Feedbacks in QCA: a Quantitative Approach

In the post-CMOS scenario a primary role is played by the quantum-dot cellular automata (QCA) technology. Irrespective of the specific implementation principle (e.g., either molecular, or magnetic or semiconductive in the current scenario) the intrinsic deep-level pipelined behavior is the dominant issue. It has important consequences on circuit design and performance, especially in the presence of feedbacks in sequential circuits. Though partially already addressed in literature, these consequences still must be fully understood and solutions thoroughly approached to allow this technology any further advancement. This paper conducts an exhaustive analysis of the effects and the consequences derived by the presence of loops in QCA circuits. For each problem arisen, a solution is presented. The analysis is performed using as a test architecture, a complex systolic array circuit for biosequences analysis (Smith–Waterman algorithm), which represents one of the most promising application for QCA technology. The circuit is based on nanomagnetic logic as QCA implementation, is designed down to the layout level considering technological constraints and experimentally validated structures, counts up to approximately 2.3 milion nanomagnets, and is described and simulated with HDL language using as a testbench realistic protein alignment sequences. The results here presented constitute a fundamental advancement in the emerging technologies field since: 1) they are based on a quantitative approach relying on a realistic and complex circuit involving a large variety of QCA blocks; 2) they strictly are reckoned starting from current technological limits without relying on unrealistic assumptions; 3) they provide general rules to design complex sequential circuits with intrinsically pipelined technologies, like QCA; and 4) they prove with a real application benchmark how to maximize the circuits performance.