On-chip variation sensor for systematic variation estimation in nanoscale fabrics

Parameter variations caused by manufacturing imprecision at the nanoscale are expected to cause large deviations in electrical characteristics of emerging nanodevices and nano-fabrics leading to performance deterioration and yield loss. Parameter variation is typically addressed pre-fabrication, with circuit design targeting worst-case timing scenarios. By contrast, if variation is estimated post-manufacturing, adaptive techniques or reconfiguration could be used to provide more optimal level of tolerance. This paper presents a new on-chip sensor design for nanoscale fabrics that from its own variation, can estimate the extent of systematic variation in neighboring regions. A Monte Carlo simulation framework is used to validate the sensor design. Known variation cases are injected and based on sensor outputs, the extent of systematic variation in physical parameters is calculated. Our results show that the sensor has less than 1.2% error in estimation of physical parameters in 100% of injected variation cases. Based on published experimental data, the sensor estimation is shown to be accurate to within 2% of the actual physical parameter value for a range of up to 7mm.