Demo Data from EDBT 2026 / reviewers in the wild / expert
Jui-Feng Kuan
dblp:15/11467
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1
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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Integrated circuit design · 67% Electronic design automation · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Integrated circuit design
analog and mixed-signal circuits |
0.1 | 1 | 2012 | Efficient trimmed-sample Monte Carlo methodology and yield-aware design flow for analog circuits · DAC 2012 |
Electronic design automation
analog circuit design automation |
0.1 | 1 | 2012 | Efficient trimmed-sample Monte Carlo methodology and yield-aware design flow for analog circuits · DAC 2012 |
Integrated circuit design › analog and mixed-signal circuits
analog circuit yield estimation |
0.1 | 1 | 2012 | Efficient trimmed-sample Monte Carlo methodology and yield-aware design flow for analog circuits · DAC 2012 |
Methods — techniques the papers use, named apart from their topics
variance reduction · 0.1trimmed-sample monte carlo · 0.1latin hypercube sampling · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Efficient trimmed-sample Monte Carlo methodology and yield-aware design flow for analog circuitsabstractThis paper proposes efficient trimmed-sample Monte Carlo (TSMC) methodology and novel yield-aware design flow for analog circuits. This approach focuses on "trimming simulation samples" to speedup MC analysis. The best possible yield and the worst performance are provided "before" MC simulations such that designers can stop MC analysis and start improving circuits earlier. Moreover, this work can combine with variance reduction techniques or low discrepancy sequences to reduce the MC simulation cost further. Using Latin Hypercube Sampling as an example, this approach gives 29x to 54x speedup over traditional MC analysis and the yield estimation errors are all smaller than 1%. For analog system designs, the proposed flow is still efficient for high-level MC analysis, as demonstrated by a PLL system. Chin-Cheng Kuo, Wei-Yi Hu, Yi-Hung Chen, Jui-Feng Kuan, Yi-Kan Cheng |
DAC | 4 |