When analytic yield-evaluation methods for fault tolerant systems are being considered, the question of their capacity to represent and conform to reality soon becomes apparent. The goodness of analytic yield-evaluation methods depends on their ability to account for the relationship between basic and faulty components and the reconfiguration strategy (R-S). With regards to Redundant Programmable Logic Arrays (RPLA), two RS have been proposed in the literature: Static RS: the diagnosis and the reconfiguration phases are performed independently, Dynamic R-S: the diagnosis and the reconfiguration phases, for some kind of faults, are performed simultaneously. This paper highlights the necessity to model the: relationship between faulty and basic components, and adopted R-S, in order to achieve a realistic yield evaluation. We show that the yield evaluation method used in the literature for two RS for Fault Tolerant RPLA is unrealistic; we propose to use two analytic yield-evaluation methods, each of which is adopted for a specific R-S. These two methods are based on Fault Pattern statistics, and are: Markov Based Method (MBM) Fault Pattern & Reconfiguration Method (FP&RM). They model the steps implemented in the R-S. Extensive Monte Carlo based simulation experiments validate the analytic approach. We present two comparisons: 1. Qualitative: between realistic yield evaluation methods and the yield evaluation method of RPLA proposed in the literature; 2. Quantitative: between the static R-S and dynamic R-S. Comparison #1 shows the inadequacy of the old approach and the necessity to use the new ones. Comparison #2, between the two R-S which are evaluated by the two new methods, show that the static R-S gives the best benefit/cost index compared to the dynamic ones.
