Efficient physics-based modeling of a representative semiconducting quantum dot single electron device
In this work we study electron transport modeling of a semiconducting quantum dot interacting with metal electrodes. The modeling utilizes a physics-based kinetic Monte Carlo algorithm to balance accuracy with improved calculation speed, applied to the transport characteristic of a reported experimental Single Electron Transistor (SET) device with semiconducting silicon islands. We introduce an efficient numerical integration method to accurately calculate the electron tunneling rates for all allowable transitions, then apply kinetic Monte Carlo methods to simulate the electronic transport properties of the device. The method accounts for non-constant density of states and transition probabilities, and parasitic field-effect device coupling. A series of test cases have been introduced to demonstrate the relevance of the model. Three test cases explore the physical properties of the SET to confirm the proper modeling of gate, drain, source capacitances, charging energies, and junction widths. A study is designed to explore the temperature sensitivity of a SET with a semiconducting island, where the results show an interesting possibility to control the temperature sensitivity of the system through the applied biases. This behavior is not possible in metallic SET systems. A final study shows that the methodology models a representative experimental three terminal silicon single electron device with a coupled parasitic field effect transistor at low to moderate source-drain biases. The study demonstrates that the complex and aperiodic behavior of the Coulomb oscillations in the experimental device over temperature and bias cannot be fully characterized by the modeled physics of the SET or the nanoscaled MOSFET.
17th International Conference on Nanotechnology (IEEE-NANO)
Bergstrom, P. L.,
Efficient physics-based modeling of a representative semiconducting quantum dot single electron device.
17th International Conference on Nanotechnology (IEEE-NANO), 739-744.
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