Analog data systems use fast digitizers to convert continuous voltage waveforms into digital signals with discrete values in both time and voltage. The voltage resolution is limited by the digitizer’s number of bits, so the output is in discrete steps, which causes an uncertainty called digitization noise, characterized by the ideal SNR for a given number of bits. The SNR improvement caused by averaging is quantified. Static figures of merit include offset and gain errors and nonlinearities. Dynamic figures of merit include signal-to-noise and distortion ratio, total harmonic distortion, effective number of bits, and spurious free dynamic range, all measured by a fast Fourier transform, and aperture error. Testing methods include the histogram test, which is illustrated with an example. A testing summary table is provided. Photon counting data systems have a discriminator, a shaper, and a counter. The shaper has a dead time between pulses that causes a maximum count rate. Models of the true versus measured count rates are given for both paralyzable and non-paralyzable photon counting systems. Hybrid analog/photon counting data systems are described.

This chapter presents a general overview of sensor characterization from a system perspective, without any reference to a specific implementation. The systems are defined on the basis of input and output signal description and the overall architecture is discussed, showing how the information is transduced, limited, and corrupted by errors. One of the main points of this chapter is the characterization of the error model, and how this one could be used to evaluate the uncertainty of the measure, along with its relationship with resolution, precision and accuracy of the overall system. Finally, the quantization process, which is at the base of any digital sensor systems, is illustrated, interpreted, and included in the error model.