Machine learning estimation of signal in laser timing probing for hardware security applications

Abstract

Laser Timing Probe (LTP, also known as laser voltage probing, LVP) is a failure analysis technique that is widely used in fault isolation and product debugging. Electrical waveforms at a location of the probed site can be predicted when given a change of properties of a reflected beam of light due to the regular change of biasing of a device that has been brightened by light. The output of the signal to noise ratio is very low. Thus, multiple traces are necessary as it will be averaged to produce a readable waveform. Many applications of LTP have proven its superiority in recovering encrypted or sensitive data. However, the reliance on regular test sequences is imminent. As such, if waveforms from LTP can be predicted with a minimum number of traces, it will be able to reduce the need for counter measures. Machine learning has been around for a while and it has tremendous capabilities especially in the field of pattern recognition. In this project, the primary goal is to use machine learning on MATLAB platform and the knowledge of supervised machine learning to help reduce the number of traces.