Measurement and analysis of soft error vulnerability of low-voltage logic and memory circuits

Scaling the supply voltage into the sub/near-threshold domain is one of the most effective methods for improving the energy efficiency of next-generation electronic microsystems. Unfortunately, the relationship between low-voltage operation and radiation-induced soft error rate is not widely known, as little research has been previously performed and reported for soft-error susceptibility of on-chip memory and logic at very low supply voltages. This information is critical for low-voltage circuit designers, as many applications that would benefit from the energy effi­ciency of sub/near-threshold also require high reliability. This work first details the design and implementation of a portable soft error reference platform, specif­ically targeting very low-voltage operation. The circuit-level details of a TSMC 65nm test-chip design are given, along with an analysis of data from experiments performed at Los Alamos Neutron Science Center (LANSCE) and the OSU Radi­ation Center. Once this soft-error rate is known, error resiliency techniques must be utilized for increased processor reliability. The design and implementation of an error-resilient, near-threshold SIMD processor in an IBM 45nm SOI process will also be covered. This prototype demonstrates both increased reliability and improved throughput over a conventional SIMD pipeline while operating in near-threshold

SEU Characterization of Three Successive Generations of COTS SRAMs at Ultralow Bias Voltage to 14.2 MeV Neutrons

This paper presents a SEU sensitivity characterization at ultra-low bias voltage of three generations of COTS SRAMs manufactured in 130 nm, 90 nm and 65 nm CMOS processes. For this purpose, radiation tests with 14.2 MeV neutrons were performed for SRAM power supplies ranging from 0.5 V to 3.15 V. The experimental results yielded clear evidences of the SEU sensitivity increase at very low bias voltages. These results have been cross-checked with predictions issued from the modeling tool MUlti-SCAles Single Event Phenomena Predictive Platform (MUSCA-SEP3). Large-scale SELs and SEFIs, observed in the 90-nm and 130-nm SRAMs respectively, are also presented and discussed