nocliper/ilt

:microscope: Numerical routines to inverse the Laplace Transform for semiconductor Deep Level Transient Spectroscopy.         To cite: Vasilev, A. (2024). Numerical Inverse Laplace Transform for Deep-Level Transient Spectroscopy. https://doi.org/10.5281/zenodo.10462383

👋 Hi

I'm a researcher at «Wide-Bandgap Materials and Devices Lab» at NUST MISIS. My work is an exploration in the field of Wide-Bandgap semiconductors (Ga₂O₃, GaN, etc), so we publish some stuff. At the present day I'm a third-year PhD student in Semiconductor Physics, and that's where I keep some of the repositories I use in my work.

🔬 Check this out:

• nocliper/ilt – The main drawback of the classic DLTS technique is its low resolution of overlapped signals from traps. In classic DLTS a trap is seen as a wide peak and if there is an overlap of two or three of them it is hard to deconvolute and extract data accurately. Instead of using the DLTS time-window concept regularization is imposed. This approach makes Laplace DLTS much more sensitive to noise in comparison with box-car DLTS but gives a huge advantage in peak resolution and traps parameters extraction.

ilt's Issues

Transient with non-equidistant time spacing

Hi Anton,
awesome tool, thanks for sharing!
I was wondering if it's also possible to import data containing time steps? Our DLTS setup doesn't sample the capacitance transient equidistant, so its not possible to use the "t_step". Is it already possible to include it in the .DLTS file or would the "read_file.py" module need a change?

Many thanks for you help,
Chris

About the "process(C, proc)" function in "read_file"

Hi Anton,

I again want to express my sincere gratitude for sharing your code. I have now spent a significant amount of time delving into it and have been able to understand most of its functionality. I have also successfully implemented some very useful tools, such as a peak-finding algorithm for the heatmap, allowing me to create a scatter plot of emission rates over temperature (Arrhenius plot). Additionally, the code now performs linear regression on the Arrhenius plot to identify defect characteristics, specifically activation energy and capture cross-section.

However, I am still struggling to fully grasp the purpose and functionality of the "process(C, proc)" function. I would be incredibly grateful if you could explain the rationale behind its implementation. Using your original code with my data consistently results in low-frequency noise. Could you perhaps provide some insight into this issue?

This noise vanishes when not performing the "F = F + np.average(F)*2":

Therefore, the "Contin" algorithm appears to perform less effectively in the low-temperature regime. This is likely due to the increased noise in the initial transient data. Unfortunately, the "reSpect" algorithm is incompatible with this specific modification. If you have any experience with the suitability of different transient processing techniques for various data types, I would greatly appreciate your insights.

Thank you very much & best regards,
Chris