Characterization of granularity and redundancy for SRAMs for optimal yield-per-area

Abstract

Memories are significant proportions of most digital systems and memory-intensive chips continue to lead the migration to new nano-fabrication processes. As these processes have increasingly higher defect rates, especially when they are first adopted, such early migration necessitates the use of increasing levels of redundancy to obtain high yield (per area). We show that as we move into nanometer processes with high defect rates, the level of redundancy needed to optimize yield-per-area is sufficiently high so as to significantly influence design tradeoffs. We then report a first step towards considering the overheads of redundancy during design optimization by characterizing the tradeoffs between the granularity of a design and the level of redundancy that optimizes the yield-per-area of static RAMs (SRAMs). Starting with physical layouts of cells and the desired memory size, we derive probabilities of failure at a range of abstractions - transistor level, cell level, and system level. We then estimate optimal memory granularity, i.e., the size of memory blocks, as well as the optimal number of spare rows and columns that maximize yield-per-area. In particular, we demonstrate the non-monotonic nature of these tradeoffs and present efficient designs for large SRAMs. Our ongoing research is characterizing several other specific tradeoffs, for SRAMs as well as logic blocks.