The 2023 list of quantum device simulation


With all this fuss about quantum things, I think it’s high time I updated the list of simulators post I did some years ago. I am completely remaking this post, instead of adding to the previous, because now things are more clear to me and to the community at large where each subdomain lies. Since this will be a list from my own experience, feel free to contact me with the software that you would like to see added in this list.

Also, it’s a list for technologies that can host Quantum Information carriers based on solid state device types, that is: semiconducting, superconducting, or even photonic. That is, with the possibility of inclusion of materials science and effects like topological. It is not about trapped ions and optical lattices, although apparently, you can do that too with Wannier functions, a method I will mention here.


In any type of solid state simulation, the materials play a fundamental role. However, we don’t always have to do some quantum mechanics simulation for different materials in order to do a simulation of the final device. This is because many of the effects get ‘wrapped up’ in simple variables, or do not affect the performance of the device.

Continue reading

Transport and trapping in oxides using Sentaurus TCAD

Charge transport and trapping in the oxides interests many people, when it comes to device engineering, for different reasons. One of them is the variety of device configurations that can exist using thin film oxides.

During my PhD, I worked a lot with simulating such trapping mehanisms using the TCAD software Synopsys Sentaurus. Of course, the main issue with this kind of work was whether it was producing meaningful results. While this can be tested only using experimental output, in order to comare the results of the simulator with the theory, I was using some Mathematica scripts that I am attaching at the end of this post.

This model, is what Sentaurus uses to model transport and trapping in the oxide, and while many calculations can be double checked with the script, some others are impossible, as to derive them solely from the bias applied to the device, would require extensive analytical modelling. Such thing, of course, would be hard if the device is three-dimensional, unless you are willing to write your own code that solves the Drift-Diffusion (DD) equation using the Finite Elements Method.

Instead, I chose to trust the solution of the DD coming from the software, and only test the equations of charge trapping using the values of current and electron density from specific points in the oxide. This was both for current and diffusion enabled trapping, as well as the state transition functionality for the creation of new traps in the oxides (as for example after stress is applied).

DDTrapping calculates the occupational probability and the trapped harge density given constants asĀ  well as model parameters from the simulator, taken at a specific location in the device.

StateTransitions similarly calculates the probability of the defect being in a specific state as well as the state transition rates.