用于新型冠状病毒检测的cmos兼容微pcr芯片快速设计优化的新型紧凑模型

Zongqin Ke, Huahuang Luo, Hadi Tavakkoli, Wenhao Chen, Zhaojun Liu, Yi-Kuen Lee
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引用次数: 0

摘要

本文首次提出了一种基于非线性偏微分方程(NL PDE)的紧凑模型,用于预测cmos兼容微PCR(聚合酶链反应)芯片的瞬态热行为,用于快速优化器件。首先用平均误差为0.4%的实验数据对模型进行验证,然后探讨关键参数对微PCR设计的影响。根据参数标度分析,两个关键因素——微PCR加热器的厚度和宽度——对功率效率、加热速率和冷却速率等性能表现出主要影响。由于我们的紧凑模型计算成本低,可以在10秒内进行设计优化,比典型的FEM模拟快约170倍。经过有效优化后,加热速率$(Q_{h})$和冷却速率$(Q_{c})$分别提高到$6.347^{\circ}\ mathm {c} /\ mathm {s}$和2.159 $^{\circ}\ mathm {c} /\ mathm {s}$,与初始设计相比,在相同工况下分别显著提高了799.47%和166.23%。总之,经过验证的紧凑模型将有望用于采用台积电$0.18\mu\ mathm {m}$ CMOS/CMOS MEMS代工工艺的下一代CMOS微型PCR设备,用于COVID-19检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel Compact Model for Rapid Design Optimization of CMOS-compatible Micro-PCR chips for COVID-19 Detection
For the first time, an NL PDE (nonlinear partial differential equation)-based compact model to predict the transient thermal behavior of a CMOS-compatible micro PCR (polymerase chain reaction) chip is proposed for rapid device optimization. The model is first validated using experimental data with an average error of 0.4% and then employed to explore the effect of crucial parameters on micro PCR design. According to the parametric scaling analysis, two critical factors - the thickness and the width of micro PCR heaters - show dominant impacts on the performance, including power efficiency, heating rate, and cooling rate. Due to the low computational cost of our compact model, design optimization can be conducted within 10 seconds, approximately 170 times faster than that with typical FEM simulation. After the effective optimization, the heating rate $(Q_{h})$ and cooling rate $(Q_{c})$ improved to $6.347^{\circ}\mathrm{C}/\mathrm{s}$ and 2.159 $^{\circ}\mathrm{C}/\mathrm{s}$, resulting in a significant increase of 799.47% and 166.23%, respectively, compared to the initial design under the identical working conditions. In conclusion, the validated compact model will be promising to be used for next-gen CMOS micro PCR devices using TSMC $0.18\mu\mathrm{m}$ CMOS/CMOS MEMS foundry processes for COVID-19 detection.
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