The process of converting continuously varying signals into digital signals is called analog-to-digital conversion.
Generally, analog-to-digital conversion is carried out in the order of sampling-quantization-coding.
According to the sampling theorem, the sampling rate is selected such that it is greater than or equal to 2fmsamples per second, where fmis the highest frequency of the continuous time or analog signal.
A continuous signal is converted into discrete, both in time and amplitude, using analog-to-digital converters or ADCs.
While working on a power electronics project, I used field programmable gate arrays (FPGAs) to control the semiconductor switches. The converter was intended to operate to maintain a constant input voltage to a load, irrespective of the power quality problems. To fulfill the constant voltage requirement, the input AC voltage was compared with the desired value (or reference value), and the missing voltage was supplied by the converter. The hardware implementation involved analog-to-digital conversion of the input voltage to generate switching pulses. The analog-to-digital converter in the FPGA was utilized to do this.
We will explore the sequence involved in analog-to-digital conversion in this article.
Analog-to-Digital Conversion: The Basics
In most engineering applications, we utilize digital signals to improve processing, reduce noise, and save time. Most systems employ embedded systems such as microprocessors, microcontrollers, FPGAs, etc. to develop control systems. It is important to digitize analog signals for use in embedded systems, as they can only understand 0s and 1s.
The process of converting continuously varying signals into digital signals is called analog-to-digital conversion. The electronics associated with analog-to-digital conversion convert the analog signal to a digital signal, keeping the essential content of the signal intact.
One of the main applications today of analog-to-digital conversion is communication systems. The speed of propagation and the propagation efficiency is higher with digital signals than with analog signals due to the well-defined and orderly arrangement of digital signals. With digital signals, it is easier for electronic circuits to identify noises and disturbances. The arena of digital signals is ever-expanding. Whenever and wherever analog signals are given to digital systems, there is a need for analog-to-digital conversion.
Steps Involved in Analog-to-Digital Conversion
A sequence is followed when converting analog signals to digital signals:
Sampling is the process in which continuous-time signals or analog signals are converted into discrete-time signals. The samples of the analog signals are taken at discrete time instants to obtain discrete-time signals. Sampling is associated with a term called sampling rate, which gives the number of samples per second, or sampling frequency.
The sampling frequency is significant in converting the analog signal to a digital signal without losing any special information. An adequate sampling frequency should be selected for recovering the spectrum of the analog signal from the spectrum of the discrete-time signal.
Nyquist Theorem or Sampling Theorem
Nyquist theorem or sampling theorem governs the sampling process for recovering the analog signal from the digital signal without loss of information. According to the sampling theorem, the sampling rate Fs is selected such that it is greater than or equal to 2fmsamples per second, where fm is the highest frequency of the continuous time or analog signal.
Classification of the Sampling Process
The sampling process can be performed in different ways. If samples are obtained at regular time intervals, there is uniformity in the resulting samples, and this corresponds to periodic sampling. When the same sampling pattern is periodically repeated, it forms multiple-order sampling. Random sampling and multiple-rate sampling are the other types of sampling.
The conversion of a discrete-time continuous-valued signal into a discrete-time discrete-value signal is called quantization. In the quantization process, each signal sample is represented by a value chosen from the finite set of possible values. The possible values are collectively called quantization levels. The difference between the unquantized sample and the quantized output is called the quantization error. The analog-to-digital conversion effectiveness can be improved by minimizing the quantization error.
Coding or Encoding
The process in which the discrete value samples are represented by an n-bit binary sequence or code is called coding.
Analog signals are continuous in terms of both magnitude and time. The continuous signal is converted into discrete, both in time and amplitude, using analog-to-digital converters or ADCs. The input to the analog-to-digital converters (for example, sine waves, human speech, or signals from a camera) consists of an infinite number of values. However, the analog-to-digital converter produces a digital output of defined levels or states from the analog input. The defined levels or states of the digital output are usually a power of two. When there are only two states, it forms binary signals and the states are either 0 or 1.
The Importance of SPICE Models of ADCs
Generally, analog-to-digital conversion is carried out in the order of sampling-quantization-coding. However, in-build (as in embedded systems) or off-the-shelf analog-to-digital converters are employed in electronic circuits for conversion.
Simulate ADC Response With Cadence Tools
Cadence’s suite of design and analysis tools can help you to simulate the response of ADCs with SPICE models. With SPICE simulation supported by Cadence software, you can quickly design ADC circuits with the required conversion accuracy.
Leading electronics providers rely on Cadence products to optimize power, space, and energy needs for a wide variety of market applications. If you’re looking to learn more about our innovative solutions, talk to our team of experts or subscribe to our YouTube channel.