arXiv subjects

Jieqiong Zhang

Publications and source records attributed to Jieqiong Zhang.

4 recordsLinked to original sources

arXiv2026

ChipLingo: A Systematic Training Framework for Large Language Models in EDA

With the rapid advancement of semiconductor technology, Electronic Design Automation (EDA) has become an increasingly knowledge-intensive and document-driven engineering domain. Although large language models (LLMs) have shown strong general capabilities, applying them directly to EDA remains challenging due to limited domain expertise, cross-tool knowledge confusion, and degraded retrieval-augmented generation (RAG) performance after domain training. To address these issues, this paper presents ChipLingo, a systematic training pipeline for domain-adapted LLMs tailored to EDA scenarios. ChipLingo consists of three stages: domain corpus construction with multi-source data curation and QA augmentation, domain-adaptive pretraining with comparisons of different parameter training strategies, and instruction alignment with RAG scenario training under diverse retrieval conditions. We also curate an internal benchmark, EDA-Bench, covering representative EDA tool scenarios, with plans for public release. Experiments show that ChipLingo-8B achieves 59.7% accuracy on EDA-Bench, outperforming the same-scale base model and some larger general-purpose models. ChipLingo-32B reaches 70.02%, approaching leading closed-source commercial models. Further analysis shows that QA augmentation improves domain performance, Partial FT offers a better balance between adaptation and general capability retention than LoRA, and explicit RAG scenario training mitigates the decline in retrieval utilization after domain training. These results demonstrate the practical value of systematic domain training for knowledge-intensive EDA tasks and provide a foundation for future EDA agents and external-knowledge-driven systems.

cs.LG↗

arXiv2025

Random batch list method for metallic system with embedded atom potential

The embedded atom method (EAM) is one of the most widely used many-body, short-range potentials in molecular dynamics simulations, particularly for metallic systems. To enhance the efficiency of calculating these short-range interactions, we extend the random batch list (RBL) concept to the EAM potential, resulting in the RBL-EAM algorithm. The newly presented method introduces two "core-shell" lists for approximately computing the host electron densities and the force terms, respectively. Direct interactions are computed in the core regions, while in the shell zones a random batch list is used to reduce the number of interaction pairs, leading to significant reductions in both computational complexity and storage requirements. We provide a theoretical, unbiased estimate of the host electron densities and the force terms. Since metallic systems are Newton-pair systems, we extend the RBL-EAM algorithm to exploit this property, thereby halving the computational cost. Numerical examples, including the lattice constant, the radial distribution function, and the elastic constants, demonstrate that the RBL-EAM method significantly accelerates simulations several times without compromising accuracy.

cond-mat.mtrl-sci↗

arXiv2024

QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer

We explore the dexterous manipulation transfer problem by designing simulators. The task wishes to transfer human manipulations to dexterous robot hand simulations and is inherently difficult due to its intricate, highly-constrained, and discontinuous dynamics and the need to control a dexterous hand with a DoF to accurately replicate human manipulations. Previous approaches that optimize in high-fidelity black-box simulators or a modified one with relaxed constraints only demonstrate limited capabilities or are restricted by insufficient simulation fidelity. We introduce parameterized quasi-physical simulators and a physics curriculum to overcome these limitations. The key ideas are 1) balancing between fidelity and optimizability of the simulation via a curriculum of parameterized simulators, and 2) solving the problem in each of the simulators from the curriculum, with properties ranging from high task optimizability to high fidelity. We successfully enable a dexterous hand to track complex and diverse manipulations in high-fidelity simulated environments, boosting the success rate by 11\%+ from the best-performed baseline. The project website is available at https://meowuu7.github.io/QuasiSim/.

cs.RO↗

arXiv2019

Heat capacity and thermal expansion of metal crystalline materials based on dynamic thermal vibration

A novel approach based on dynamic thermal vibration is proposed to calculate the heat capacity and thermal expansion coefficient (TEC) for metal crystalline materials from 0K to the melting point. The motion of metal atomic clusters is decomposed into structural deformation and thermal vibration. Then thermal vibration equations are established by the fourth-order Taylor expansion of Hamiltonian at the transient structural deformation position $\bar{\mathbf{x}}$. As a result, the thermal vibration frequencies dynamically change with the structural deformation positions and temperatures. A parameter $\barδ(\bar{\mathbf{x}}, T)$ is newly introduced to illustrate how the thermal vibration frequencies vary with the temperature $T$. Besides, the modified temperature-dependent Grüneisen parameter $\bar γ(\bar{\mathbf{x}}, T)$ is given. Finally, the formulae of heat capacity and TEC for metal crystalline materials are derived from the dynamic thermal vibration frequencies and $\bar δ(\bar{\mathbf{x}}, T)$ as well as $\bar γ(\bar{\mathbf{x}}, T)$. The numerical results of heat capacity and TEC for metals Cu, Al, Au, Ag, Ni, Pd, Pt and Pb show a temperature dependence and agree well with the experimental data from 0K to the melting point. This work suggests an efficient approach to calculate thermodynamic properties of metal materials for a wide range of temperatures, up to the melting point.

cond-mat.stat-mech↗