A device-level characterization approach to quantify the impacts of different random variation sources in FinFET technology

A simple device-level characterization approach to quantitatively evaluate the impacts of different random variation sources in FinFETs is proposed. The impacts of random dopant fluctuation are negligible for FinFETs with lightly doped channel, leaving metal gate granularity and line-edge roughness as the two major random variation sources. The variations of Vth induced by these two major categories are theoretically decomposed based on the distinction in physical mechanisms and their influences on different electrical characteristics. The effectiveness of the proposed method is confirmed through both TCAD simulations and experimental results. This letter can provide helpful guidelines for variation-aware technology development

GaSb Inversion-Mode PMOSFETs With Atomic-Layer-Deposited Al2O3 as Gate Dielectric

GaSb inversion-mode PMOSFETs with atomic-layer-deposited (ALD) Al2O3 as gate dielectric are demonstrated. A 0.75-mu m-gate-length device has a maximum drain current of 70 mA/mm, a transconductance of 26 mS/mm, and a hole inversion mobility of 200 cm(2)/V . s. The OFF-state performance is improved by reducing the ALD growth temperature from 300 degrees C to 200 degrees C. The measured interface trap distribution shows a low interface trap density of 2 x 10(12) /cm(2) . eV near the valence band edge. However, it increases to 1 - 4 x 10(13) /cm(2) . eV near the conduction band edge, leading to a drain current on-off ratio of 265 and a subthreshold swing of similar to 600 mV/decade. GaSb, similar to Ge, is a promising channel material for PMOSFETs due to its high bulk hole mobility, high density of states at the valence band edge, and, most importantly, its unique interface trap distribution and trap neutral level alignment

HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction

Accurate early congestion prediction can prevent unpleasant surprises at the routing stage, playing a crucial character in assisting designers to iterate faster in VLSI design cycles. In this paper, we introduce a novel strategy to fully incorporate topological and geometrical features of circuits by making several key designs in our network architecture. To be more specific, we construct two individual graphs (geometry-graph, topology-graph) with distinct edge construction schemes according to their unique properties. We then propose a dual-branch network with different encoder layers in each pathway and aggregate representations with a sophisticated fusion strategy. Our network, named HybridNet, not only provides a simple yet effective way to capture the geometric interactions of cells, but also preserves the original topological relationships in the netlist. Experimental results on the ISPD2015 benchmarks show that we achieve an improvement of 10.9% compared to previous methods

In vivo analysis of Caenorhabditis elegans noncoding RNA promoter motifs

<p>Abstract</p> <p>Background</p> <p>Noncoding RNAs (ncRNAs) play important roles in a variety of cellular processes. Characterizing the transcriptional activity of ncRNA promoters is therefore a critical step toward understanding the complex cellular roles of ncRNAs.</p> <p>Results</p> <p>Here we present an <it>in vivo </it>transcriptional analysis of three <it>C. elegans </it>ncRNA upstream motifs (UM1-3). Transcriptional activity of all three motifs has been demonstrated, and mutational analysis revealed differential contributions of different parts of each motif. We showed that upstream motif 1 (UM1) can drive the expression of green fluorescent protein (GFP), and utilized this for detailed analysis of temporal and spatial expression patterns of 5 SL2 RNAs. Upstream motifs 2 and 3 do not drive GFP expression, and termination at consecutive T runs suggests transcription by RNA polymerase III. The UM2 sequence resembles the tRNA promoter, and is actually embedded within its own short-lived, primary transcript. This is a structure which is also found at a few plant and yeast loci, and may indicate an evolutionarily very old dicistronic transcription pattern in which a tRNA serves as a promoter for an adjacent snoRNA.</p> <p>Conclusion</p> <p>The study has demonstrated that the three upstream motifs UM1-3 have promoter activity. The UM1 sequence can drive expression of GFP, which allows for the use of UM1::GFP fusion constructs to study temporal-spatial expression patterns of UM1 ncRNA loci. The UM1 loci appear to act in concert with other upstream sequences, whereas the transcriptional activities of the UM2 and UM3 are confined to the motifs themselves.</p

Collaboration Helps Camera Overtake LiDAR in 3D Detection

Camera-only 3D detection provides an economical solution with a simple configuration for localizing objects in 3D space compared to LiDAR-based detection systems. However, a major challenge lies in precise depth estimation due to the lack of direct 3D measurements in the input. Many previous methods attempt to improve depth estimation through network designs, e.g., deformable layers and larger receptive fields. This work proposes an orthogonal direction, improving the camera-only 3D detection by introducing multi-agent collaborations. Our proposed collaborative camera-only 3D detection (CoCa3D) enables agents to share complementary information with each other through communication. Meanwhile, we optimize communication efficiency by selecting the most informative cues. The shared messages from multiple viewpoints disambiguate the single-agent estimated depth and complement the occluded and long-range regions in the single-agent view. We evaluate CoCa3D in one real-world dataset and two new simulation datasets. Results show that CoCa3D improves previous SOTA performances by 44.21% on DAIR-V2X, 30.60% on OPV2V+, 12.59% on CoPerception-UAVs+ for AP@70. Our preliminary results show a potential that with sufficient collaboration, the camera might overtake LiDAR in some practical scenarios. We released the dataset and code at https://siheng-chen.github.io/dataset/CoPerception+ and https://github.com/MediaBrain-SJTU/CoCa3D.Comment: Accepted by CVPR2