振铃现象汇总.doc

找个数字电路，接上电源让它跑起来，然后用示波器去看看有规则波形的信号。把示波器的采样率调到足够高，并利用沿触发模式捕捉波形，你能观察到波形在沿(不管是上升还是下降)之后有振幅很快衰减的高频振荡，那就是数字电路永远甩不掉的“振铃”。振铃和过冲什么是过冲（overshoot）？过冲（Overshoot）就是第一个峰值或谷值超过设定电压对于上升沿是指最高电压而对于下降沿是指最低电压。下冲（Undershoot）是指下一个谷值或峰值。过分的过冲（overshoot）能够引起保护二级管工作，导致过早地失效。 什么是下冲（undershoot）（ringback）？过冲（Overshoot）是第二个峰值或谷值超过设定电压对于上升沿过度地谷值或对于下降沿太大地峰值。过分地下冲（undershoot）能够引起假的时钟或数据错误（误操作）。什么是振荡（ringing）？振荡（ringing）就是在反复出现过冲（overshoots）和下冲（undershoots）。信号的振铃（ringing）和环绕振荡（rounding）由线上过度的电感和电容引起，振铃属于欠阻尼状态而环绕振荡属于过阻尼状态。信号完整性问题通常发生在周期信号中，如时钟等，振荡和环绕振荡同反射一样也是由多种因素引起的，振荡可以通过适当的端接予以减小，但是不可能完全消除。一般指LC回路的自由衰减振荡。如在开关电源中，变压器漏感与开关管（或整流二极管）结电容就会产生振铃。例如某个频率信号，上升沿的顶峰超过平均高电平很多就是过冲，下降沿的顶峰超过平均低电平很活就是负冲，上升或下降产生波浪就叫振铃这类现像多数与电路中分布参数有关，例如电路板上两线之间的分布电容，导线自身的电感，芯片输入和输出端对地的电容，等等，很难完全避免。在含电感的电路中更有电感自身的分布电容、变压器漏感等等。频率较高时还需要考虑传输线的反射。每个电路，电原理图可能完全相同，但实际制作时元器件布局不同，电路板布线不同，这种振铃和过冲也不同，没有具体布局布线，很难分析。通常是在可能发生分布电容与电感产生振铃的地方使用电阻来抑制，电路板布线时考虑线的走向，长线考虑阻抗的匹配以减少反射，电源与地之间尽量多布置旁路电容，等等。1/ 用74AC04/74AHC04替代74HC042/ 增加地线面积3/ 用钽电容+0.01u代替电解电容试试看.阻尼振荡，以前骑老式自行车的时候，碰到前面有人大老远就按铃，然后铃声逐渐变小。在这里把声波换成电波就是振铃效应！Leakage InductanceThis section examines leakage inductance, describing what it is, what it does, why it needs to be minimised and how to reduce its effects. What Is It?Leakage inductance is a parasitic component of transformer design caused by poor coupling between windings. This leads to small amouns of magnetic flux leaking out and not being transferred to other windings. It appears electrically as a separate inductor in series with the windings and causes problems.Primarily, in SSTC applications, we are concerned with the amount of ringing caused by the leakage inductance resonating with the gate input capacitance of the switching device (this is illustrated in a series of SPICE simulations below). With ringing, there are a few main concerns:1 The ringing overshoot on waveform edges can be enough to breakdown the gate insulation of MOSFET devices. Most MOSFETs are specified to +/- 15V of gate drive voltage (relative to the source) with some having +/- 20V or higher. Back-to-back 15V Zener diodes are seen in many designs to act as protection in the case of excessive ringing to prevent the gate being damaged. With good GDT design, these should not be necessary. The STE70NM60s I use in my Stubby SSTC have a rating of +/- 30V with internal back-to-back Zener diodes to clamp the voltage. This seems to be a common feature in ST power MOSFETs and one that is appreciated. 2 Excessive leakage inductance, combined with insufficient damping resistances can distort the waveform beyond all recognition and in this case (opposite) the ringing is sufficient to change the operating state of the MOSFET between on, linear and off modes. 3 The high frequency ringing could couple into other circuits and affect performance, cause false triggering, etc. 4 Some of the ringing could be reflected back to the GDT primary driver, causing damage to the device.This needs to be avoided as the intended input signal is totally overridden by the ringing, making the waveform useless, as shown in the waveform on the right. In fact, red waveform, if appearing on a MOSFET gate, wold turn the device on and off multiple times during one switching period.Leakage inductance also causes a time delay in the signal which can be seen in the above waveform as the phase difference between the input waveform (green) and the output waveform (red). This is because the voltage across an inductor cannot change instantaneously - the larger the leakage inductance, the larger the time delay. This is examined below in relation to the possibility of it causing shoot through conditions. Trace InductanceTrace inductance is the parasitic inductance of the traces connecting the driver to the GDT and the GDT to the MOSFET gate-source terminals. It has a similar effect to leakage inductance in that is causes ringing and time delays. This can be minimised by using short, wide tracks as connections between driver, GDT and MOSFET and also by keeping the current loop area small.Ribbon cable, with alternate + and - wires also has quite a low inductance, as does tightly twisted wires. Double sided PCB, with the gate trace on the top layer and the return on the bottom will also have a low parasitic inductance. Keep those loops small! SPICE SimulationsSome basic simulations were performed using SIMetrix Spice v4.2 (Demo) to get a feel for what effects leakage inductance can have and what can be done from a circuit point of view to reduce it.A pulse voltage source was used to simulate the drive signal, with a 1 ohm resistor to simulate source and any impedance in the tracks caused by resistance of the copper or an inserted resistor. The 5nF capacitor (set to an initial voltage of 0V) represents the input capacitance of a power FET (we are assuming it is linear for this example). 100ns rise / fall times are used for these simulations. With No Series InductanceThe most important thing to not here is that there is no ringing on the top (overshoot) or the bottom (undershoot) of the waveform. This is an ideal situation. With Some Series InductanceSo now we introduce leakage inductance, simulated by a series inductor in the circuit. The value of 100nH represents a reasonably good transformer design. This inductance causes some ringing on the top and bottom of the waveform and causes a very small degree of phase delay. The ringing is not enough to breakdown the gate of an average power MOSFET and also not enough to turn it back on again on the first ring after the falling edge. With Larger Series InductanceAnd this is what happens when the inductance increases by a factor of 10 to 1uH. The ringing (red) destroys the original waveform (green) and will cause multiple on/off transitions of the driven device. This is of no use in our intended application. Ive seen Coilcraft GDTs do this when driven hard, as they have a specified maxiximum leakage inductance of 4uH. With A Series Damping ResistorHowever, this ringing can be easily reduced (but not removed) by increasing the series damping resistor value - to 10 ohms in this case. This reduces the Q of the tuned circuit formed by the leakage inductance and the input capacitance, reducing the ringing amplitude and frequency.However, the ringing peaks are still signficant and, more importantly, the rise time on the gate has been slowed. This means the time spent in the linear region of switching is increased, resulting in more devie heating. Switching SpeedsUsing the circuit with the 100nH leakage inductance above, the input rise and fall times were varied to judge the effect on the ringing.Above: 25ns rise time, 20V peak ringAbove: 50ns rise time, 18V peak ringAbove: 100ns rise time, 15V peak ringThese measurements show how a fast slew rate injects more energy into the tuned circuit, causing larger amounts of ringing. The peak values for each of these circuits can be seen to reduce in value the slower the input slew rate gets from 8V overshoot at 25ns rise to about 2.75V overshoot at 100ns.So, for fast switching we need low leakage inductance to reduce ringing and reduce the risk of damage to our switching devices and to ensure correct operation of our half bridge.Conclusion: There is no substitute for a decent transformer design in the first place!Filters (Frequency) Frequency filters process an image in the frequency domain. The image is first, Fourier transformed, then multiplied with the filter function, and then transformed back to the spatial domain. Attenuating high frequencies results in a smoother image in the spatial domain, attenuating low frequencies enhances the edges. Background Frequency filtering is based on the Fourier Transform. The operator usually takes an image and a filter function in the Fourier domain. This image is then multiplied with the filter function in a pixel-by-pixel fashion: Equation 1G(k,l)=F(k,l)*H(k,l) Where F(k,l) is the input image in the Fourier domain, H(k,l) is the filter function and G(k,l) is the result filtered image. The form of the filter function determines the effects of the operator. There are basically four different kinds of filters: lowpass, highpass, bandpass and bandstop filters. A low-pass filter attenuates high frequencies and retains low frequencies unchanged. The result in the spatial domain is equivalent to that of a smoothing filter; as the blocked high frequencies correspond to sharp intensity changes, i.e. to the fine-scale details and noise in the spatial domain image. A highpass filter yields edge enhancement or edge detection in the spatial domain, because edges contain mostly high frequencies. In other hand, areas of rather constant gray level consist of mainly low frequencies and are, therefore, suppressed. A bandpass filter attenuates very low and very high frequencies, but retains a middle range band of frequencies. Bandpass filtering is used to enhance edges (suppressing low frequencies) while reducing the noise at the same time (attenuating high frequencies). The most simple lowpass filter is the ideal lowpass. It suppresses all frequencies higher than the cut-off frequency F0 and leaves smaller frequencies unchanged, which can be described as: Equation 2H(k,l)=1 if (k2+l2)1/2 F0 Where, H(k,l) is the filter function. In most implementations, F0 is given as a fraction of the highest frequency represented in the Fourier domain image. The drawback of this filter function is a ringing effect that occurs along the edges of the filtered spatial domain image. See Figure 1. Figure 1. The ideal lowpass filter in frequency and spatial domains Better results can be achieved with a Gaussian shaped filter function. Equation 4H(k.l)=exp(-F2(k,l)/2Fo2) Where, H(k,l) is the Gaussian lowpass filter function, and F0 is the cut-off frequency. The advantage is that the Gaussian has the same shape in the spatial and Fourier domains and therefore does not incur the ringing effect in the spatial domain of the filtered image. See also Volume 2 Algorithms, Gaussian filter. See Figure 2. Figure 2. Gaussian low pass filter: (a) a plot of Gaussian function, (b) the inverse Fourier transform of Gaussian, (c) Frequency response of Gaussian with different F0 A commonly used discrete approximation to the Gaussian is the Butterworth filter. Applying this filter in the frequency domain shows a similar result to the Gaussian smoothing in the spatial domain. One difference is that the computational cost of the spatial filter increases with the size of the filter kernel, whereas the costs for a frequency filter are independent on the filter function. Hence, the spatial Gaussian filter is more appropriate for narrow lowpass filtering, while the Butterworth filter is a better implementation for wide lowpass filtering. See also Gaussian and Butterworth filters . The same principles apply to highpass filters. A highpass filter function can be obtained by inverting the corresponding lowpass filter, e.g. an ideal highpass filter blocks all frequencies smaller than F0 and leaves the others unchanged. A bandpass filter is a combination of both lowpass and highpass filters. It attenuates all frequencies smaller than a frequency Fmin and higher than a frequency Fmax, while the frequencies between the two cut-offs remain in the resulting output image. A bandstop filter is a filter that passes most frequencies unaltered, but attenuates those in a specific range to very low levels. .i.Butterworth filters:2D;.i.Butterworth filters:3D;.i.Butterworth filters:high-pass;.i.Butterworth filters:low-pass;.i.Butterworth filters:band-stop;.i.Butterworth filters:band-stop;Windowed Finite Impulse Response Window functions are time limited. This means there is always a finite integer Nw such that w(n) almost equal to 0 for all |n| Nw. The final windowed impulse response is thus always time-limited, as needed for practical implementation. This window method designs a finite-impulse-response (FIR) digital filter based on the Hamming function. Hamming function can be described as Equation 5w(n)=0.53836-0.46164*cos(2Pi*n/N-1) The method consists of simply windowing a theoretically ideal filter impulse response h(n) by some suitably chosen window function w(n), yielding the following equation: Equation 6hw(n)=w(n)*h(n), where n belongs to Z For example, the impulse response of the ideal lowpass filter is the sinc function. Equation 7h(n)=B*sinc(Bn) almost equal to B*(sin(Pi*Bn)/Pi*Bn), where n belongs to Z where B=2fc is the normalized bandwidth of the lowpass filter and fc denotes the cut-off frequency. Since h(n)=sinc(BnT) decays away from time 0 as 1/n, the method truncates it to the interval -N, N for some sufficiently large N defined by the user and as a result it obtains a finite response filter that approximates the ideal filter. As for Hamming function, |N| 3 is sufficient for good approximation. Outline of the method The Filters Frequency method processes images by filtering in the frequency domain in three steps: 1 It performs a forward Fast Fourier Transform (FFT) to convert a spatial image into its frequency image. 2 It enhances some frequency components of the image and attenuates other frequency components of the image by using a lowpass, highpass, bandpass, or bandstop filter. Frequency filters may be constructed with 1 of 3 methods: Hamming windows, Gaussian filters, and Butterworth filters. However, for the Gaussian filters only lowpass and highpass filters are available. 3 It performs an inverse FFT to convert from the frequency domain back into the spatial domain. This method performs all three steps in a single combined process. Note: Since the core algorithm of this module is the FFT, it requires that all the dimensions of an N-dimensional dataset were powers of two. To be able to use this algorithm on datasets with arbitrary dimensions, the data is zero padded to powers of two before applying the forward FFT and stripped down to the original dimensions after applying the inverse FFT. The powers of two are not kept identical because symmetrical Fourier pictures are not required. The module also creates a Gabor filter, which is essentially a tilted Gaussian with two unequal axes at an offset (freqU, freqV) from the origin. A Gabor filter only responds to a texture having both a particular frequency and a particular orientation. Note that a filter and its mirror image reflected across the u and v frequency axes produce identical frequency responses. See also Volume 2 Algorithms, Gabor Filter. The Frequency Filter algorithm also used in homomorphic filtering, for more information refer to Volume 2 Algorithms, Homomorphic Filter. Figure 3. Applying the Frequency Filter algorithm with different parameters References Digital Image Processing Second Edition by Rafael C. Gonzalez and Richard E. Woods, Prentice-Hall, Inc., 2002, Chapter 4.5, pp. 191-194. Image types 2D and 3D grayscale images. Applying the Frequency Filter algorithm To run this algorithm, complete the following steps: 1 Open an image of interest. 1 Select Algorithms Filters Frequency. 1 The Frequency Filter dialog box appears. 1 Fill out the dialog box. 1 Specify the destination image frame, and then, press OK. Depending on whether you selected the New Image or Replace Image option, the result appears in a new window or replaces the original image. The Bjack Desulfation Home PageThis is the PSpice model scheme for the original Alastair Couper lead acid battery low power desulfator, a simple but greatly performing circuit: it allows for heavily sulfated batteries reclamation.For more details on the original Alastair Couper desulfator, please visit the Low Power Desulfator Info Home Page (there is a power version too): youll find all sorts of schematics, tips and tricks to build your homebrew desulfator.My PSpice model for this circuit is freely downloadable: try it, modify it, check it with PSpice simulator.You can see the PSpice model at top of this page: node numbers are also reported (in red colour) for easy printout readings.But. why simulation? This is a simple circuit, derived from well known DC-DC converters, and it behaves exactly as described in Low Power Desulfator Info Home Page.Well. IMHO, simulating a circuit may be the best manner to go deeply inside it, this because: Some voltages and currents may be improperly measured (scopes or meters disturbing normal circuit operation, erroneous measurement techniques giving wrong results etc.). You may think to make some experimentations with the real circuit, but this may toast expensive devices; making changes in a model wont harm the PC, but will show the results (of course, you have to decide if that current flowing through that device will damage it or not. to decide, simply take a look at the device datasheet, and youre done). You may try to improve circuit performance, substituting devices, timing, output waveforms and so on. but some circuits (AC desulfator being a perfect example) are operated with allfruit power supplies and loads to check for performance.How will you judge if your modified circuit is performing better than the unmodified one? For AC pulsers, youll use sulfated batteries to make tests, and damaged battery electrical parameters are strongly variable. Youll measure desulfation process speed, and this is ALWAYS the conclusive test, but. how can you find two identical batteries, having identical sulfation degree? Not that simple!Using the model, you set a g