EDBT 2026 Demo / reviewers in the wild / expert
Jian Xin
dblp:348/3368
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Electronic design automation · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Electronic design automation
circuit simulation |
0.9 | 1 | 2025 | PiSPICE: Accelerating Post-Layout SPICE Simulation via Essential Parasitic Identification · DAC 2025 |
Electronic design automation › physical design
parasitic extraction |
0.9 | 1 | 2025 | PiSPICE: Accelerating Post-Layout SPICE Simulation via Essential Parasitic Identification · DAC 2025 |
Electronic design automation › circuit simulation
post-layout simulation |
0.9 | 1 | 2025 | PiSPICE: Accelerating Post-Layout SPICE Simulation via Essential Parasitic Identification · DAC 2025 |
Electronic design automation › circuit simulation › analog circuit simulation
SPICE simulation |
0.9 | 1 | 2025 | PiSPICE: Accelerating Post-Layout SPICE Simulation via Essential Parasitic Identification · DAC 2025 |
Electronic design automation › circuit simulation
model order reduction |
0.3 | 1 | 2025 | PiSPICE: Accelerating Post-Layout SPICE Simulation via Essential Parasitic Identification · DAC 2025 |
Methods — techniques the papers use, named apart from their topics
adjoint sensitivity analysis · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | PiSPICE: Accelerating Post-Layout SPICE Simulation via Essential Parasitic IdentificationabstractAs process nodes scale to more advanced technologies, post-layout simulations for integrated circuits have become increasingly complex, involving billions to trillions of nodes. The growing design complexity and transistor integration require more accurate and efficient post-layout SPICE simulations. However, existing methods for solving large-scale post-layout circuits face significant challenges due to high computational costs. In this paper, we propose a new approach, PiSPICE, which utilizes adjoint sensitivity analysis to identify critical parasitics and eliminate non-critical ones, effectively reducing the simulation scale and improving speed. By modeling parasitics and performing sensitivity analysis on pre-layout circuits, we significantly reduce the computational burden and avoid the overhead of directly analyzing sensitivities in large-scale postlayout circuits. By retaining only the critical parasitics and applying model order reduction to minimize their impact, while eliminating non-critical parasitics, PiSPICE achieves a speedup of up to 17.27 x in simulation with an error margin of less than 0.78% compared to the commercial simulator Spectre. Jian Xin, Tianjia Zhou, Dan Niu, Zuochang Ye |
DAC | 3 |