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.
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 of power and ground bounce effects at the package level, On-chip substrate coupling noise between digital and analog functions, noise induced by electromagnetic radiation, and 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 of
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 the
The
The key components of the RF test
simulator are shown in Figure
Building blocks of the proposed test simulator.
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. The applied tests are generated by an algorithm that
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 [
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 [
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. Figure
Block diagram of the behavioral model of an RF transmitter.
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 (
In the above, the coefficients
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 Figure
Test response before and after the digitizer module.
In this section, the modeling of three different types of test response sensors has been discussed.
The envelope detector
employed is shown in Figure
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 [
Envelope detector output.
Figure
Schematic diagram of the RMS detector.
The low value of S11 for this detector
(
The output of the detector for a multitone input stimulus is plotted
in Figure
RMS detector output for different power levels of multitone input signal.
The conceptual diagram
of a standard peak detector is shown in Figure
Conceptual diagram of the peak detector.
Figure
Transient response of the peak detector to multitone input signals of different power levels.
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 [
It should be noted here that a
GA-based optimization routine.
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.
Figure
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.
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. Figure
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.
The proposed BIT design methodology has
been validated on a wireless RF transmitter. The transmitter was modeled as
described in Section
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.
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
operating
Performance degradation of the power amplifier at 15 dBm output power level.
Component | Actual | Envelope | Peak | RMS |
---|---|---|---|---|
PA | 15.16 | 14.14 | 10.58 | 10.64 |
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.
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 Table
Evaluation of the test quality for three types of DfT solution.
Error type | Envelope | Peak | RMS |
---|---|---|---|
Rms_error | 0.1475 | 0.2 | 0.21 |
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.
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
parameters
Diagnostic parameter estimation for the evaluated BIT technique.
Once the
polynomial of (
OFDM-QPSK constellation plots for a transmitter.
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.
A methodology for optimizing built-in test infrastructure to diagnose and
tune an RF transmitter is presented here.
This work was funded in part by NSF ITR award CCR-0325555 and GSRC/MARCO 2003-DT-660.