This paper describes how a digital tool could help researchers build better avalanche photodiodes. Avalanche photodiodes, or APDs, are specialized detectors that signal the presence of photons under low-light conditions. Electrons generated by single photons are accelerated across a strong internal electric field, giving them enough momentum to create new electrons as they slam into atoms along the way. Through this “avalanche” effect, a single photogenerated electron becomes as many as 40 to 100. That provides a way of making otherwise hidden objects visible, and it’s why APDs are indispensable in astronomy, military ranging, and medical imaging. The ideal APD is one that offers maximum multiplication of electrons with low noise. One way of accounting for noise is McIntyre’s equation. While powerful, this equation does not account for a known physical property of APDs: dead space. Dead space is the distance that a charge carrier travels before acquiring enough energy to generate a new carrier. This study describes how incorporating dead space into a model of APD behavior predicts stable charge carrier multiplication and lower noise than that predicted by McIntyre’s equation. The model is represented using a graphical user interface built in MATLAB. Digitizing APD performance data, such as that reported by the French Aerospace Laboratory (ONERA), and comparing it with the output of this new model could help account for the dead space effect and reduce noise in APDs.