数模摸数转换-科技英语.ppt

单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,Analog to Digital Converters,An analog-to-digital converter,or ADC as it is more commonly called,is a,device,that converts,analog,signals into,digital,signals.,Analog information is transmitted by modulating a continuous transmission signal by amplifying a signals strength or varying its frequency to add or take away data.,Digital information describes any system based on discontinuous data or events.,Computers,which handle data in digital form,require analog-to-digital converters to turn signals from analog to digital before it can be read.,One example is a,modem,which turns signals from digital to analog before transmitting those signals over communication lines such as telephone lines that carry only analog signals.,The signals are turned back into digital form(,demodulated,)at the receiving end so that the computer can process the,data,in its digital format.,ADCs come in almost as many flavors as ice creams,and at least as much care is needed in choosing the,former as is required with the latter.,A popular and readily understood type of ADC is,the Flash ADC(Figure 2.5).,This is capable of very high-speed conversion and thus can accommodate high sampling rates,but in its basic form is very power hungry,.,The flash converter operates by simultaneously presenting the input signal to a bank of,comparators,whose,reference voltages are set by a resistor chain to exactly correspond to all of the possible sample levels,which can be represented by the converter,.,.,The output from each comparator(either a 1 or a 0),is then encoded into,an N-bit word representing the input sample level.,This approach is the most simple,most intuitive and also the fastest solution for ADC implementation.,For large numbers of bits(e.g.14 bits),the number of resistors needed(),becomes prohibitively large for most practical applications,.,Also,the power consumption is considerably higher than some of the slightly slower and more exotic solutions.,The output from each comparator(either a 1 or a 0)is then encoded into an N-bit word representing the input sample level.,This approach is the most simple,most intuitive and also the fastest solution for ADC implementation.For large numbers of bits(e.g.14 bits),the number of resistors needed(,)becomes prohibitively large for most practical applications.,Also,the power consumption is considerably higher than some of the slightly slower and more exotic solutions.,Figure 2.6 shows the output spectrum of a typical flash converter as determined by taking an FFT(Fast Fourier Transform)of the converter output samples for a pure sine wave input.,One thing is immediately apparent.,The spectrum does not simply consist of the pure input sine wave component,but also has a mass of other components,spread throughout,the measurement band.,These largely,arise from,the inevitable quantization error(noise),because the converter is trying to represent the analog input level from a finite number of available sample values(dictated by the number of bits in the ADC).,The converter resolution for the ADC generating the plot in Figure 2.6 is 12 bits,giving a theoretical SNR of 74 dB.,Looking at,the plot,the difference in levels between the sine wave component and the individual noise components,is much nearer 104 dB.,The reason for the difference between these two values is that the SNR formula,refers to,the whole noise contribution,comprising the sum of all the individual noise components,making up,the FFT.,The reason for pointing out this feature is it can be used to increase the effective resolution of a converter by,trading off,sampling rate as shown below.,Assume that we need to achieve a minimum 70 dB SNR in the conversion process for a given audio application.,The formula above suggests a minimum of 12-bit converter resolution is required(full scale sine wave input and ideal converter),where the noise within the whole band from 0 Hz to fs/2 is included in the measurement.,Now,if we employ for example 8 X bandwidth sampling(Figure 2.7),we see that the actual audio signal only occupies 1/4 of the base band(0 to fs/2)space whereas the noise is spread uniformly across the band.,If we were to now digitally filter this sampled signal,we could remove approximately 3/4 of the noise,increasing the signal to noise ratio by a factor of 4 or 6 dB.,This effective increase in SNR is termed the processing gain,achieved by over-sampling the input relative to the 2 X bandwidth rule.,A simple formula for the maximum processing gain can easily be derived:processing gain(dB)=10,log(sample,rate/2,signal bandwidth),where it is assumed that,digital filtering,is employed to restrict,the sample bandwidth,to exactly match the wanted input signal bandwidth and that the noise is uniformly distributed.,Thus,if we use a 128 times,over-sampling design,(typically found in minidisk recorders and PC sound cards),we can achieve a real 18 dB improvement in signal to quantization noise,effectively increasing the resolution of the converter from 12 bits to 15 bits.,By way of an example,consider using this method to improve the performance of a data converter within a digital cellular phone.,The signal bandwidth for a GSM cellular channel is 200 kHz.,It is now possible to obtain high speed ADCs with a sampling rate of 80 MSPS and 14-bit resolution,giving an impressive measured 75 dB SNR over the full 0 to fs/2 bandwidth.,The processing gain possible is thus:processing gain(dB)=10 log(80 000 000/2,This concept of processing gain leads us nicely into the topic of,sigma-delta converters,.,These,take the notion of,processing gain to the extreme to achieve very high performance,low cost,low power devices ideally suited for audio applications,with simple analog interfacing(little or no anti-aliasing filters).,A block diagram of a basic sigma-delta converter is shown if Figure 2.8.,The converter is essentially a highly over-sampling I-bit ADC(the comparator)followed by digital filtering and decimation to realize the processing gain.,The effective performance of the converter,is,greatly enhanced by the addition of circuitry to,shape the quantization noise,such that,instead of being uniformly spread throughout the 0 to fs/2 band,it is minimized in the band of interest(Figure 2.9).,