Integrating nano-logic into an undergraduate logic design course

The goal of this work is to motivate our students and enhance their ability to address newer logic blocks namely majority gates in the existing framework. We use a K-map based methodology to introduce a few novel nano-logic design concepts for the undergraduate logic design class. We want them to possess knowledge about a few fundamental abstracted logical behaviors of future nano-devices and their functionality which in turn would motivate them to further investigate these non-CMOS emerging devices, logics and architectures. This would augment critical thinking of the students where they apply the learnt knowledge to a novel/unfamiliar situation. We intend to augment the existing standard EE and CS courses by inserting K-map based knowledge modules on nano-logic structure for stimulating their interest without significant diversion from the course framework. Experiments with our students show that all the students were able to grasp the basic concept of majority logic synthesis and almost 63 of them had a deeper understanding of the synthesis algorithm demonstrated to them

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

Integrating a nanologic knowledge module Into an undergraduate logic design course

This work discusses a knowledge module in an undergraduate logic design course for electrical engineering (EE) and computer science (CS) students, that introduces them to nanocomputing concepts. This knowledge module has a twofold objective. First, the module interests students in the fundamental logical behavior and functionality of the nanodevices of the future, which will motivate them to enroll in other elective courses related to nanotechnology, offered in most EE and CS departments. Second, this module can be used to let students analyze, synthesize, and apply their existing knowledge of the Karnaugh-map-based Boolean logic reduction scheme into a revolutionary design context with majority logic. Where many efforts focus on developing new courses on nanofabrication and even nanocomputing, this work is designed to augment the existing standard EE and CS courses by inserting knowledge modules on nanologic structures so as to stimulate student interest without creating a significant diversion from the course framework

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

Work in progress: introduction of K-map based nano-logic synthesis as knowledge module in logic design course

This work in progress reports an effort of introducing knowledge module regarding novel nano-devices and novel logic primitives in undergraduate logic design class. Our motivation is to make our students aware of fundamental abstracted logical behaviors of future nano-devices, their functionality. This effort would also help the students use their existing knowledge of K-map based logical synthesis into constructing logic blocks for novel devices that uses majority logic as basic construct. Moreover, additional to stimulating our students' interests, we are also augmenting their learning by challenging them to use their existing knowledge to analyze, synthesize and comprehend novel nano-logic issues through the worksheets and lecture modules. Whereas many efforts are focusing on developing new courses on nanofabrication and even nano-computing, we intend to augment the existing standard EE and CS courses by inserting knowledge modules on nano-logic structure for stimulating their interest without significant diversion from the course framework

Error-power tradeoffs in QCA design

In this work we present an error-power tradeoff study in a Quantum-dot Cellular Automata (QCA) circuit design. Device parameter variation to optimize performance is a very crucial step in the development of a technology. In this work we vary the maximum kink energy of a QCA circuit to perform an error-power tradeoff study in QCA design. We make use of graphical probabilistic models to estimate polarization errors and non-adiabatic energy dissipated in a clocked QCA circuit and demonstrate the tradeoff studies on the basic QCA circuits such as majority gate and inverter. We also show how this study can be used by comparing two single bit adder designs. The study will be of great use to designers and fabrication scientists to choose the most optimum size and spacing of QCA cells to fabricate QCA logic designs

Sequential circuit design in quantum-dot cellular automata

In this work we present a novel probabilistic modeling scheme for sequential circuit design in quantum-dot cellular automata(QCA) technology. Clocked QCA circuits possess an inherent direction for flow of information which can be effectively modeled using Bayesian networks (BN). In sequential circuit design this presents a problem due to the presence of feedback cycles since BN are direct acyclic graphs (DAG). The model presented in this work can be constructed from a logic design layout in QCA and is shown to be a dynamic Bayesian Network (DBN). DBN are very powerful in modeling higher order spatial and temporal correlations that are present in most of the sequential circuits. The attractive feature of this graphical probabilistic model is that that it not only makes the dependency relationships amongst node explicit, but it also serves as a computational mechanism for probabilistic inference. We analyze our work by modeling clocked QCA circuits for SR F/F, JK F/F and RAM designs

Benefits and pitfalls of GRACE data assimilation: A case study of terrestrial water storage depletion in India

This study investigates some of the benefits and drawbacks of assimilating terrestrial water storage (TWS) observations from the Gravity Recovery and Climate Experiment (GRACE) into a land surface model over India. GRACE observes TWS depletion associated with anthropogenic groundwater extraction in northwest India. The model, however, does not represent anthropogenic groundwater withdrawals and is not skillful in reproducing the interannual variability of groundwater. Assimilation of GRACE TWS introduces long-term trends and improves the interannual variability in groundwater. But the assimilation also introduces a negative trend in simulated evapotranspiration, whereas in reality evapotranspiration is likely enhanced by irrigation, which is also unmodeled. Moreover, in situ measurements of shallow groundwater show no trend, suggesting that the trends are erroneously introduced by the assimilation into the modeled shallow groundwater, when in reality the groundwater is depleted in deeper aquifers. The results emphasize the importance of representing anthropogenic processes in land surface modeling and data assimilation systems

Estimating long-term groundwater storage and its controlling factors in Alberta, Canada

Groundwater is one of the most important natural resources for economic development and environmental sustainability. In this study, we estimated groundwater storage in 11 major river basins across Alberta, Canada, using a combination of remote sensing (Gravity Recovery and Climate Experiment, GRACE), in situ surface water data, and land surface modeling estimates (GWSAsat). We applied separate calculations for unconfined and confined aquifers, for the first time, to represent their hydrogeological differences. Storage coefficients for the individual wells were incorporated to compute the monthly in situ groundwater storage (GWSAobs). The GWSAsat values from the two satellite-based products were compared with GWSAobs estimates. The estimates of GWSAsat were in good agreement with the GWSAobs in terms of pattern and magnitude (e.g., RMSE ranged from 2 to 14&thinsp;cm). While comparing GWSAsat with GWSAobs, most of the statistical analyses provide mixed responses; however the Hodrick–Prescott trend analysis clearly showed a better performance of the GRACE-mascon estimate. The results showed trends of GWSAobs depletion in 5 of the 11 basins. Our results indicate that precipitation played an important role in influencing the GWSAobs variation in 4 of the 11 basins studied. A combination of rainfall and snowmelt positively influences the GWSAobs in six basins. Water budget analysis showed an availability of comparatively lower terrestrial water in 9 of the 11 basins in the study period. Historical groundwater recharge estimates indicate a reduction of groundwater recharge in eight basins during 1960–2009. The output of this study could be used to develop sustainable water withdrawal strategies in Alberta, Canada.</p