Sang-Sun Kim

TL;DR: This paper presents a 16Gb 18Gb/s/pin GDDR6 DRAM with a die architecture and high-speed circuit techniques on 1.35V DRAM process and introduces a dual channel for a data granularity of 32B with a BL16, per-bit training of l/REF, and an equalizer with PLL-less clocking.

TL;DR: A 2Gb LPDDR4 SDRAM with 0.15mW standby mode power is presented, which is 66% lower than the standby power for a memory of the same density and achieves a bandwidth of 3.733Gb/s/pin.

TL;DR: This paper introduces 16-Gb GDDR6 DRAM with a per-bit trainable single-ended decision feedback equalizer (DFE), a reference impedance (ZQ)-coded transmitter, and a phase-locked loop (PLL)-less clocking to overcome I/O speed limitation by the DRAM process.

Abstract: This paper focuses on overcoming the problem of tracking control in structurally flexible redundant manipulators by utilizing their self-motion capabilities. In the proposed algorithm, the self-motion is evaluated in order to nullify the dominant modal force of flexural motion that is induced by a rigid body motion.The flexure motions of manipulators, which are induced by joint motion, cause undesired inaccuracy in end-effector tracking. In-plath planning states, joint trajectories are so designed as not to excite but to damp out the flexure motions. The self-motion, inherent in redundant manipulators, can alter joint motion, influencing the flexure motion (by exciting and damping the flexure modes), while not affecting end-effector motion at all. Therefore, the self-motion can be utilized to regulate flexibility and effectively reduce the end-effector tracking error.The effectiveness and applicability of the proposed algorithm have been demonstrated through numerical simulation with three-link planar robotic manipulators possessing flexible links.