PhyBolt integrates high-Precision power and thermal simulation. Its signoff-level engine computes average and peak power for specific scenarios, aiding power hotspot identification and fixes. Power results directly feed into thermal simulation for seamless data transfer. The thermal analysis includes optimized meshing and solvers for chip-to-package heat transfer. A unique power modeling technology abstracts intermediate results into efficient models, dynamically adjusting for temperature, voltage, and frequency to boost co-simulation efficiency.

Unique Power Modeling Technology:
PhyBolt employs an innovative power modeling approach that significantly enhances simulation efficiency and accuracy. By reading library files (.lib) under multiple temperature (T) and voltage (V) conditions, the tool abstracts intermediate power calculation results into a highly efficient mathematical model. This model allows rapid substitution of arbitrary voltage, temperature, and frequency (V/T/F) parameters, enabling instantaneous power evaluation under diverse operating conditions. Compared to full simulations, this method improves computational speed by two orders of magnitude. Additionally, PhyBolt supports the construction of activity mode-specific power models based on multiple toggle information files, ensuring comprehensive coverage of diverse operational scenarios. This capability allows designers to accurately simulate power consumption across various working modes without repetitive full-scale analyses.

Advanced Packaging Thermal Simulation:
Custom meshing and solvers for 2.5D/3DIC structures, importing package data, materials, and boundaries for system-level thermal feasibility analysis. High-accuracy modeling for TSV, Micro Bump, and Hybrid Bonding balances precision and efficiency, offering 10-30x speedup over traditional methods.

Integrated Power-Thermal Platform:
PhyBolt offers a unified command interface that complies with Python syntax, enabling seamless integration of power and thermal simulation processes within a cohesive workflow. The Python-based open architecture facilitates effortless functional extensions, empowering users to perform advanced operations such as:
·Back-annotating power calculations based on thermal simulation results
·Investigating power-temperature coupling relationships under various operating conditions
·Evaluating the practical effectiveness of dynamic control algorithms like DVFS
This approach achieves comprehensive and controllable multi-physics domain closed-loop verification.



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