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Over the past week, some of our readers have encountered an error while converting adc errors. This issue can occur due to many factors. Let’s discuss this below.

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The absolute accuracy and/or possibly the total accuracy of the ADC based on Figure 7 is clearly the maximum value of the approximate difference between the analog value and the preferred median value. This includes offset, gain, and integral linearity errors, as well as quantization error that simply comes from the ADC.

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Using a 12-bit analog-to-digital converter (ADC) does not necessarily mean that your system has 12-bit precision. Sometimes, to the surprise and chagrin of a great engineer, the data acquisition system performs much worse than expected. When this important fact is magically discovered after the first run, any more powerful ADC becomes insane, and countless hours are spent reworking the design as the pre-production deadline quickly approaches. What happened? What has changed since the first scan? A thorough understanding of the ADC specifications will reveal the complexities that often result in a lower amount than desired. Understanding ADC specifications will also help you select the right ADC for your application.

## What is ADC gain error?

The analog-to-digital converter (ADC) arrival error is defined as the large difference between the midpoint of the last step behind the actual ADC and the midpoint of the longer last step of the ideal ADC, thus compensating for the offset error. Applying a peak voltage of 0 always results in an output value of 0 after offset errors have been subtracted.

Let’s start by defining the general performance requirements for our system. Every component in the system has an associated fault; The goal, of course, is to keep the total error below a certain limit. Often the ADC is a key component in some signal paths, so we must be careful when choosing the right printer. For an ADC, let’s assume that the requirements for conversion rate, interface, power supply, power dissipation, input range, and number of channels are acceptable before evaluating overall system performance. The accuracy of most ADCs depends on several key characteristics, including integral non-linearity (INL) error, offset and gain errors, voltage reference accuracy, temperature and influence, and AC power. It is generally recommended to start ADC analysis by checking DC characteristics, because ADCs use a wide range of non-standard test conditions for their AC characteristics, making it easy to compare ICs based on characteristics.DC tick. DC performance is generally better than AC performance.

## Requirements

The two popular methods for determining total system error are undoubtedly the square root method (RSS) and our own worst case method. Using RSS facilities, error terms are only squared, then added, and then squared. takes root. The RSS error budget is defined as follows:

## What does error offsetting relates to in ADC?

The offset error is the difference between the point of convergence of the least significant code and the center of the same system on an ideal ADC with your current equal number of bits. The offset error is usually reported in terms of the least significant bit (LSB) associated with the converter. LSB corresponds exactly to the quantization interval of the transducer.

where E_{N} is the schema component’s required parameter term or . This formula is most accurate when most of the error terms are uncorrelated (which may or may not be the case in each of our cases). What errors are added when analyzing errors in the worst case. This method promises an error that never exceeds the selected limit. Because it sets a bound on the severity of the error, the actual error will always be MUCH (often less) less than this value.

The measured error is usually between the values of your two current methods, but is often less than the RSS value. pay attentionNote that, depending on the error budget, generalized or worst-case values can be used for most error conditions. The thought is based on many factors, it consists of the standard deviation of the size value, the importance of these different parameters, the size of the exact error compared to other similar errors. So there is nothing complicated in following simple rules. For our analysis, we use the worst case method.

In this example, let’s say we want 0.1% precision and 10 bits (1/2^{10}). So it makes sense to just convert to a higher resolution than that. If we decide to use a 12-bit air compressor, we can assume that this will be enough; but without wish checking, there is no guarantee of 12-bit performance (could be better or worse). For example, a 12-bit ADC with 4 LSBs of cumulative non-linearity error is likely to provide only 10-bit accuracy at best (assuming the numerator and gain errors are already calibrated). A device with an INL of 0.5 lbsbs can provide errorsbku 0.0122% 13 or possibly some accuracy (with gain and offset errors removed). To find the optimal precision, divide the maximum INL error by 2^{N}, where N is the number of bits. As an argument, an error of 0.075% (or 88 bits) allows for a 0.025% error for the ADC with respect to the rest of the circuit, which includes the errors of the most important sensor, associated input signal, audio circuits (op-amps, multiplexers, etc.). ) or possibly digital-to-analog converters (DACs), signals, PWM, or other analog output signals in the new signal path.

## How do you calculate ADC error?

Profitand compensation error are estimated using the heterosexual line equation y = mx + s, where m is the slope of the line and b is our bias. Gain error can finally be calculated as the transconductance of, I would say, the actual ADC output divided by the transconductance of the ideal ADC output.

We assume that the system typically has some total error budget based on the sum of the error terms for each factor that affects the circuit in the signal path. Other beliefs we will make is that we measure slowly varying DC sickness input with 1kHz data and our operating temperature range is 0°C to 70°C with overall performance guaranteed from 0 °C to 50 °C.

## DC Power

### Y differential Non-linearity

Although your differential non-linearity error (dnl) is not listed as a decision parameter for the ADC, it is the most important characteristic to track. DNL shows how different the code is from that of a good solid neighbor. The distance is actually the change in input voltage value, then converted to LSB (illustration 1). Please note that INL is an integral part of DNL errors, which is why DNL is not documented in our list of key policies. The biggest performance benefit of an ADC is the claim that “there is no missing code”. This means that once the input voltage is set within its range, all output code options will be displayed depending on the performance of the converter. DNL error <±1 LSB guarantees no missing codes (Figure 1a). DNL error values are shown in figures 1b, 1c and 1d. With error DNL -0.5 LSB 1b), (digit, the device finally has no more missing codes. In doing so, you simply evaluate to -1LSB (Figure 1c), your current deviceIt is unlikely to miss any codes. This note 10 code is missing. However, most ADCs that give a maximum DNL +/-1 error simply indicate if the device missed any codes. Because our production test limits are actually more stringent than those listed in the specifications, there is no recommendation.

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