Assessing the Impact of Defects on Energy Dissipation of QCA LT Gate using ML Models

Quantum Cellular Automata (QCA) is an emerging nanotechnology in the deep sub-micron regime. Significant characteristics like atomic-scale feature size, extreme-low power dissipation and high speed make QCA incredibly appealing and a more promising alternative to the traditional CMOS technology. The Layered T (LT) gate excels in the QCA implementation of the digital logic design paradigm since it operates with a stable and physically realisable fixed polarised cell. This work assesses the total energy dissipation of LT universal logic gates with Majority voter (MV) counterparts. The impact of single-cell displacement defects (SCDD) on the energy dissipation of the LT universal gate layouts is collated into a new dataset, namely energy-dissipation versus SCDD. Finally, the polynomial regression model of machine learning is exploited to predict the energy dissipation of the LT universal gate layouts in the presence of SCDD at the output cell.