Quantum-dot cellular automata (QCA) technology is considered to be a possible alternative for circuit implementation in terms of energy efficiency, integration density and switching frequency. Multiplexer (MUX) can be considered to be a suitable candidate for designing QCA circuits. In this paper, two different structures of energy-efficient 2×1 MUX designs are proposed. These MUXes outperform the best existing design in terms of power consumption with approximate reductions of 26% and 35%. Moreover, similar or better performance factors such as area and latency are achieved compared to the available designs. These MUX structures can be used as fundamental energy-efficient building blocks for replacing the majority-based structures in QCA. The scalability property of the proposed MUXes is excellent and can be used for energy-efficient complex QCA circuit designs.
This paper describes the implementation of a scalable SiGe FPGA that serves as a high speed FPGA test platform. A new configurable block (Basic Cell) has been evolved from the Xilinx 6200 specification, and is designed to perform in the gigahertz range. Two chips, a 4:1 multiplexer and 1:4 demultiplexer, were designed using the IBM SiGe 7HP process. The two designs can process 10 Gbps data streams.
DNA-based circuits are considered as the possible replacement for the traditional silicon transistor based circuits for biomedical applications, especially for implantable medical devices because of their programmability, bio-compatibility, light weight, and small size. The seesaw DNA circuits, which uses DNA strand displacement operation lacks the logic inversion operation and uses a dual-rail design to overcome this issue. Here, a logic inverter gate using DNA strand displacement operations is introduced. This logic inverter gate is having a modular property and hence it can be used anywhere in the circuit. A gate enabling technique is used to achieve the modular design and the same can be used in analogue designs also. A full-adder circuit is developed to confirm the modularity property of the logic inverter gate. The DNA circuits were simulated in visual DSD software.
With the recent developments in DNA nanotechnology, DNA has been used as the basic building block for the design of nanostructures, autonomous molecular motors, various devices, and circuits. DNA is considered as a possible candidate for replacing silicon for designing digital circuits in a near future, especially in implantable medical devices, because of its parallelism, computational powers, small size, light weight, and compatibility with bio-signals. The research in DNA digital design is in early stages of development, and electrical and computer engineers are not much attracted towards this field. In this paper, we give a brief review of the existing enzyme-free scalable DNA digital design techniques which are recently developed. With the developments in DNA circuits, it would be possible to design synthetic molecular systems, therapeutic molecular devices, and other molecular scale devices and instruments. The ultimate aim will be to build complex digital designs using DNA strands which may even be placed inside a human body.
Decision-making systems are an integral part of any autonomous device. With the recent developments in bio-nanorobots, smart drugs, and engineered viruses, there is an immediate need of decision-making systems which are bio-compatible in nature. DNA is considered a perfect candidate for designing the computing systems in such decision-making systems because of their bio-compatibility and programmability. Complex biological systems can be easily modeled/controlled using fuzzy logic operations with the help of linguistic rules. In this paper, we propose an enzyme-free DNA strand displacement-based architecture of fuzzy inference engine using the fuzzy operators, such as fuzzy intersection and union. The basic building blocks of this architecture are minimum, maximum, and fan-out gates. All these gates are analog in nature, which means that the input/output values of the gates are represented by the concentration of the input/output DNA strands. To demonstrate the performance of the proposed architecture, a detailed design, analysis, and kinetic simulation of each gate were carried out. Finally, the minimum and maximum gates are cascaded according to the pre-defined rules to design the fuzzy inference engine. All these DNA circuits are implemented and simulated in Visual DSD software.
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