Yield estimation via multi-cones

We propose a new yield estimation algorithm which estimates the acceptability region as the union of spherical cones. The algorithm works by dividing the input parameter space into approximately equi-probable cones, efficiently estimating the refined weight contributions for each cone, then combining the results to get the total yield. The algorithm is broadly similar to the worst-case-distances method, but is more generally applicable for cases with - for example - multiple failure regions. The algorithm is quite accurate, and offers several orders (>;100×) of magnitude of speedup compared to traditional Monte Carlo. The paper includes example applications to difficult high-yield circuits like SRAM.