Ayswarya Rajagopalan | Principal Engineer, Reliability Engineering
Sandisk
Ayswarya Rajagopalan is a Principal Engineer in Reliability Engineering with deep expertise in solid‑state drives (SSDs). She leads the reliability execution team, owning end‑to‑end validation—from test infrastructure and testing methodology to execution, data analytics, and product qualification—ensuring robust field performance and faster time‑to‑market.
A champion of methodical validation and cross‑functional collaboration, Ayswarya has shaped SSD test strategies that strengthen quality and reduce risk across complex programs. Beyond her engineering leadership, she serves as a RAMS (Reliability & Maintainability Symposium) Associate Editor for 2025–2027, contributing to the community through technical reviews and thought leadership on reliability modeling, accelerated testing, and systems assurance.
Appearances:
Future of Memory and Storage - Day 1 @ 10:10
Predicting the SSD Reliability During End of Product Life Cycle
This study investigates the long-term reliability of Solid-State Drives (SSDs) for end of product life. The aim is to understand the performance of SSDs in the field once and to quantify the risks associated with extended operation. By evaluating wear-out indicators and failure modes, the work provides insight into how to build the reliability prediction model. The study incorporates physics-based life modeling techniques commonly applied in SSD reliability analysis, such as those described in SSD lifetime modeling research. We also leverage methodologies inspired by Life test, Reliability Demonstration Testing (RDT), and On-Going Reliability Test (ORT) used in broader SSD qualification frameworks. Various simulation models are applied to represent drive behavior under prolonged stress conditions, enabling prediction of SSD during end of product life cycle. These models help identify which factors—such as workloads, endurance rating variance, and environmental stress—most significantly affect life of an SSD. Ultimately, the study highlights how accurately we can predict SSD usage in the field.
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