The RO (Ring-Oscillator)-based sensor is one of easily-implementable variation sensors, but for decomposing the observed variability into multiple unique device-parameter variations, a large number of ROs with different structures and sensitivities to device-parameters is required. This paper proposes an area efficient device parameter estimation method with sensitivity-configurable ring oscillator (RO). This sensitivity-configurable RO has a number of configurations and the proposed method exploits this property for reducing sensor area and/or improving estimation accuracy. The proposed method selects multiple sets of sensitivity configurations, obtains multiple estimates and computes the average of them for accuracy improvement exploiting an averaging effect. Experimental results with a 32-nm predictive technology model show that the proposed averaging with multiple estimates can reduce the estimation error by 49% or reduce the sensor area by 75% while keeping the accuracy. Compared to previous work with iterative estimation, 23% accuracy improvement is achieved.

Device-parameter estimation sensors inside a chip are gaining its importance as the post-fabrication tuning is becoming of a practical use. In estimation of variational parameters using on-chip sensors, it is often assumed that the outputs of variation sensors are not affected by random variations. However, random variations can deteriorate the accuracy of the estimation result. In this paper, we propose a device-parameter estimation method with on-chip variation sensors explicitly considering random variability. The proposed method derives the global variation parameters and the standard deviation of the random variability using the maximum likelihood estimation. We experimentally verified that the proposed method improves the accuracy of device-parameter estimation by 11.1 to 73.4% compared to the conventional method that neglects random variations.