Parametric failure analysis of embedded SRAMs using fast & accurate dynamic analysis

Increased die-to-die and on-die variations in scaled technologies can lead to parametric failures (Read/Write/Access) in embedded SRAMs. Conventionally, SRAM bit-cell failure analysis is based on the Static Noise Margin (SNM), a metric that leads to conservative estimate of design yield. In this paper we present a method of dynamic noise margin (DNM) estimation based on the modeling technique developed that can efficiently estimate failures in bit-cells under parameter variations. The proposed DNM estimation method is fast, and can accurately estimate the SRAM yield. Monte Carlo simulation results show that the proposed DNM closely matches the results from SPICE analysis.