Error Characterization and Correction Techniques for Reliable STT-RAM Designs

The concerns on the continuous scaling of mainstream memory technologies have motivated tremendous investment to emerging memories. Being a promising candidate, spin-transfer torque random access memory (STT-RAM) offers nanosecond access time comparable to SRAM, high integration density close to DRAM, non-volatility as Flash memory, and good scalability. It is well positioned as the replacement of SRAM and DRAM for on-chip cache and main memory applications. However, reliability issue continues being one of the major challenges in STT-RAM memory designs due to the process variations and unique thermal fluctuations, i.e., the stochastic resistance switching property of magnetic devices. In this dissertation, I decoupled the reliability issues as following three-folds: First, the characterization of STT-RAM operation errors often require expensive Monte-Carlo runs with hybrid magnetic-CMOS simulation steps, making it impracticable for architects and system designs; Second, the state of the art does not have sufficiently understanding on the unique reliability issue of STT-RAM, and conventional error correction codes (ECCs) cannot efficiently handle such errors; Third, while the information density of STT-RAM can be boosted by multi-level cell (MLC) design, the more prominent reliability concerns and the complicated access mechanism greatly limit its applications in memory subsystems. Thus, I present a novel through solution set to both characterize and tackle the above reliability challenges in STT-RAM designs. In the first part of the dissertation, I introduce a new characterization method that can accurately and efficiently capture the multi-variable design metrics of STT-RAM cells; Second, a novel ECC scheme, namely, content-dependent ECC (CD-ECC), is developed to combat the characterized asymmetric errors of STT-RAM at 0->1 and 1->0 bit flipping's; Third, I present a circuit-architecture design, namely state-restricted multi-level cell (SR-MLC) STT-RAM design, which simultaneously achieves high information density, good storage reliability and fast write speed, making MLC STT-RAM accessible for system designers under current technology node. Finally, I conclude that efficient robust (or ECC) designs for STT-RAM require a deep holistic understanding on three different levels-device, circuit and architecture. Innovative ECC schemes and their architectural applications, still deserve serious research and investigation in the near future

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

A DATA AWARE APPROACH TO SALVAGE THE ENDURANCE OF PHASE-CHANGE MEMORY

Phase Change Memory (PCM) is an emerging non-volatile memory technology that could either replace or augment DRAM and NAND flash that are hindered by scalability challenges. PCM suffers from a limited endurance problem that needs to be alleviated before it can be endorsed into the memory stack. This thesis is based on the observation that the endurance problem and its ramification depend on the write data. Accordingly, a data-aware approach is applied to salvage the endurance of PCM at three different stages: pre-write fault avoidance, post-write fault tolerance and post-failure recovery. The pre-write fault avoidance stage aims at reducing the endurance cost of servicing write requests. To this end, Cost Aware Flip Optimization (CAFO) is presented as an efficient technique to lessen the endurance degradation. Essentially, CAFO relies on a cost model that captures the endurance cost of programming memory cells based on their already stored values. Subsequently,the write data is encoded into a form that incurs a lower endurance cost than the original write data. Overall, CAFO is capable of reducing the endurance cost by up to 65% more than the existing schemes. Worn out PCM cells exhibit a stuck-at fault model which makes the manifestation of errors dependent on the values that cells are stuck at. When a write fails, the data is rewritten inverted. This dissertation shows that applying data inversion at the post-write fault tolerance stage exploits the data dependent nature of errors which enables ECCs to tolerate faults up to double their nominal capability. Furthermore, extensions to RDIS which is an ECC designed specifically for the stuck-at fault model are presented. At the post-failure recovery stage, Data Dependent Sparing is presented to manage bad blocks in PCM. Departing from the observation that defective blocks in the context of the stuck-at fault model still exhibit a low write failure probability due to the data dependent nature of errors, this thesis takes the approach of reusing blocks that are defective yet better-than-bad through a dynamic management of the reserve spare space. Data Dependent Sparing is capable of increasing the lifetime of PCM by up to 18%