R&D: SSD Thermal Throttling Prediction Using Improved Fast Prediction Model

IEEE Xplore has published, in 2019 18th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (Itherm) proceedings, an article written by Hedan Zhang, Western Digital, Packaging/Assembly Engineering, Milpitas, CA, 95035, USA, Ernold Thompson, Western Digital, Bengaluru, Karnataka, 560103, India, Ning Ye, Western Digital, Packaging/Assembly Engineering, Milpitas, CA, 95035, USA, Dror Nissim, Steve Chi, Western Digital, Hardware Development Engineering, Milpitas, CA, 95035, USA, and Hem Takiar, Western Digital, Packaging/Assembly Engineering, Milpitas, CA, 95035, USA.

Abstract: “Thermal management is very challenging for Solid State Drive (SSD) product especially for M.2 due to its small form factor and high energy density. A better understanding of thermal prediction of SSD can help optimize performance and avoid thermal runaway issue. Using traditional Computational Fluid Dynamics (CFD) simulation approach to predict temperature under user defined power input is very time consuming and tedious. For example, to predict a typical thermal throttling profile lasting three cycles, for CFD simulation it could take from days to a week to finish. CFD approach is not favorable to be incorporated into SSD performance simulator either. Therefore, a fast, robust, and easy to use thermal prediction model for SSD is becoming more and more critical to meet these challenges. This paper has used an earlier fast prediction model methodology with modified thermal impedance using partial fraction circuit. For a recent M.2 SSD product model, constant pairs can be extracted accordingly for NAND when powering one package only at a time. R-square values have been shown to be good in curve fitting. A detailed SSD in a laptop environment CFD model has been established and validated through experiment. Constant pairs were extracted from the CFD simulation results. Temperature profile using these parameters was perfectly aligned with CFD results with R-square values close to one. Using linear superposition principle, the predicted temperature profile was compared with CFD results under arbitrary cycling power mode for verification. Result consistency is within expected range. This approach was then applied to predict M.2 SSD thermal throttling for 1000 seconds. Different throttling temperatures have been studied. Results have shown that increasing temperature threshold for both upper and lower limit may help improve SSD thermal performance.“