Impact of energy quantisation in single electron transistor island on hybrid complementary metal oxide semiconductor-single electron transistor integrated circuits

For the first time, the impact of energy quantisation in single electron transistor (SET) island on the performance of hybrid complementary metal oxide semiconductor (CMOS)-SET transistor circuits has been studied. It has been shown through simple analytical models that energy quantisation primarily increases the Coulomb Blockade area and Coulomb Blockade oscillation periodicity of the SET device and thus influences the performance of hybrid CMOS-SET circuits. A novel computer aided design (CAD) framework has been developed for hybrid CMOS-SET co-simulation, which uses Monte Carlo (MC) simulator for SET devices along with conventional SPICE for metal oxide semiconductor devices. Using this co-simulation framework, the effects of energy quantisation have been studied for some hybrid circuits, namely, SETMOS, multiband voltage filter and multiple valued logic circuits. Although energy quantisation immensely deteriorates the performance of the hybrid circuits, it has been shown that the performance degradation because of energy quantisation can be compensated by properly tuning the bias current of the current-biased SET devices within the hybrid CMOS-SET circuits. Although this study is primarily done by exhaustive MC simulation, effort has also been put to develop first-order compact model for SET that includes energy quantisation effects. Finally, it has been demonstrated that one can predict the SET behaviour under energy quantisation with reasonable accuracy by slightly modifying the existing SET compact models that are valid for metallic devices having continuous energy states.

Fusion of Complementary Online and Offline Strategies for Recognition of Handwritten Kannada Characters

This work describes an online handwritten character recognition system working in combination with an offline recognition system. The online input data is also converted into an offline image, and parallely recognized by both online and offline strategies. Features are proposed for offline recognition and a disambiguation step is employed in the offline system for the samples for which the confidence level of the classifier is low. The outputs are then combined probabilistically resulting in a classifier out-performing both individual systems. Experiments are performed for Kannada, a South Indian Language, over a database of 295 classes. The accuracy of the online recognizer improves by 11% when the combination with offline system is used.