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Eric Leong

dblp:73/10849 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Autonomous driving · 50% Robot navigation and mapping · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
dynamic environments
0.612022
Context-Aware Streaming Perception in Dynamic Environments · ECCV (38) 2022
Robotics › Autonomous driving › perception › perception systems
streaming perception
0.612022
Context-Aware Streaming Perception in Dynamic Environments · ECCV (38) 2022
YearPublicationVenuePosition
2022 Context-Aware Streaming Perception in Dynamic Environments
Gur-Eyal Sela, Ionel Gog, Justin Wong, Kumar Krishna Agrawal, Xiangxi Mo, Sukrit Kalra, Peter Schafhalter, Eric Leong, Xin Wang 0066, Bharathan Balaji, Joseph Gonzalez 0001, Ion Stoica
ECCV (38)8
2010 Mask versus Schematic - an enhanced design-verification flow for first silicon success
abstract
Layout versus Schematic (LVS) is a commonly used technique employed at the design stage to insure the correctness of physical layout. However, as process technologies continually advance, increasingly complex boolean operations are required to produce the desired on-mask patterns, which are frequently optimized to enhance transistor performance and process margin. Design layout which has been verified by LVS may undergo substantial layout changes when subjected to the mask generation booleans, with potential implications for performance and margin estimation, particularly given the aggressive use of stressors in modern CMOS technologies. Errors in mask generation booleans, which are very difficult to detect by present primitive inspection methods, can easily result in functional failure although the initial LVS predicted success. Therefore, LVS performed at the design stage is no longer an iron-clad guarantee of chip functionality in advanced process technologies. In this paper, we introduce Mask-versus-Schematic (MVS) verification, a novel design verification flow which directly compares the schematic netlist with a netlist extracted after application of all mask generation booleans, in order to insure the correctness of the final mask data just before tapeout. Furthermore, the introduced methodology can be performed using currently available physical verification EDA tools. The experimental results presented here, using examples from some of the industry's most advanced process technology nodes, demonstrate the effectiveness and efficiency of this methodology in detecting errors resulting from mask generation boolean operations.
Tseng-Chin Luo, Eric Leong, Mango Chia-Tso Chao, Philip A. Fisher, Wen-Hsiang Chang
ITC2