Time-resolved photoelectron spectroscopy provides a versatile tool for investigating electron dynamics in gaseous, liquid, and solid samples on sub-femtosecond time scales. The extraction of information from spectrograms recorded with the attosecond streak camera remains a difficult challenge. Common algorithms are highly specialized and typically computationally heavy. In this work, we apply deep neural networks to map from streaking traces to near-infrared pulses as well as electron wavepackets and extensively benchmark our results on simulated data. Additionally, we illustrate domain-shift to real-world data. We also attempt to quantify the model predictive uncertainty. Our deep neural networks display competitive retrieval quality and superior tolerance against noisy data conditions, while reducing the computational time by orders of magnitude.
Laser Fault Injection (LFI) is considered to be the most powerful semiinvasive fault injection method for implementation attacks on security devices. In this work we discuss for the first time the application of the nonlinear Two-Photon Absorption (TPA) effect for the purpose of LFI. Though TPA is an established technique in other areas, e.g. fluorescence microscopy, so far it did not receive any attention in the field of physical attack methods on integrated circuits. We show that TPA has several superior properties over the regular linear LFI method. The TPA effect allows to work on non-thinned devices without increasing the induced energy and hence the stress on the device. In contrast to regular LFI, the nonlinearity of the TPA effect leads to increased precision due to the steeper descent in intensity and also a vertically restricted photoelectric effect. By practical experiments, we demonstrate the general applicability of the method for a specific device and that unlike a regular LFI setup, TPA-LFI is capable to inject faults without triggering a latch-up effect. In addition we discuss the possible implications of TPA-LFI on various sensor-based countermeasures.
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