Graduation Year


Document Type




Degree Granting Department

Electrical Engineering

Major Professor

Sanjukta Bhanja, Ph.D.


Probabilistic model, QCA, Power dissipation, Bayesian networks, Quantum-Dot Cellular Automata


As CMOS scaling faces a technological barrier in the near future, novel design paradigms are being proposed to keep up with the ever growing need for computation power and speed. Most of these novel technologies have device sizes comparable to atomic and molecular scales. At these levels the quantum mechanical effects play a dominant role in device performance, thus inducing uncertainty. The wave nature of particle matter and the uncertainty associated with device operation make a case for probabilistic modeling of the device. As the dimensions go down to a molecular scale, functioning of a nano-device will be governed primarily by the atomic level device physics. Modeling a device at such a small scale will require taking into account the quantum mechanical phenomenon inherent to the device. In this dissertation, we studied one such nano-device: Quantum-Dot Cellular Automata (QCA).

We used probabilistic modeling to perform a fast approximation based method to estimate error, power and reliability in large QCA circuits. First, we associate the quantum mechanical probabilities associated with each QCA cell to design and build a probabilistic Bayesian network. Our proposed modeling is derived from density matrix-based quantum modeling, and it takes into account dependency patterns induced by clocking. Our modeling scheme is orders of magnitude faster than the coherent vector simulation method that uses quantum mechanical simulations. Furthermore, our output node polarization values match those obtained from the state of the art simulations. Second, we use this model to approximate power dissipated in a QCA circuit during a non-adiabatic switching event and also to isolate the thermal hotspots in a design. Third, we also use a hierarchical probabilistic macromodeling scheme to model QCA designs at circuit level to isolate weak spots early in the design process.

It can also be used to compare two functionally equivalent logic designs without performing the expensive quantum mechanical simulations. Finally, we perform optimization studies on different QCA layouts by analyzing the designs for error and power over a range of kink energies.To the best of our knowledge the non-adiabatic power model presented in this dissertation is the first work that uses abrupt clocking scheme to estimate realistic power dissipation. All prior works used quasi-adiabatic power dissipation models. The hierarchical macromodel design is also the first work in QCA design that uses circuit level modeling and is faithful to the underlying layout level design. The effect of kink energy to study power-error tradeoffs will be of great use to circuit designers and fabrication scientists in choosing the most suitable design parameters such as cell size and grid spacing.