Hierarchical probabilistic macromodeling for QCA circuits

With the goal of building an hierarchical design methodology for quantum-dot cellular automata (QCA) circuits, we put forward a novel, theoretically sound, method for abstracting the behavior of circuit components in QCA circuit, such as majority logic, lines, wire-taps, cross-overs, inverters, and corners, using macromodels. Recognizing that the basic operation of QCA is probabilistic in nature, we propose probabilistic macromodels for standard QCA circuit elements based on conditional probability characterization, defined over the output states given the input states. Any circuit model is constructed by chaining together the individual logic element macromodels, forming a Bayesian network, defining a joint probability distribution over the whole circuit. We demonstrate three uses for these macromodel-based circuits. First, the probabilistic macromodels allow us to model the logical function of QCA circuits at an abstract level - the "circuit" level - above the current practice of layout level in a time and space efficient manner. We show that the circuit level model is orders of magnitude faster and requires less space than layout level models, making the design and testing of large QCA circuits efficient and relegating the costly full quantum-mechanical simulation of the temporal dynamics to a later stage in the design process. Second, the probabilistic macromodels abstract crucial device level characteristics such as polarization and low-energy error state configurations at the circuit level. We demonstrate how this macromodel-based circuit level representation can be used to infer the ground state probabilities, i.e., cell polarizations, a crucial QCA parameter. This allows us to study the thermal behavior of QCA circuits at a higher level of abstraction. Third, we demonstrate the use of these macromodels for error analysis. We show that low-energy state configurations of the macromodel circuit match those of the layout level, thus allowing us to isolate weak p- oints in circuits design at the circuit level itsel

Quantum Dot Cellular Automata Check Node Implementation for LDPC Decoders

The quantum dot Cellular Automata (QCA) is an emerging nanotechnology that has gained significant research interest in recent years. Extremely small feature sizes, ultralow power consumption, and high clock frequency make QCA a potentially attractive solution for implementing computing architectures at the nanoscale. To be considered as a suitable CMOS substitute, the QCA technology must be able to implement complex real-time applications with affordable complexity. Low density parity check (LDPC) decoding is one of such applications. The core of LDPC decoding lies in the check node (CN) processing element which executes actual decoding algorithm and contributes toward overall performance and complexity of the LDPC decoder. This study presents a novel QCA architecture for partial parallel, layered LDPC check node. The CN executes Normalized Min Sum decoding algorithm and is flexible to support CN degree dc up to 20. The CN is constructed using a VHDL behavioral model of QCA elementary circuits which provides a hierarchical bottom up approach to evaluate the logical behavior, area, and power dissipation of the whole design. Performance evaluations are reported for the two main implementations of QCA i.e. molecular and magneti

Bayesian macromodeling for circuit level QCA design

We present a probabilistic methodology to model and abstract the behavior of quantum-dot cellular automata circuit(QCA) at “ circuit level” above the current practice of layout level. These macromodels provide input-output relationship of components (a set of QCA cells emulating a logical function) that are faithful to the underlying quantum effects. We show the macromodeling of a few key circuit components in QCA circuit, such as majority logic, lines, wire-taps, cross-overs, inverters, and corners. In this work, we demostrate how we can make use of these macromodels to abstract the logical function of QCA circuits and to extract crucial device level characteristics such as polarization and low-energy error state configurations by circuit level Bayesian model, accurately accounting for temperature and other device level parameters. We also demonstrate how this macromodel based design can be used effectively in analysing and isolating the weak spots in the design at circuit level itself

A Signal Distribution Network for Sequential Quantum-dot Cellular Automata Systems

The authors describe a signal distribution network for sequential systems constructed using the Quantum-dot Cellular Automata (QCA) computing paradigm. This network promises to enable the construction of arbitrarily complex QCA sequential systems in which all wire crossings are performed using nearest neighbor interactions, which will improve the thermal behavior of QCA systems as well as their resistance to stray charge and fabrication imperfections. The new sequential signal distribution network is demonstrated by the complete design and simulation of a two-bit counter, a three-bit counter, and a pattern detection circuit