Toward efficient spatial variation decomposition via sparse regression

In this paper, we propose a new technique to accurately decompose process variation into two different components: (1) spatially correlated variation, and (2) uncorrelated random variation. Such variation decomposition is important to identify systematic variation patterns at wafer and/or chip level for process modeling, control and diagnosis. We demonstrate that spatially correlated variation carries a unique sparse signature in frequency domain. Based upon this observation, an efficient sparse regression algorithm is applied to accurately separate spatially correlated variation from uncorrelated random variation. An important contribution of this paper is to develop a fast numerical algorithm that reduces the computational time of sparse regression by several orders of magnitude over the traditional implementation. Our experimental results based on silicon measurement data demonstrate that the proposed sparse regression technique can capture spatially correlated variation patterns with high accuracy. The estimation error is reduced by more than 3.5 compared to other traditional methods.