The present invention relates to timing measurements, and more particularly to transport delay and jitter measurements in clock driven systems.
In clock driven systems a reference clock propagates through different paths, with the layouts of the paths introducing different delays for the reference clock. Certain circuits along the paths, such as clock data recovery (CDR) circuits, introduce additional delays. High speed chipsets used for transmitting data at multiple gigabits per second present numerous signal integrity challenges. Although a high speed backplane may seem like just a printed circuit (PC) board, it really may be viewed as a communications system. The reference clock that propagates to different parts of the backplane experiences different transport delays. System designs need to take into account the transport delays to make system performance meet requirements, such as limits to jitter.
For high speed serial data standards, such as the Peripheral Component Interface (PCI) Express and Fully Buffered Dual In-line Memory Module (FB-DIMM) standards, the physical layer may be described as shown in
What is desired is a direct method for measuring transport delays and jitter on a high speed printed circuit board.
Accordingly the present invention provides a method of measuring transport delay and jitter with a realtime oscilloscope using cross-correlation. Waveforms are acquired from two test points in a system under test. Clock recovery is run on both waveforms to obtain respective rates and offsets. A time offset between the two waveforms is computed from the respective offsets. The jitter from the two test points is filtered and a mean-removed cross-correlation coefficient is computed from the filtered jitters. A fractional delay is computed using interpolation based on LMS error, and the respective computational components are summed to compute a transport delay between the two test points. The transport delay may then be used to provide an “idealized” reference clock from one of the signals for edge comparison with the other signal in order to determine jitter.
The objects, advantages and other novel features of the present invention are apparent from the following detailed description when read in conjunction with the appended claims and attached drawing.
Referring again to
The procedure of measuring transport delay and jitter is shown in
The cross-correlation based transport delay method described above may be applied to measure transport delay or difference of delays between any two points in the system. For example the difference of transport delays between “tp.4” and “tp.5” may be directly measured. The signals at the two points may be either clock or data signals, i.e., from “tp.1” to “tp.5” is from clock to clock and from “tp.3” to “tp.4” is from data to data. To get the measurement results with high accuracy the signal-to-noise ratio (SNR) should be high enough, which is true in this example since the low frequency jitter component is the signal as is often the case in high speed data transmission systems. For example spread spectrum clocking (SSC) is widely used in reference clocks and has large low frequency jitter.
In a clock data recovery (CDR) system that has both clock and data as inputs, such as the CDR 26 in the receiver 16 which has data and a reference clock output from the PLL 28 as inputs, the PLL is a clock multiplier and has a characteristic of low passing the jitter from the input reference clock. The CDR 26 figures out the optimal delay between its recovered clock and data, which determines the data buffer size. The cross-correlation procedure described for transport delay computation may be applied to find the optimal delay between the data and recovered clock.
After the optimal delay is obtained as shown in
As an example the clock and data waveforms are shown in
T—CCR_Offset=−24.416 (UI)
T—XCOR_Delay=28 (UI)
T_INTERP_Delay=−0.0007 (UI)
Td=3.5769 (UI) or 1.4308 (ns)
The data rate is 25 times the reference clock rate in this PCI-Express system and the first intermediate result, T_CCR_Offset, is about −24 UI, which means that the first recovered data edge is about 24 UI ahead of the first recovered clock edge. This is consistent with the observation from
The second intermediate result, T_XCORR_Delay, is obtained from the cross-correlation curve shown in
In Step 6 the cross-correlation between two signals is computed. When assuming the two signals, x(n) and y(n), n=1, 2, . . . , N, are correlated, there is an optimal integer delay, k, such that the signal x(n) and delayed signal y(n+k) have minimum difference. The good criterion of difference is squared difference or error. Since N is a constant number, minimizing on mean squared error is the same as minimizing squared error. So the solution of delay for LMS error is the same as the solution for least square error here.
where r(k) is the cross correlation coefficient, ∥x∥ and ∥y∥ are 2-norm of x(n) and y(n), n=1, 2, . . . , N. When x(n) and y(n) are normalized, ∥x∥ and ∥y∥ are both equal to one. Since ∥x∥ and ∥y∥ are constant, a particular k that maximizes the cross correlation coefficient also minimizes the squared error in the mean time.
Step 7 computes the fractional delay using interpolation, as illustrated in
z(n+p)=(1−p)*z(n)+p*z(n+1)
The good optimization criterion is squared error or, equivalently, mean squared error when N is constant.
minpJ(p)=minp{Σn(x(n)−z(n+p))2}=minp{Σn(x(n)−z(n)−(z(n+1)−z(n))*p)2}
The solution to this optimization problem is obtained by taking a partial derivative on variable p:
p={Σn(x(n)−z(n))*(z(n+1)−z(n))}/{Σn(z(n+1)−z(n))2}
Thus the present invention provides a method of transport delay and jitter measurements effectively using realtime oscilloscopes that is based on cross-correlation.
Number | Name | Date | Kind |
---|---|---|---|
5309428 | Copley et al. | May 1994 | A |
6931335 | Mueller | Aug 2005 | B2 |
6934648 | Hanai et al. | Aug 2005 | B2 |
7149638 | Stephens | Dec 2006 | B2 |
7206368 | Engel et al. | Apr 2007 | B2 |
7218670 | Lesea et al. | May 2007 | B1 |
7236555 | Brewer | Jun 2007 | B2 |
7251764 | Bonneau et al. | Jul 2007 | B2 |
7305025 | Yamaguchi et al. | Dec 2007 | B2 |
7493223 | Kayal et al. | Feb 2009 | B2 |
7516030 | Miller | Apr 2009 | B2 |
20020097761 | Sucha et al. | Jul 2002 | A1 |
20020118738 | Whitlock | Aug 2002 | A1 |
20020163958 | Yamaguchi et al. | Nov 2002 | A1 |
20020176491 | Kleck et al. | Nov 2002 | A1 |
20030125888 | Yamaguchi et al. | Jul 2003 | A1 |
20030202573 | Yamaguchi et al. | Oct 2003 | A1 |
20040061488 | Rosenbaum et al. | Apr 2004 | A1 |
20040062301 | Yamaguchi et al. | Apr 2004 | A1 |
20040125873 | Han | Jul 2004 | A1 |
20050003785 | Jackson et al. | Jan 2005 | A1 |
20050031029 | Yamaguchi et al. | Feb 2005 | A1 |
20050163204 | Brewer | Jul 2005 | A1 |
20060013263 | Fellman | Jan 2006 | A1 |
20060018374 | Nelson et al. | Jan 2006 | A1 |
20060251162 | Yamaguchi et al. | Nov 2006 | A1 |
20070058708 | Bultan et al. | Mar 2007 | A1 |
20070189372 | Slaboda | Aug 2007 | A1 |
20070268963 | Wallace et al. | Nov 2007 | A1 |
20070271049 | Carole et al. | Nov 2007 | A1 |
Number | Date | Country | |
---|---|---|---|
20080080605 A1 | Apr 2008 | US |