Yesterday, Today, and Tomorrow of Emerging Non-volatile Memory: A Holistic and Historical View of Technologies, Designs, and Applications
Presentation Menu
This tutorial will provide attendees a holistic and historical view about technology evolutions of emerging nonvolatile memory in devices, circuit design, and architectural applications. The presenter will first briefly describe the basic physics of several important emerging nonvolatile memory technologies – magnetic memory, resistive memory, phase change memory, and ferroelectric memory, as well as some common device engineering tradeoffs. After that, the presenter will introduce several typical emerging memory cell designs that maximize the advantages of these emerging storage devices, e.g., non-volatility, multi-level cell, and small footprint, and alleviate device drawbacks like high programming current/voltage etc. The focus will be given to the circuit design development following the advance in device engineering in last two decades from a device-circuit co-design perspective. The presenter will then go through the computer architectures that have built their memory hierarchy with the emerging non-volatile memory technologies for various concerns on power, performance, and reliability. Again, some solutions that mitigate the drawbacks of emerging memories and significantly enhance the computing systems’ efficiency and robustness will be discussed in details. Finally, the presenter will give the prospect of new applications of emerging nonvolatile memory technologies in future computing systems like neuromorphic computing etc.
- Device physics and basic working mechanisms
- Device engineering for various applications
- Memory cell design tradeoffs between area, power, performance, and reliability
- Special memory cell designs to overcome the device drawbacks and multi-level cells
- Memory structures for fast access time and high endurance
- On-chip memory hierarchy: performance-driven designs
- Off-chip memory hierarchy: density-driven designs
- Applications in persistency retaining and neuromorphic computing