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Jianxiang Jin

Publications and source records attributed to Jianxiang Jin.

2 recordsLinked to original sources

First Demonstration of Flip DRAM from Process, Architecture to System to Push DRAM Scaling beyond 4F2: 2F2 Self-aligned Flip Vertical Channel Transistor (FVCT) DRAM and Flip WL (FWL) 3D-DRAM

For the first time, we proposed a novel stacking technology for DRAM scaling by flipping and backside processes, making full use of DRAM wafer's backside and investigating it on both 4F2 and 3D-DRAM. For 4F2 VCT, 2F2 Flip VCT featuring self-aligned back-to-back stacked 1T1C bitcell, with various BL and WL configurations, were studied and key process modules such as self-aligned stacked vertical channel, BL and WL formations, wafer bonding and flipping, substrate thinning and low-R Co storage node (SN) were successfully developed, addressing the potential thermal, misalign and parasitic concerns in the Flip VCT process. A full DRAM DTCO framework was also established from device to mat and chip level. Compared to 4F2 VCT DRAM with the same mat size, 2F2 FVCT delivers 27.5% less parasitics, 11% better sense margin, 16.3% higher charge sharing (CS) speed and 50% less area. For 3D-DRAM, a brand-new flip WL staircase design with peripheral circuit innovations was studied and proved to have 25% density gain, 15.1% faster turn-on speed and 6.8% less CS time, proving further extendibility of flip technology on DRAM.

cond-mat.mes-hall

LiDAR-Inertial 3D SLAM with Plane Constraint for Multi-story Building

The ubiquitous planes and structural consistency are the most apparent features of indoor multi-story Buildings compared with outdoor environments. In this paper, we propose a tightly coupled LiDAR-Inertial 3D SLAM framework with plane features for the multi-story building. The framework we proposed is mainly composed of three parts: tightly coupled LiDAR-Inertial odometry, extraction of representative planes of the structure, and factor graph optimization. By building a local map and inertial measurement unit (IMU) pre-integration, we get LiDAR scan-to-local-map matching and IMU measurements, respectively. Minimize the joint cost function to obtain the LiDAR-Inertial odometry information. Once a new keyframe is added to the graph, all the planes of this keyframe that can represent structural features are extracted to find the constraint between different poses and stories. A keyframe-based factor graph is conducted with the constraint of planes, and LiDAR-Inertial odometry for keyframe poses refinement. The experimental results show that our algorithm has outstanding performance in accuracy compared with the state-of-the-art algorithms.

cs.RO