Analog to Digital Converters (ADCs) . An Analog to Digital Converter (ADC) is an essential electronic device that transforms continuous analog signals into discrete digital data. This conversion allows devices to interpret analog signals—such as sound, temperature, or light intensity—into digital values that can be processed by computers and other digital systems. ADCs play a critical role in enabling seamless interaction between the analog and digital worlds.
In a world dominated by digital technologies, ADCs ensure that analog information can be captured, processed, and utilized effectively. From your smartphone’s microphone to medical imaging equipment, ADCs are everywhere.
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How Do ADCs Work?
In the real world, analog signals are signals that have a continuous sequence with continuous values (there are some cases where it can be finite). These types of signals can come from sound, light, temperature and motion. Digital signals are represented by a sequence of discrete values where the signal is broken down into sequences that depend on the time series or sampling rate (more on this later). The easiest way to explain this it through a visual! Figure 1 shows a great example of what analog and digital signals look like.
Microcontrollers can’t read values unless it’s digital data. This is because microcontrollers can only see “levels” of the voltage, which depends on the resolution of the ADC and the system voltage.
ADCs follow a sequence when converting analog signals to digital. They first sample the signal, then quantify it to determine the resolution of the signal, and finally set binary values and send it to the system to read the digital signal. Two important aspects of the ADC are its sampling rate and resolution.
1. Sampling
Sampling involves taking periodic snapshots of the analog signal. The frequency of these snapshots, called the sampling rate, determines how accurately the digital representation mirrors the original analog signal.
2. Quantization
Quantization assigns discrete digital values to the sampled analog signals. The precision of this step is dictated by the ADC’s resolution, measured in bits.
3. Encoding
Finally, the quantized values are encoded into binary data for further processing.
For example, in audio recording, an ADC converts sound waves into digital data that can be edited, stored, and played back without significant loss of quality.
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Block Diagram of ADC
The analog signal is first applied to the ‘sample‘ block where it is sampled at a specific sampling frequency. The sample amplitude value is maintained and held in the ‘hold‘ block. It is an analog value. The hold sample is quantized into discrete value by the ‘quantize‘ block. At last, the ‘encoder‘ converts the discrete amplitude into a binary number.
What is the ADC sampling rate/frequency?
The ADC’s sampling rate, also known as sampling frequency, can be tied to the ADC’s speed. The sampling rate is measured by using “samples per second”, where the units are in SPS or S/s (or if you’re using sampling frequency, it would be in Hz). This simply means how many samples or data points it takes within a second. The more samples the ADC takes, the higher frequencies it can handle.
One important equation on the sample rate is:
fs = 1/T
Where,
fs = Sample Rate/Frequency
T = Period of the sample or the time it takes before sampling again
For example, in Figure 1, it seems fs is 20 S/s (or 20 Hz), while T is 50 ms. The sample rate is very slow, but the signal still came out similar to the original analog signal. This is because the frequency of the original signal is a slow 1 Hz, meaning the frequency rate was still good enough to reconstruct a similar signal.
“What happens when the sampling rate is considerably slower?” you might ask. It is important to know the sampling rate of the ADC because you will need to know if it will cause aliasing. Aliasing means that when a digital image/signal is reconstructed, it differs greatly from the original image/signal caused from sampling.
If the sampling rate is slow and the frequency of the signal is high, the ADC will not be able to reconstruct the original analog signal which will cause the system to read incorrect data. A good example is shown in Figure 2.
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In this example, you can see where the sampling occurs in the analog input signal. The output of the digital signal is not at all close to the original signal as the sampling rate is not high enough to keep up with the analog signal. This causes aliasing and now the digital system will be missing the full picture of the analog signal.
One rule of thumb when figuring out if aliasing will happen is using Nyquist Theorem. According to the theorem, the sampling rate/frequency needs to be at least twice as much as the highest frequency in the signal to recreate the original analog signal. The following equation is used to find the Nyquist frequency:
fNyquist = 2fMax
Where,
fNyquist = Nyquist frequency
fMax = The max frequency that appears in the signal
For example, if the signal that you input into the digital system has a max frequency of 100 kHz, then the sampling rate on your ADC needs to be equal or greater than 200 kS/s. This will allow for a successful reconstruction of the original signal.
It is also good to note that there are cases where outside noise can introduce unexpected high frequency into the analog signal, which can disrupt the signal because the sample rate couldn’t handle the added noise frequency. It is always a good idea to add an anti-aliasing filter (low-pass filter) before the ADC and sampling begins, as it can prevent unexpected high frequencies to make it to the system.
Analog To Digital Conversion Steps
The conversion from analog signal to a digital signal in an analog to digital converter is explained below using the block diagram given above.
Sample
The sample block function is to sample the input analog signal at a specific time interval. The samples are taken in continuous amplitude & possess real value but they are discrete with respect to time.
The sampling frequency plays important role in the conversion. So it is maintained at a specific rate. The sampling rate is set according to the requirement of the system.
Hold
The second block used in ADC is the ‘Hold’ block. It has no function. It only holds the sample amplitude until the next sample is taken. The hold value remains unchanged till the next sample.
Quantize
This block is used for quantization. It converts the analog or continuous amplitude into discrete amplitude.
The on hold continuous amplitude value in hold block goes through ‘quantize’ block & becomes discrete in amplitude. The signal is now in digital form as it has discrete time & discrete amplitude.
Encoder
The encoder block converts the digital signal into binary form i.e. into bits.
As we know that the digital devices operate on binary signals so it is necessary to convert the digital signal into the binary form using the Encoder.
This is the whole process of converting an Analog signal into digital form using an Analog to Digital Converter. This whole conversion occurs in a microsecond.
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Types of ADCs
ADCs come in various types, each suited for specific applications:
1. Successive Approximation Register (SAR) ADC
SAR ADCs strike a balance between speed and precision, making them ideal for applications such as digital oscilloscopes and industrial sensors.
2. Flash ADC
Known for their speed, Flash ADCs are used in applications requiring high-speed data acquisition, such as radar systems and video processing.
3. Delta-Sigma ADC
Delta-Sigma ADCs offer high resolution and are commonly found in audio applications and precision measurement instruments.
4. Dual Slope ADC
These ADCs are popular in digital multimeters due to their noise rejection and high accuracy over long conversion periods.
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How is resolution of ADC determined?
The ADC’s resolution can be tied to the precision of the ADC. The resolution of the ADC can be determined by its bit length. A quick example on how it helps the digital signal output a more accurate signal is shown in Figure 3. Here you can see that the 1-bit only has two “levels”. As you increase the bit length, the levels increase making the signal more closely represent the original analog signal.
If you need accurate voltage level for your system to read, then the bit resolution is important to know. The resolution depends on both the bit length and the reference voltage. These equations help you figure out the total resolution of the signal that you are trying to input in voltage terms:
Sample ADC Resolution Formula:
Step Size = VRef/N
Where,
Step Size = The resolution of each level in terms of voltage
VRef = The voltage reference (range of voltages)
N = Total level size of ADC
To find N size, use this equation:
N = 2n
Where,
n = Bit Size
For example, let’s say that a sine wave with a voltage range of 5 needs to be read. The ADC has a bit size of 12-bit. Plug in 12 to n on equation 4 and N will be 4096. With that known and the voltage reference set to 5V, you’ll have: Step Size = 5V/4096. You will find that the step size will be around 0.00122V (or 1.22mV). This is accurate as the digital system will be able to tell when the voltage changes on an accuracy of 1.22mV.
If the ADC was a very small bit length, let’s say only 2 bits, then the accuracy would reduce to only 1.25V, which is very poor as it will only be able to tell the system of four voltage levels (0V, 1.25V, 2.5V, 3.75V and 5V).
Figure 4 shows common bit length and their number of levels. It also shows what the step size would be for a 5V reference. You can see how accurate it gets as the bit length increases.
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With understanding both the resolution and the sample rates of the ADC, you can see how important it is to know these values and what to expect from your ADC.
Applications of ADCs
In the modern world of growing technology, we are dependent on digital devices. These digital devices operate on the digital signal. But not every quantity is in digital form instead they are in analog form. So an ADC is used for converting analog signals into digital signals. The applications of ADC are limitless. Some of these applications given below:
- Cell phones operate on the digital voice signal. Originally the voice is in analog form, which is converted through ADC before feeding to the cell phone transmitter.
- Images and videos captured using camera is stored in any digital device, is also converted into digital form using ADC.
- Medical Imaging like x-ray & MRI also uses ADC to convert images into Digital form before modification. They are then modified for better understanding.
- Music from the cassette is also converted into the digital form such as CDs and thumb drives using ADC converters.
- Digital Oscilloscope also contains ADC for converting Analog signal into a digital signal for display purposes & different other features.
- Air conditioner contains temperature sensors for maintaining the room temperature. This temperature is converted into digital form using ADC so that onboard controller can read & adjust the cooling effect.
In today’s modern world almost every device has become the digital version of itself & they need to have ADC in it. Because it has to operate in digital domain which can be only acquired using analog to digital converter (ADC).
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Advantages of ADCs
ADCs provide several advantages:
- Improved Precision: High-resolution ADCs enable detailed digital representations.
- Digital Signal Processing Compatibility: Digital data is easier to analyze, store, and transmit.
- Noise Resistance: ADCs can filter out noise in analog signals during conversion.
Sampling Rate
1. Nyquist Theorem
The Nyquist theorem states that the sampling rate must be at least twice the highest frequency of the analog signal to avoid aliasing, ensuring accurate signal reconstruction.
2. Oversampling vs. Undersampling
- Oversampling: Captures more details, improving accuracy but increasing data size.
- Undersampling: Reduces data size but risks losing crucial information.
Dynamic Range
Dynamic range refers to the ratio between the largest and smallest signal levels an ADC can measure. Applications like audio recording and imaging demand ADCs with high dynamic ranges to capture both faint and loud signals accurately.
ADC Specifications
Key specifications to evaluate an ADC include:
- Signal-to-Noise Ratio (SNR): Higher SNR ensures better signal clarity.
- Total Harmonic Distortion (THD): Lower THD translates to reduced signal distortion.
- Effective Number of Bits (ENOB): Measures the actual performance of an ADC compared to its theoretical resolution.
ADC vs. DAC
While ADCs convert analog signals into digital, Digital to Analog Converters (DACs) perform the reverse. Together, they enable seamless analog-digital interfacing, such as in audio equipment, where sound is digitized, processed, and converted back to analog.
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How to Choose an ADC
When selecting an ADC, consider:
- Application Requirements: For audio, choose a high-resolution ADC; for radar, opt for speed.
- Resolution and Sampling Rate: Match the ADC’s specifications to your needs.
- Cost vs. Performance: Ensure a balance between budget and functionality.
FAQs
1. What is the primary function of an ADC?
An ADC converts analog signals into digital data for processing and storage.
2. What are some common examples of ADCs in everyday life?
Examples include the microphone in smartphones, digital thermometers, and medical imaging devices.
3. Why is resolution important in an ADC?
Resolution determines the detail and accuracy of the digital representation of an analog signal.
4. What is the difference between ADC and DAC?
An ADC converts analog signals to digital, while a DAC does the reverse.
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