Nano-crossbar arrays have emerged to achieve high performance computing beyond the limits of current CMOS. They offer area and power efficiency in courtesy of their easyto-fabricate and dense physical
Neuromorphic computing mimicking the functionalities of mammalian brain holds the promise for cognitive capabilities enabling new intelligent applications. However, research efforts so far mainly focused on using analog and digital CMOS technologies to emulate neural activities, and are yet to achieve expected benefits. They suffer from limited scalability, density overhead, interconnection bottleneck and power consumption related constraints. In this paper, we present a transformative approach for neuromorphic computing with Wave Interference Functions (WIF).
Most proposed nanoscale computing architectures are based on a certain type of two-level logic family, e.g.,AND–OR, NOR–NOR, NAND–NAND, etc. In this paper, a new fabric architecture that combines different logic families in the same nanofabric is proposed for higher density and better defect tolerance. To achieve this, we apply very minor modifications on the way of controlling nanogrids, while the basic manufacturing requirements remain the same. The fabric that is based on the new heterogeneous two-level logic yields higher density for the applications mapped to it.
High defect rates are associated with novel nanodevice-based systems owing to unconventional and self-assembly based manufacturing processes. Furthermore, in emerging nanosystems, fault mechanisms and distributions may be very different from CMOS due to unique physical layer aspects, and emerging circuits and logic styles. Development of analytical fault models for nanosystems is necessary to explore the design of novel fault tolerance schemes that could be more effective than conventional schemes.
An integrated device-fabric methodology for evaluating and validating Nanoscale Cognitive Computing Fabrics is presented. The methodology integrates physical layer assumptions for materials and device structures with accurate 3-D simulations of device electrostatics and operations and circuit level noise and cascading validations. Electrical characteristics of six different Crossed Nanowire Field Effect Transistors (xnwFETs) are simulated and current and capacitance data obtained.
