A statistical approach for full-chip gate-oxide reliability analysis

Gate oxide breakdown is a key factor limiting the useful lifetime of an integrated circuit. Unfortunately, the conventional approach for full chip oxide reliability analysis assumes a uniform oxide-thickness for all devices. In practice, however, gate-oxide thickness varies from die-to-die and within-die and as the precision of process control worsens an alternative reliability analysis approach is needed. In this work, we propose a statistical framework for chip level gate oxide reliability analysis while considering both die-to-die and within-die components of thickness variation. The thickness of each device is modeled as a distinct random variable and thus the full chip reliability estimation problem is defined on a huge sample space of several million devices. We observe that the full chip oxide reliability is independent of the relative location of the individual devices. This enables us to transform the problem such that the resulting representation can be expressed in terms of only two distinct random variables. Using this transformation we present a computationally efficient and accurate approach for estimating the full chip reliability while considering spatial correlations of gateoxide thickness. We show that, compared to Monte Carlo simulation, the proposed method incurs an error of only 1∼6% while improving the runtime by around three orders.