Using ADC Code Density Data Without Mistaking the Stimulus for DNL

An ADC histogram can reveal uneven code widths, but unequal counts do not automatically indicate converter nonlinearity. The expected count depends on the input waveform and how it is sampled. A useful code-density test separates the stimulus distribution, acquisition integrity, and statistical coverage from the converter behavior being estimated.

Choose the expected distribution

The Analog Devices article on histogram testing explains that measured code density must be compared with the ideal density for the applied signal. A sine wave naturally spends more time near its extremes than near its center, so its raw histogram is not flat.

Record waveform type, amplitude, offset, frequency, sampling rate, and analysis method. Confirm which codes are included and how endpoint clipping or incomplete range coverage is handled.

Verify the acquisition path first

Check code format, bit alignment, sign conversion, and dropped-sample behavior before calculating nonlinearity. A missing bit or incorrect sign extension can create a dramatic histogram defect that has nothing to do with analog code width.

Use supported digital test patterns where available to validate the capture path. Preserve raw samples so the analysis can be repeated independently of the acquisition software.

Establish sufficient coverage

Count the observations in each analyzed code and assess uncertainty. A code with no hits in a short record has not necessarily been proven missing. The record must exercise that region often enough for the conclusion being drawn.

For example, a high-resolution converter driven over only a small portion of its input range cannot yield a full-range DNL claim. Label the tested range explicitly and avoid filling unvisited codes with an assumed result.

Check stimulus quality and repeatability

Repeat the measurement with a changed input frequency or phase relationship while preserving the intended distribution. Look for a pattern that follows the source or acquisition timing rather than remaining associated with the same converter codes.

Assess source distortion, noise, and amplitude stability against the required measurement accuracy. An imperfect ramp or sine source can bias the estimated code widths. The source’s nominal resolution alone does not establish its suitability.

Compare independent records rather than reporting only one best-looking run. Where the method derives INL from DNL, state the endpoint or best-fit convention and any excluded codes.

Save the raw data, expected distribution, sample counts, source characterization, and analysis settings. The result should describe a supported estimate with its tested range and limitations. A colorful histogram is useful evidence only when the relationship between input probability and output-code occurrence has been established.