The Revolutionary Technology Center (RTC) of SK hynix R&D will be showcasing its research leadership in memory extention, value-added position, and memory-centric computation at the 14th International Memory Workshop (IMW) from May 15th to 18th, 2022 in Dresden, Germany. The IMW, sponsored by the IEEE Electron Devices Society, brings the memory community together in a workshop environment to discuss memory process and design technologies, applications, market needs, and strategies. Be sure to take a look at the most-viewed papers on the RTC website. Read more

“Memory Window Expansion for Ferroelectric FET-Based Multilevel NVM: Hybrid Solution with Combination of Polarization and Injected Charges” by Jae-Gil Lee, Ph.D. 

In this paper, a memory window-boosting model and scheme are quantitatively demonstrated as a potential solution to implementing a multi-level bit operation that can accelerate ferroelectric memory application as a highly scalable non-volatile memory in future computing systems.  To see more

“Structural and Device Considerations for Vertical Cross Point Memory with Single-Stack Memory toward CXL Memory Beyond 1x nm 3DXP” by Sijung Yoo, Ph.D. 

In this paper, a structural breakthrough is proposed for ultimate cost-competitive and value-added memory solution in heterogeneous computing systems. It can serve as a future solution to overcoming the scaling and stacking limitations of the conventional 3D XP memory.  To see more

“Highly Stackable 3D Ferroelectric NAND Devices: Beyond the Charge Trap-Based Memory” by Sunghyun Yoon, Ph.D. 

In this paper, a 3D type new storage device, including cell pitch-scalable and stackable structure, is successfully demonstrated as a strong candidate for the next generation 3D NAND storage. The proposed 3D ferroelectric NAND shows a potential multi-level cell operation with the 3.4 V program/erase window.  To see more

“Recognition Accuracy Enhancement Using Interface Control with Weight Variation-Lowering in Analog Computation-in-Memory” by Sangsu Park. Ph.D. 

Resistive synaptic devices successfully mimic the function of biological synapses in the human brain. This technology will accelerate the transition to memory-centric computation for energy-efficient AI processing. The A-CiM including resistive synaptic matrix can be a stepping stone for entering the post-von Neumann Era.  To see more