  Biologically inspired event-based vision sensors (EVS) are growing in
popularity due to performance benefits including ultra-low power consumption,
high dynamic range, data sparsity, and fast temporal response. They efficiently
encode dynamic information from a visual scene through pixels that respond
autonomously and asynchronously when the per-pixel illumination level changes
by a user-selectable contrast threshold ratio, $\theta$. Due to their unique
sensing paradigm and complex analog pixel circuitry, characterizing Event-based
Vision Sensor (EVS) is non-trivial. The step-response probability curve
(S-curve) is a key measurement technique that has emerged as the standard for
measuring $\theta$. In this work, we detail the method for generating accurate
S-curves by applying an appropriate stimulus and sensor configuration to
decouple 2nd-order effects from the parameter being studied. We use an EVS
pixel simulation to demonstrate how noise and other physical constraints can
lead to error in the measurement, and develop two techniques that are robust
enough to obtain accurate estimates. We then apply best practices derived from
our simulation to generate S-curves for the latest generation Sony IMX636 and
interpret the resulting family of curves to correct the apparent anomalous
result of previous reports suggesting that $\theta$ changes with illumination.
Further, we demonstrate that with correct interpretation, fundamental physical
parameters such as dark current and RMS noise can be accurately inferred from a
collection of S-curves, leading to more accurate parameterization for
high-fidelity EVS simulations.
