March 3, 2016
Food sponsored by I.C.E. Labs, ISO 9001 & 17025 Reliability Test Lab
Title: Physics- based life distribution and reliability modeling of solid state drives
Invited Speaker: Dr. Alexander Parkhomovsky, Ph.D., Engineering Development Manager at Lumentum
Date: Thursday, March 3, 2016
Time: Check in and food at 6:00PM – 6:30 PM. Presentation from 6:30 PM to 7:30 PM
Location: Qualcomm Inc., 3165 Kifer Rd, Santa Clara, CA, 95051 (Meeting will be in the cafeteria, Building B)
Admission: Open to all IEEE members and non-members
Abstract: The model of solid state drive (SSD) life time distribution from physics-based life model considering the random nature of real world customer data usage and product inherent physical properties is developed. The talk is focused on the following two cases:
Case 1: When only field write duty cycle is treated as a random variable while assuming all other physical characteristics are non-random, it is found that the SSD life time follows . Reciprocal-Weibull distribution when field Write Duty Cycle follows Weibull distribution, . Reciprocal-Exponential distribution when field Write Duty Cycle follows Exponential distribution, . Lognormal distribution when field Write Duty Cycle follows Lognormal distribution, . Reciprocal-Normal Distribution when field Write Duty Cycle follows Normal distribution. The corresponding mathematical expressions for reliability, unreliability, hazard rate, MTTF, etc. are derived for each scenario accordingly.
Case 2: In real world, SSD endurance rating is also a random variable due to part-to-part variance from material in-homogeneity and inherent defects from manufacturing process. Given the distributions of field customer write duty cycle (stress) and SSD endurance rating (strength), the distribution of lifetime random variable can be derived either analytically, if closed form solution exists, or numerically using Monte Carlo simulation if no closed form solution exists. This paper provides a special case where the analytic solution exists when both random variables follow Lognormal distribution. A numerical example is given to show the application of the models developed in this paper. The results derived in this paper will benefit the SSD industry in various aspects of product design, development, reliability testing and prediction, field return/failure estimation and warranty management.
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