IEEE 2005 CUSTOM INTEGRATED CIRCUITS CONFERENCE
A Signal Integrity-based Link Performance
Simulation Platform
Yuming Tao, Wm. Bereza, Rakesh H. Patel*, Sergey Shumarayev* and Tad Kwasniewski
Altera Corp.
Ottawa, ON K2K3C9 Canada, *San Jose, CA, 95134 USA
ABSTRACT-This paper embodies a methodology used to
create high-speed transceiver behavior models employed
within a signal integrity-based link simulation platform.
This tool includes routines for the optimization of
transmitter pre-emphasis and equalization. This platform
was created using MATLAB, qualified against Agilent’s
ADS SI suite, and correlated with measurements. This
paper also describes the practical uses of such a simulator
developed at Altera to predict link performance over
backplanes.
I. INTRODUCTION
The challenge in today’s quest to minimize BER at everincreasing data rates has led to SERDES transceivers that
have vastly grown in complexity. Such sophisticated
transceivers now have a large number of pre-emphasis and/or
equalization settings available. Optimizing these settings to
obtain the best transceiver performance is necessary to
maximize link performance for any given customer link or
backplane situation. Doing so, without aid, can be time
consuming and difficult. What is required is a tool that
accurately takes into account the customer’s link or backplane
properties and contains models of the transceiver circuit
behavior. Knowing the available transmitter and/or equalizer
settings, the tool optimizes those settings to offer the best
combination for that given link while predicting the link
performance.
One example of transceiver modeling based on MATLAB, is
“StatEye”, a new methodology released recently from the
Optical Internetworking Forum (OIF). It is used to support
compliance testing of differential backplane channels. StatEye
includes parameterized models for transmitter, receiver and
the passive channel but doesn’t support the incorporation of
silicon device models necessary to yield the most accurate
correlation between simulation and the actual implemented
serial link. Our effort addresses this.
This paper first presents a behavioral modeling
methodology of high-speed transceiver with pre-emphasis and
equalization, which reflects the actual behavior of silicon
devices. Optimization of FIR filter-based pre-emphasis will
be discussed using LMS criterion and followed by full link
0-7803-9023-7/05/$20.00 ©2005 IEEE.
performance optimization with combined pre-emphasis and
equalization techniques. This paper finally describes the
practical uses of such a simulator that was developed at Altera
to predict link performance in backplanes. Transmitter output
amplitudes, tap numbers, pre-emphasis levels and/or equalizer
settings are hard-coded in the simulator for each product. An
optimizer is built into the simulator so automatically the best
settings would be offered. This simulator platform accepts the
full backplane or link represented by its S-parameters.
Furthermore, this platform offers both NEXT and FEXT
simulation capabilities to simulate X-talk effects on link
performance.
II. MATLAB BEHAVIORAL MODELING
Figure 1 shows the configuration of a backplane transceiver
application with FIR pre-emphasis and analog equalization.
The backplane channel typically consists of a transmitter
daughter card, a backplane, a receiver daughter card and
connectors. Pre-emphasis and/or equalization are used to
counteract ISI caused by PCB traces, connectors, chip
packages, and so on. Since the backplane channel has already
been represented by S-parameters, only transceiver devices
need now be modeled to complete the system. This latter
point is key since on-chip parasitic and package effects
largely influence the behavioral model and need to be
included for accurate results.
Near-end
Data In
FIR
Far-end
RX
TX
EQ
Data Out
Backplane
Figure 1 Transceiver for backplane application
A. Tx Pre-emphasis Behavioral Modeling
Transmitter pre-emphasis is a widely used technique by
many of the high-speed transceiver vendors including Altera.
However, obtaining the optimum pre-emphasis level at the
transmitter side is needed in order to obtain the best
performance at the far-end. The process of creating the
transmitter’s behavioral models is made easier by utilizing
MATLAB’s
matrix
manipulation
routines.
These
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725
computations take place in both the frequency and time
domain.
Amplitude & Datarate Input
Effective Tapcoefficients & Tap
Options Input
Multi-poles & zeros
Filter Parameters
Input
PRBS Pattern
Generator
FIR Filter Based
Multi-Taps Preemphasis
Transistor-Level
Behavioral Model
Creation
Package-to-Board
Interconnect and
Board Model
Integration
Parasitic and
Package Model
Integration
Package model integration: The behavioral model includes
the effects of the package and an operational link mode (e.g.
AC- or DC-coupled options). The package model can be
imported into the simulation platform as simulated or
measured S-parameter data. The behavioral model could
include package-to-board interconnection, board and
connectors.
B. Behavioral Model Correlation
Figure 2 Transmitter behavioral modeling flow chart
Figure 2 shows the flow chart that graphically describes the
creation of these models. The function of each block is
described as follows:
PRBS Pattern Generator: Pseudo-random bit sequence
(PRBS) data patterns can be of arbitrary bit width and the
waveform can then be generated for any specified data-rate
and amplitude.
The best way to validate behavioral models is to correlate it
with both extracted transistor-level simulations as well as
measurements. For the transmitter, this is best accomplished
at the near end.
Two examples of Altera transmitters at both 3.125Gb/s and
6.5Gb/s data rates are provided by observing the eye diagrams
seen in Figure 4. Here, even without accounting for some
secondary effects in the model yet, there is good correlation
with measurement.
FIR Pre-emphasis: Transmitter pre-emphasis is generated
using a discrete Z-domain FIR filter. For accuracy, the model
created uses tap-coefficients extracted from either transistorlevel simulations or measurement data. This approach allowed
a straightforward implementation of multi-tap types and
locations.
(3.125Gb/s)
(3.125Gb/s)
(6.5Gb/s)
(6.5Gb/s)
Specifically, the structure of the FIR filter is shown in Figure
3. For symbol-spaced FIR, the delay D is equal to one symbol
period, and the Z transform is given by
N 1
H z ¦C
n ˜z
n
(1)
(a)
n 0
where C n are the tap coefficients,
sampling frequency f s 1 / T .
xn
D
C0
z
exp j 2Sf / f s , and the
D
D
C2
C1
-
C N-2
(b)
Figure 4 Tx output eye-diagram correlation: (a) measurement;
(b) simulation from behavioral model
C. Rx Equalizer Modeling
Equalization techniques are commonly used to compensate
for ISI in addition to Tx pre-emphasis. As shown in Figure 5,
analog equalizers generally have programmable gain, zero
and pole locations
C N-1
yn
Figure 3 FIR filter structure
Transistor behavioral modeling: A multi-pole and multizero filter is employed to model the transistor switching
behavior, parasitic and output loading conditions. Taking for
example the transmitter, the actual amplitude seen at the
output experiences degradation from the ideal case. This is
accounted for by non-idealities that include headroom
limitations, transistor charging-sharing effects as well as
parasitics from routing on the silicon, ESD and bump pads.
These effects need to be accounted for in the behavioral
model.
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Figure 5 Frequency response example of analog equalizer
Once again, the behavioral model (now for the equalizer)
can be based on either an analytic formula derived from the
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equivalent circuit (not shown) or from extracted transistorlevel simulations of the actual equalizer.
III. LINK SIMULATION PLATFORM
The behavioral models created for the transmitter and
equalizer are now added into our signal integrity-based
simulation framework that predicts link performance. Once
again, the results of the entire system are qualified against
Agilent’s ADS signal-integrity suite (or Hspice) and then
correlated with measurements.
A. Tx Pre-emphasis Optimization
As part of this development, the ability to optimize preemphasis was added. The tap coefficients are variable,
quantized to the available settings in the product according to
the optimization process outlined in Figure 6. At the end of
this process, time-domain simulations are performed with the
resulting eye-diagrams displayed.
PRBS
Data Pattern
Convolution
Delay
Tap
Coefficients
LMS Algorithm
Tx Behavioral
Model
FFT
S-Parameter
input
Channel Frequency
Response
Parameter
Input
Rx Equalizer
Behavioral Model
IFFT
IFFT
Equalizer
Setting
Figure 7 Rx equalizer optimization flow chart
C. Full Link Simulation Platform
Now, given the methodology employed for obtaining
accurate representation for the transmitter and equalizer,
behavioral models are added into link simulation platform.
This is graphically shown in Figure 8. Given a backplane as
represented by S-parameters, this simulation platform
provides various options to minimize the effects from ISI
through different signaling techniques, optimization
algorithms and/or manually setting those parameters available
to the customer.
¦
Pre-emphasis
(LMS Optimization)
S-Parameter
input
Optimization
Algorithm
Specifically, once the optimal tap coefficients and
equalization settings are obtained, the eye diagrams can be
plotted at specified locations and parameters associated with
the eye-diagram (e.g. eye opening width and height) can be
extracted.
Parameter
Input
Tx Behavioral
Model
PRBS
Data Pattern
Impulse
Response
Backplane Channel
(S-paremeter)
Rx Behavioral
Model
Pre-emphasis
(Manually Setting)
PRBS
Data Pattern
Figure 6 Tx pre-emphasis optimization flow chart.
Specifically, the channel impulse response is first obtained
by applying the Inverse Fast Fourier Transform (IFFT) to the
input S- parameters. Then training data as produced at the
output from the Tx behavioral model is convolved with the
impulse response to get the distorted data at the far-end of
channel. The distorted data and the error signal, as defined by
the difference between the delayed training data and FIR filter
output, go into the least-mean-square (LMS) convergence
engine. The LMS algorithm is given by
C n 1 C n P ˜ u ˜ e
Equalization
(Optimization)
Tx Behavioral
Model
Eye-diagram
Plot
Equalization
(Manually Setting)
Near-end
Eye-diagram Plot
Far-end
Eye-diagram Plot
Figure 8 Block diagram of a full link simulation platform
ADS Simulation
Link Simulator
EQ setting #4
(2)
Where C is the tap coefficient, P is the step size, u is the
distorted signal, and e is the error signal. The convergence of
error drives the tap coefficients to their optimal values.
EQ setting #5
Link Tool automatically finds the
right setting #5 for Rx equalizer
at this backplane
B. Rx Equalization Optimization
The program was also developed to optimize equalizer
performance and provide the best setting for a given
backplane. As shown in Figure 7, the Tx output data pattern is
first converted to the frequency domain, then multiplied by
both the channel frequency response and the Rx equalization
model represented by it’s transfer function. The product of the
above three parts is converted back to time domain through an
IFFT process. Finally an optimization algorithm is repeatedly
applied to this process to seek for an optimal equalizer setting.
EQ setting #6
Figure 9 One qualification example of link simulation platform
against ADS tool
The optimization approaches used here can be qualified by
simply comparing transistor-level ADS simulation results for
tap settings surrounding the optimized result. One example
has been given in Figure 9, which is intended to find the
optimal setting of the analog equalizer applied through
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727
receiving data across 34” FR4 backplane by using this tool.
Here, the tool automatically found the best setting for the
equalizer for this particular backplane.
11, excellent correlation is achieved which validates the
accuracy of this simulator.
Measurement
MATLAB Link Simulator
IV. APPLICATIONS AND DISCUSION
No pre-emphasis
No pre-emphasis
Input S-parameter file name
Network format
Frequency unit
Data format
Optimization mode selection (0 ~3)
If '0' is selected
0.
1.
2.
3.
Tx manually but Rx auto
Tx auto but Rx manually
Both Tx & Rx auto
Both Tx & Rx manually
Max. pre-emphasis
Max. pre-emphasis
Products selection (SGX/SGXII)
If 'SGX' is selected
Plot channel response
Vod input (400 ~1400) mVpp
Pre-emphasis setting input
Figure 11 Far-end eye-diagrams correlation between the link
simulator and measurement (3.125Gb/s data across 34” FR4
backplane)
Specify data rate
(<=3.25Gb/s)
A second application of this platform is instrumental in
assessing the types and levels of signaling required in nextgeneration transceiver products. This is afforded by allowing
a version of this platform to be arbitrary in its transmitter and
equalizer’s abilities. For example, one may add an arbitrary
number of pre- and post-taps of any value to the list of
available transmitter characteristics.
Search optimal EQ setting
Plot eye-diagrams
Parameter output
Figure 10 Link simulation platform user interface example
The settable behavioral models for supported transceiver
products are hard-coded in each release of this tool. To make
the tool easy to use, a user interface is added. As shown in the
flowchart of Figure 10, the user is requested to input the
backplane data file that contains it’s S-parameters and then
choose the type of Altera transceiver product and one of fouroptimization modes. Available choices at this time include
1)
2)
3)
4)
Tx is manually set, with Rx automatically optimized
Rx is manually set, with Tx automatically optimized
Both Tx and Rx are automatically optimized and
Both Tx and Rx are manually set.
Depending on the selection of optimization mode, as in the
example shown in Figure 10, users need to specify the
transmitter output amplitude, data rate, and pre-emphasis
setting. The simulator then automatically searches for the
optimal equalization setting for this particular backplane. The
simulator will plot the S-parameter characteristics of the link
as a function of frequency followed by the impulse response
with and without transmitter pre-emphasis. All eye-diagrams
are shown with and without pre-emphasis and/or equalization.
Open eye measurements are generated as accompanying text.
The correlation between simulation and measurements is
accomplished through a suite of backplanes available to us.
Three backplanes were selected to represent the worst-,
typical- and best-case customer scenarios. As seen in Figure
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Since the end goal of any system is to hit a given BER, it is
necessary for this tool to incorporate total jitter. Clearly, a link
simulator based on the above only accounts for DJ
components. The RJ component is added from either
measurement or from a system-level designer’s perspective
such as an upper bound that can’t be exceeded. Armed with
the DJ simulated through the link, and the RJ supplied, the
BER becomes predictable. Furthermore, since this platform
offers both NEXT and FEXT simulation capabilities, the user
would enter cross-talk data in multi-port S-parameters format
to simulate crosstalk effects on link performance.
ACKNOWLEDGEMENT
Authors would like to thank Dr. Shoujun Wang at Altera
Corp., and Mr. Miao Li at Carleton University for their
contribution on this work.
REFERENCES
[1] M. Li, Y. Tao, S. Wang and T. Kwasniewski, “Studies on
FIR Filter Pre-emphasis for High-Speed Backplane Data
Transmission”, Proceedings of GSPx, Sept 27~30, 2004
[2] D. E. Bockelman and W. R. Eisenstadt, “Combined
differential and common-mode scattering parameters: theory
and simulation,” IEEE Trans. Microwave Theory Tech., vol.
43, pp.1530–1539, July 1995.
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