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FAST: A Framework for Simulation and Analysis of Large-Scale Protein-Silicon Biosensor Circuits

机译:FAST:大规模蛋白质-硅生物传感器电路的仿真和分析框架

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摘要

This paper presents a computer aided design (CAD) framework for verification and reliability analysis of protein-silicon hybrid circuits used in biosensors. It is envisioned that similar to integrated circuit (IC) CAD design tools, the proposed framework will be useful for system level optimization of biosensors and for discovery of new sensing modalities without resorting to laborious fabrication and experimental procedures. The framework referred to as FAST analyzes protein-based circuits by solving inverse problems involving stochastic functional elements that admit non-linear relationships between different circuit variables. In this regard, FAST uses a factor-graph netlist as a user interface and solving the inverse problem entails passing messages/signals between the internal nodes of the netlist. Stochastic analysis techniques like density evolution are used to understand the dynamics of the circuit and estimate the reliability of the solution. As an example, we present a complete design flow using FAST for synthesis, analysis and verification of our previously reported conductometric immunoassay that uses antibody-based circuits to implement forward error-correction (FEC).
机译:本文提出了一种计算机辅助设计(CAD)框架,用于生物传感器中蛋白质-硅混合电路的验证和可靠性分析。可以预见,与集成电路(IC)CAD设计工具类似,所提出的框架将可用于生物传感器的系统级优化和发现新的传感方式,而无需进行繁琐的制造和实验程序。称为FAST的框架通过解决涉及随机功能元素的逆问题来分析基于蛋白质的电路,这些功能元素允许不同电路变量之间存在非线性关系。在这方面,FAST使用因子图网表作为用户界面,解决反问题需要在网表的内部节点之间传递消息/信号。诸如密度演化之类的随机分析技术可用于了解电路的动态并估算解决方案的可靠性。例如,我们介绍了一个完整的设计流程,该流程使用FAST进行合成,分析和验证我们以前报道的电导免疫测定,该测定使用基于抗体的电路来实现正向误差校正(FEC)。

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