Analysis and mitigation of process variation impacts on Power-Attack Tolerance

Embedded cryptosystems show increased vulnerabilities to implementation attacks such as power analysis. CMOS technology trends are causing increased process variations which impact the data-dependent power of deep submicron cryptosystem designs. In this paper, we use Monte Carlo methods in SPICE circuit simulations to analyze the statistical properties of the data-dependent power with predictive 45 nm CMOS device and ITRS process variation models. In addition to the "measurement to disclosure" (MTD) used, we define a lower level metric, power-attack tolerance (PAT), to model both dynamic power and leakage power data-dependence. We show that the PAT of a typical cryptographic component implementation using CMOS standard-cells can significantly deteriorate due to process variations, thus increasing the component\´s vulnerability to power attacks. Power-attack-resistant logic styles (e.g. SABL) have been developed which increase PAT by an order of magnitude by balancing power consumption at the gate level with considerable overhead. However in the presence of process variations, the degradation probability of MTD is 57%. To mitigate this problem, we demonstrate a transistor sizing optimization method that can reduce such negative impacts to only 18% with minimal power and area overhead.