the amplitudes adjacent to the frequencies fall off
according to the phase-noise characteristics of the local oscillator.In the above, the coefficientsα0, α1, and so forth that are used
to define the linearity of amplifiers are extracted from the input-output (Pin−P0)
relationship of the amplifier as determined by transistor level simulation via
least squares polynomial fitting. Alternatively, they can also be extracted
from the 1 dB compression point and IIP3 specifications of the amplifiers. The
relationship between P1 dB and IIP3 can also be used to obtain the behavioral
parameters of the subsystems. Equation (3) describes the modeling
process mathematically;
(3)α3=P0(P1dB2Pin/0.145)+(3Pin/4),α1=α3P1dB20.145.


## 2.2. Modeling ADC/DAC Nonlinearity

The nonlinearities of the ADCs/DACs used
for data acquisition and stimulus generation (in production test systems)
significantly affect the signal quality of both the applied test stimulus and
the observed test response. The capture characteristics are modeled from the
specified nonlinear characteristics of the PCI6115 data acquisition card (DAQ)
used in the prototype test system. The PCI6115 is used as a low cost
replacement for an ATE to transfer test data to the baseband processor (in our
case, a PC). The INL and DNL characteristics obtained from the datasheets of
the PCI6115 DAQ card are used to characterize the sourcing and digitizing
properties of the data acquisition system used in this work. The frequency
domain inputs and outputs of the developed digitizer model are shown in Figure3. It can be observed that the signal quality of the test response is
significantly affected by the nonlinearities of the sourcer/digitizer modules.Figure 3
Test response before and after the digitizer module.


## 2.3. Modeling Test Response Sensors

In this section, the modeling of three
different types of test response sensors has been discussed.


### 2.3.1. Envelope Detector Sensor

The envelope detector
employed is shown in Figure4 and its uses as a test response sensor were first
proposed by Han and Chatterjee in [14]. This is a common circuit for AM
demodulation, composed of a diode, a resistor, and a capacitor. Through proper
adjustment of the RC constant value, the requisite envelope detection can be
performed.
The value of the RC time constant should be such that  fo≪1/RC≪fc. This allows the circuit to track the low-frequency
envelope of the RF signal. Considering that the above two frequencies have
large separation, the RC time constant can be picked to make the decoded
envelope immune to process variations.Figure 4
The schematic of the envelope detector.A key consideration is the loading of
the circuit under test (CUT) by the envelope detector. The input impedance of
the envelope detector depends mainly on the bias resistors and the capacitance
of the diode. The bias resistors are relatively large compared to the typical
50 Ohm RF matching impedance. During the normal operating mode, the power for
the envelope detector can be turned off using a switch [14]. Hence, the diode
behaves like an open switch. In general, the capacitance of a diode is several
tens of farads. Therefore, the impedance (loading) of the envelope detector has
negligible effect on the RF DUT performance. Figure 5 shows the transient
output of the envelope detector showing that it follows the RF envelope. The
observed output is a transient waveform and hence it has much more information
compared to the o/p of RMS or peak detectors. However, this added information
comes at the cost of having to process much more data than just single DC RMS
or peak values.Figure 5
Envelope detector output.


### 2.3.2. RMS Detector Sensor

Figure6 shows the RMS detector
circuit [15] used as the test case in this research. Elements L1 and C1 form
the matching network, providing a 50 Ohm match at 1.5 GHz.Figure 6
Schematic diagram of the RMS detector.The low value of S11 for this detector
(<−45 dB) results in minimal loading of the RF system.
R2
is chosen so that
the circuit provides the “true” RMS value of the input signal to the detector.Though it is debated in [16] that this does not achieve true RMS detection, the
accuracy achieved is good for the purpose of this work.The output of the detector for a multitone input stimulus is plotted
in Figure7 for four different specified combinations of input power levels. It is
observed that both the DC value as well as the ripple voltage increases with the increase in
input power level.Figure 7
RMS detector output for different power levels of multitone input signal.


### 2.3.3. Peak Detector

The conceptual diagram
of a standard peak detector is shown in Figure8. The biasing and matching
circuits have not been shown for simplicity. The output is a DC voltage
indicating the peak of the applied voltage. As shown in Figure 8, the voltage Vo takes some time to reach its final value. This transient response is ignored
and the final steady-state value is considered as the peak value.Figure 8
Conceptual diagram of the peak detector.Figure9 shows the transient output
voltage for different power levels of a multitone input signal. The voltage attains its steady-state level after a certain amount of time depending on the
input power level. This information is used to model the peak detector.Figure 9
Transient response of the peak detector to multitone input signals of different power levels.


## 2.3.1. Envelope Detector Sensor

The envelope detector
employed is shown in Figure4 and its uses as a test response sensor were first
proposed by Han and Chatterjee in [14]. This is a common circuit for AM
demodulation, composed of a diode, a resistor, and a capacitor. Through proper
adjustment of the RC constant value, the requisite envelope detection can be
performed.
The value of the RC time constant should be such that  fo≪1/RC≪fc. This allows the circuit to track the low-frequency
envelope of the RF signal. Considering that the above two frequencies have
large separation, the RC time constant can be picked to make the decoded
envelope immune to process variations.Figure 4
The schematic of the envelope detector.A key consideration is the loading of
the circuit under test (CUT) by the envelope detector. The input impedance of
the envelope detector depends mainly on the bias resistors and the capacitance
of the diode. The bias resistors are relatively large compared to the typical
50 Ohm RF matching impedance. During the normal operating mode, the power for
the envelope detector can be turned off using a switch [14]. Hence, the diode
behaves like an open switch. In general, the capacitance of a diode is several
tens of farads. Therefore, the impedance (loading) of the envelope detector has
negligible effect on the RF DUT performance. Figure 5 shows the transient
output of the envelope detector showing that it follows the RF envelope. The
observed output is a transient waveform and hence it has much more information
compared to the o/p of RMS or peak detectors. However, this added information
comes at the cost of having to process much more data than just single DC RMS
or peak values.Figure 5
Envelope detector output.


## 2.3.2. RMS Detector Sensor

Figure6 shows the RMS detector
circuit [15] used as the test case in this research. Elements L1 and C1 form
the matching network, providing a 50 Ohm match at 1.5 GHz.Figure 6
Schematic diagram of the RMS detector.The low value of S11 for this detector
(<−45 dB) results in minimal loading of the RF system.
R2
is chosen so that
the circuit provides the “true” RMS value of the input signal to the detector.Though it is debated in [16] that this does not achieve true RMS detection, the
accuracy achieved is good for the purpose of this work.The output of the detector for a multitone input stimulus is plotted
in Figure7 for four different specified combinations of input power levels. It is
observed that both the DC value as well as the ripple voltage increases with the increase in
input power level.Figure 7
RMS detector output for different power levels of multitone input signal.


## 2.3.3. Peak Detector

The conceptual diagram
of a standard peak detector is shown in Figure8. The biasing and matching
circuits have not been shown for simplicity. The output is a DC voltage
indicating the peak of the applied voltage. As shown in Figure 8, the voltage Vo takes some time to reach its final value. This transient response is ignored
and the final steady-state value is considered as the peak value.Figure 8
Conceptual diagram of the peak detector.Figure9 shows the transient output
voltage for different power levels of a multitone input signal. The voltage attains its steady-state level after a certain amount of time depending on the
input power level. This information is used to model the peak detector.Figure 9
Transient response of the peak detector to multitone input signals of different power levels.


## 2.4. Test Generation

Alternate test procedures have been
extensively used in the past for analog/RF circuits to accurately predict
specifications of interest. In this approach, regression functions such as
those generated by MARS [8] are used to build a mapping function between the
test response and the DUT specifications using a set of devices with random
process variations called the training
set.During the actual test, trained MARS models are used to predict the
specifications of the DUT from the observed test response.It should be noted here that adefect filter is used to screen out
devices with catastrophic faults, and only the devices that “pass” the defect
filter are considered for this study. This defect filter consists of simple
algorithms that analyze the test response of the DUT to determine whether it
has characteristics that are different from the “training set” of devices used
to build the MARS regression model described earlier. The alternate test quality is highly correlated to the sensitivity of the observed
measurement response to changes in the process parameters. For a given DUT,
there are several test generation algorithms [17] available for optimizing the
input test stimulus. In this work, a genetic algorithm-based test generator (as
shown in Figure 10) is used due to the nonlinear nature of the search space to avoid local
convergence. An optimized multitone test input is used as
the test stimulus of choice. The multitone test stimulus is binary coded to
result in a genetic individual of length 100 bits, with each tone represented
by a 5 bit gene sequence.Figure 10
GA-based optimization routine.Crossover and mutation are the two mechanisms by which a new generation of solutions is created
[18]. Figure 11 shows an example case of crossover. In crossover, two parent
genes are crossed over in an arbitrary fashion defined by the crossover
probability to result in a completely new child. In mutation a single bit of the gene sequence is randomly mutated as determined by a
mutation probability to result in a new child. These two
mechanisms are responsible for the creation of new individuals which in-turn are assessed for
their fitness. There is one
another mechanism known as elitism in which genes with a particular fitness
values are classified as elite and are passed on from one generation to
another without any crossover or mutation. This ensures that a “minimum” fitness is
passed on from one generation of solutions to another. Further insights into the
algorithms can be found in [8].Figure 11
Single-point crossover operation for reproduction in the GA.The multitone test
stimulus is a carefully crafted test stimulus with selected number of tones and
varying amplitude levels. A set of 20 tones are uniformly deployed across a
bandwidth of 1 MHz. The amplitude levels are allowed to have 32 (25)
levels of variation ranging from −70 to 10 dBm. Thus, each tone can be coded
into a 5 bit gene sequence to result in a 100 bit gene individual. This
waveform is then converted to the frequency domain by means of the Fourier
transform.Figure12 shows the
frequency domain representation of two multitone signals with 20 component
tones with coherent and noncoherent sampling. The wide-skirts observed in Figure
12 are the outcome of noncoherent sampling of the time domain waveform.
Coherency is mathematically represented [19] as follows:
(4)finfs=nwindownrecord,
where
fin is the input
frequency of the multitone signal; fs is the sampling frequency; nwindow is the number of cycles of the
signal that fits in a sampling window; and nrecord is the number of data points
in the FFT of the signal. The presence of unwanted tones in the test input
degrades the test quality significantly. Hence, it is very critical that the
multitone is coherently sampled. The genetically optimized waveform is then used to obtain the response of the
component receivers. The subsystem specifications are computed by mapping the response to
the subsystem specifications using nonlinear regression techniques.Figure 12
Coherently and noncoherently sampled response.The proposed test
simulator as described above is used to evaluate different BIT alternatives. A
set of candidate BIT solutions is first selected for evaluation. Each candidate
consists of a set of sensors (envelope, rms, or peak) attached to a specified
set of nodes (output of mixer or power amplifier). The test generation
algorithm produces an optimized test stimulus for each BIT candidate. The BIT
candidates are then ranked in order of the accuracy with which the test
specifications of the RF DUT can be predicted from the observed test response.
Since the hardware cost of each BIT solution is also known, the test designer
can then pick the best solution from a cost versus accuracy perspective as
desired by the test customer.


## 2.5. Built-in RF Test-Assisted Diagnosis and Tuning Procedure

The BIT technique
evaluated by the test simulator is used to drive diagnosis and tuning
procedures for the RF transceiver. The diagnosis and tuning performed here restores
the performance of the DUT in the presence of process variation induced performance
degradation. Figure13 describes the proposed approach. A built-in envelope tracking sensor (or other sensors as presented
earlier) at the output of the transmitter is used to capture the transmitter response.
The envelope detector consumes very little area overhead and is robust to
process variations. The sensor captures the envelope of a multisine test
stimulus, and ‘‘response features’’ extracted from this envelope are used to
track the power and nonlinearity specifications (amplitude and phase distortion)
of the power amplifier (PA). The transmitter can be tested during production
testing, or while the system is idle (concurrent testing).Figure 13
The proposed transmitter diagnostic testing and compensation approach.The captured response is a low-frequency
signal that contains information about the linearity of the PA, is sampled, and
fed to the baseband processor. The performance parameters of the transmitter are
then predicted accurately from analysis of the obtained response envelope using
nonlinear regression mapping functions built from calibration experiments.The
test stimulus is designed to exercise the nonlinearities of the PA. Optimized test stimuli is fed
individually to I and Q channels to exercise PA non-linearity.When multiple nonidealities (I/Q mismatch, frequency
offset) are present in the transmitter, a test stimulus optimization algorithm
is used to design the test stimulus to increase the accuracy of transmitter specification prediction from the obtained
response. Using the captured ‘‘response features’’ of the transmitter, the
behavioral performance parameters are predicted. After the PA nonlinearity is determined,
an inverse function to the predicted PA transfer characteristics is computed to
determine the predistortion coefficients (alpha values) and stored in a lookup
table (LUT) in the DSP for correction (tuning). During real-time operation,
the input signal to the transmitter instance is predistorted by the
corresponding polynomial to tune out the effects of nonidealities present in
the transmitter system.


## 3. Validation of the Proposed Methodology

The proposed BIT design methodology has
been validated on a wireless RF transmitter. The transmitter was modeled as
described in Section2. The proposed test simulator was implemented in Matlab.


### 3.1. BIT Evaluation

The test simulator allows evaluation of
different choices of test access points (nodes at which test response sensors
are inserted) and test response sensors, while taking into account the
nonlinearity and noise of the on-chip test response data acquisition and
stimulus generation hardware. Optimized tests are generated for each such test
configuration using the genetic algorithm-based test generator described earlier
and the “best” test configuration (the one that allows the specifications of
the DUT to be predicted from the test response with the highest accuracy with
minimal hardware cost) is used for on-chip BIT.


#### 3.1.1. Impact of Test Response Sensors on Performance of Transmitter

For
simplicity, the sensors were deployed only at the output of the power amplifier
of the RF transmitter. The
performance degradation simulation was performed using Agilent Advanced Design System
(ADS). The performance degradation plots in terms of the
operatinggain of the transmitter are
shown in Figure 14. The results are quantified numerically in Table 1 for a
specified level output power level of the transmitter.Table 1
Performance degradation of the power amplifier at 15 dBm output power level.
ComponentActualEnvelopePeakRMSPA15.1614.1410.5810.64Figure 14
Performance degradation of the power amplifier for three types of sensors.It is observed that the envelope detector provides the least performance degradation of 1 dB at an output power of 15 dBm.


#### 3.1.2. Impact of Test Response Sensors on Test Quality

The quality of the
alternate test procedure employed was determined by the accuracy with which the
specifications of the DUT could be predicted from the obtained BIT response.The
rms prediction error over a large number of sample DUTs was used to quantify
the quality of the BIT technique. This is shown in Table2.Table 2
Evaluation of the test quality for three types of DfT solution.
Error typeEnvelopePeakRMSRms_error0.14750.20.21


#### 3.1.3. Sensor Selection

In general, the BIT
solution that provides the least amount of performance degradation while
providing acceptable accuracy of specification prediction from the test response
is chosen as the final test solution. The envelope detection sensor
attached to the output of the transmitter was found to have the best performance of the three
sensors investigated in this work. It is important to
note here that the developed tool has the capability to select the best
possible DfT solution given an available set of sensors and their designs (e.g.,
there are many different ways in rms sensor can be designed). In the following, the
recommended DfT solution is used to perform diagnostic testing and tuning of
the RF transmitter.


#### 3.1.4. BIT-Assisted Diagnosis of the RF Transmitter

The best possible test input as generated
by the test simulator for the recommended DfT solution is stored in the system
DSP and is used to drive the BIT procedure from the DSP. The behavioral
parametersα1 and α3 of (1) are used to characterize
the transmitter’s performance and are predicted from the obtained BIT response
to the applied test stimulus. The developed BIT technique was
evaluated on a set of 23 instances of the transmitter. The prediction plots of the diagnostic
parameters for the transmitter are shown in Figure 15. As seen from the plots,
the parameters are estimated with good accuracy.Diagnostic parameter estimation for the evaluated BIT technique.
(a)(b)


#### 3.1.5. Transmitter Tuning

Once the
polynomial of (1) corresponding to the transmitter is known, a lookup table
based inverse correction function that predistorts the input of the transmitter
to compensate for its intrinsic nonlinearity is used to improve transmitter
performance. To evaluate the effectiveness of the tuning procedure, the
transmitter was interfaced with a baseband OFDM module and the system was
simulated by transmitting QPSK modulated frames. Figure 16 shows the transmitted
OFDM-QPSK constellation plots with and without digital predistortion. As
observed from the plots, the nonlinear characteristics of the transmitter are compensated
accurately (nonlinearity results in a “spread” of each of the four
constellation points corresponding to OFDM-QPSK as observed in the left
constellation diagram.OFDM-QPSK constellation plots for a transmitter.
(a)(b)Currently, test quality along with the
performance degradation of the sensor is used as the primary method for BIT
sensor selection. In general, the dynamic range of the sensors, the volume of
sensor data generated as well as the complexity of the sensor design and
performance (sensor bandwidth and loading of the DUT) will need to be
considered as well and can be incorporated easily into the test simulator.


## 3.1. BIT Evaluation

The test simulator allows evaluation of
different choices of test access points (nodes at which test response sensors
are inserted) and test response sensors, while taking into account the
nonlinearity and noise of the on-chip test response data acquisition and
stimulus generation hardware. Optimized tests are generated for each such test
configuration using the genetic algorithm-based test generator described earlier
and the “best” test configuration (the one that allows the specifications of
the DUT to be predicted from the test response with the highest accuracy with
minimal hardware cost) is used for on-chip BIT.


### 3.1.1. Impact of Test Response Sensors on Performance of Transmitter

For
simplicity, the sensors were deployed only at the output of the power amplifier
of the RF transmitter. The
performance degradation simulation was performed using Agilent Advanced Design System
(ADS). The performance degradation plots in terms of the
operatinggain of the transmitter are
shown in Figure 14. The results are quantified numerically in Table 1 for a
specified level output power level of the transmitter.Table 1
Performance degradation of the power amplifier at 15 dBm output power level.
ComponentActualEnvelopePeakRMSPA15.1614.1410.5810.64Figure 14
Performance degradation of the power amplifier for three types of sensors.It is observed that the envelope detector provides the least performance degradation of 1 dB at an output power of 15 dBm.


### 3.1.2. Impact of Test Response Sensors on Test Quality

The quality of the
alternate test procedure employed was determined by the accuracy with which the
specifications of the DUT could be predicted from the obtained BIT response.The
rms prediction error over a large number of sample DUTs was used to quantify
the quality of the BIT technique. This is shown in Table2.Table 2
Evaluation of the test quality for three types of DfT solution.
Error typeEnvelopePeakRMSRms_error0.14750.20.21


### 3.1.3. Sensor Selection

In general, the BIT
solution that provides the least amount of performance degradation while
providing acceptable accuracy of specification prediction from the test response
is chosen as the final test solution. The envelope detection sensor
attached to the output of the transmitter was found to have the best performance of the three
sensors investigated in this work. It is important to
note here that the developed tool has the capability to select the best
possible DfT solution given an available set of sensors and their designs (e.g.,
there are many different ways in rms sensor can be designed). In the following, the
recommended DfT solution is used to perform diagnostic testing and tuning of
the RF transmitter.


### 3.1.4. BIT-Assisted Diagnosis of the RF Transmitter

The best possible test input as generated
by the test simulator for the recommended DfT solution is stored in the system
DSP and is used to drive the BIT procedure from the DSP. The behavioral
parametersα1 and α3 of (1) are used to characterize
the transmitter’s performance and are predicted from the obtained BIT response
to the applied test stimulus. The developed BIT technique was
evaluated on a set of 23 instances of the transmitter. The prediction plots of the diagnostic
parameters for the transmitter are shown in Figure 15. As seen from the plots,
the parameters are estimated with good accuracy.Diagnostic parameter estimation for the evaluated BIT technique.
(a)(b)


### 3.1.5. Transmitter Tuning

Once the
polynomial of (1) corresponding to the transmitter is known, a lookup table
based inverse correction function that predistorts the input of the transmitter
to compensate for its intrinsic nonlinearity is used to improve transmitter
performance. To evaluate the effectiveness of the tuning procedure, the
transmitter was interfaced with a baseband OFDM module and the system was
simulated by transmitting QPSK modulated frames. Figure 16 shows the transmitted
OFDM-QPSK constellation plots with and without digital predistortion. As
observed from the plots, the nonlinear characteristics of the transmitter are compensated
accurately (nonlinearity results in a “spread” of each of the four
constellation points corresponding to OFDM-QPSK as observed in the left
constellation diagram.OFDM-QPSK constellation plots for a transmitter.
(a)(b)Currently, test quality along with the
performance degradation of the sensor is used as the primary method for BIT
sensor selection. In general, the dynamic range of the sensors, the volume of
sensor data generated as well as the complexity of the sensor design and
performance (sensor bandwidth and loading of the DUT) will need to be
considered as well and can be incorporated easily into the test simulator.


## 3.1.1. Impact of Test Response Sensors on Performance of Transmitter

For
simplicity, the sensors were deployed only at the output of the power amplifier
of the RF transmitter. The
performance degradation simulation was performed using Agilent Advanced Design System
(ADS). The performance degradation plots in terms of the
operatinggain of the transmitter are
shown in Figure 14. The results are quantified numerically in Table 1 for a
specified level output power level of the transmitter.Table 1
Performance degradation of the power amplifier at 15 dBm output power level.
ComponentActualEnvelopePeakRMSPA15.1614.1410.5810.64Figure 14
Performance degradation of the power amplifier for three types of sensors.It is observed that the envelope detector provides the least performance degradation of 1 dB at an output power of 15 dBm.


## 3.1.2. Impact of Test Response Sensors on Test Quality

The quality of the
alternate test procedure employed was determined by the accuracy with which the
specifications of the DUT could be predicted from the obtained BIT response.The
rms prediction error over a large number of sample DUTs was used to quantify
the quality of the BIT technique. This is shown in Table2.Table 2
Evaluation of the test quality for three types of DfT solution.
Error typeEnvelopePeakRMSRms_error0.14750.20.21


## 3.1.3. Sensor Selection

In general, the BIT
solution that provides the least amount of performance degradation while
providing acceptable accuracy of specification prediction from the test response
is chosen as the final test solution. The envelope detection sensor
attached to the output of the transmitter was found to have the best performance of the three
sensors investigated in this work. It is important to
note here that the developed tool has the capability to select the best
possible DfT solution given an available set of sensors and their designs (e.g.,
there are many different ways in rms sensor can be designed). In the following, the
recommended DfT solution is used to perform diagnostic testing and tuning of
the RF transmitter.


## 3.1.4. BIT-Assisted Diagnosis of the RF Transmitter

The best possible test input as generated
by the test simulator for the recommended DfT solution is stored in the system
DSP and is used to drive the BIT procedure from the DSP. The behavioral
parametersα1 and α3 of (1) are used to characterize
the transmitter’s performance and are predicted from the obtained BIT response
to the applied test stimulus. The developed BIT technique was
evaluated on a set of 23 instances of the transmitter. The prediction plots of the diagnostic
parameters for the transmitter are shown in Figure 15. As seen from the plots,
the parameters are estimated with good accuracy.Diagnostic parameter estimation for the evaluated BIT technique.
(a)(b)


## 3.1.5. Transmitter Tuning

Once the
polynomial of (1) corresponding to the transmitter is known, a lookup table
based inverse correction function that predistorts the input of the transmitter
to compensate for its intrinsic nonlinearity is used to improve transmitter
performance. To evaluate the effectiveness of the tuning procedure, the
transmitter was interfaced with a baseband OFDM module and the system was
simulated by transmitting QPSK modulated frames. Figure 16 shows the transmitted
OFDM-QPSK constellation plots with and without digital predistortion. As
observed from the plots, the nonlinear characteristics of the transmitter are compensated
accurately (nonlinearity results in a “spread” of each of the four
constellation points corresponding to OFDM-QPSK as observed in the left
constellation diagram.OFDM-QPSK constellation plots for a transmitter.
(a)(b)Currently, test quality along with the
performance degradation of the sensor is used as the primary method for BIT
sensor selection. In general, the dynamic range of the sensors, the volume of
sensor data generated as well as the complexity of the sensor design and
performance (sensor bandwidth and loading of the DUT) will need to be
considered as well and can be incorporated easily into the test simulator.


## 4. Conclusion and Future

A methodology for optimizing built-in test infrastructure to diagnose and
tune an RF transmitter is presented here.The
evaluated BIT technique can accurately
diagnose the system performance parameters. Also, a tuning procedure for
digital predistortion is proposed that accurately compensates for system
nonlinearities. These results justify the need to co-optimize the DfT
structures along with the response capture characteristics to result in a
highly accurate BIT. Current work is focused on extending the approach to a
complete RF transceiver.



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*Source: 418165-2008-06-23.xml*