Leveraging nanotechnology for computing opens up exciting new avenues for breakthroughs. For example, graphene is an emerging nanoscale material and is believed to be a potential candidate for post-Si nanoelectronics due to high carrier mobility and extreme scalability. Recently, a new graphene nanoribbon crossbar (xGNR) device was proposed which exhibits negative differential resistance (NDR).
Parameter variations introduced by manufacturing imprecision are becoming more influential on circuit performance. This is especially the case in emerging Nanoscale Cognitive Computing Fabrics due to unconventional manufacturing steps and aggressive scaling. On-chip variation sensors are gaining in importance since post-fabrication compensation techniques can be employed. In estimation with on-chip variation sensors, however, random variations are masked because of well-known averaging effects during measurements.
