Machine Learning Assisted Multi-Functional Graphene-Based Harmonic Sensors
Machine Learning Assisted Multi-Functional Graphene-Based Harmonic Sensors
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DOI：
10.1109/jsen.2020.3046455
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
M. Hajizadegan;M. Sakhdari;Samira Abbasi;Pai-Yen Chen
中科院分区：
文献类型：
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作者：
M. Hajizadegan;M. Sakhdari;Samira Abbasi;Pai-Yen Chen
实时监测多种（生物）化学试剂、分子和气体的需求很高，特别是对于通过5G生态系统连接的未来物联网（IoT）和即时检测（POCT）。在这里，我们提出了一种基于石墨烯场效应晶体管（GFET）电路的轻量级多代理（生物）化学无线传感器，利用GFET的双重功能，即，频率调制和（生物）化学传感。基于GFET的射频（RF）调制器电路可以将连续波（CW）单调信号转换为多个谐波，其中转换效率敏感地取决于（生物）化学试剂的密度。具体而言，我们利用基于机器学习（ML）的读出方法从谐波光谱中提取（生物）化学掺杂剂的浓度水平。此外，我们表明，通过增加GFET电路的阶数，从而可检测到的谐波的数量，神经网络的性能和整体读出精度可以提高。所提出的基于GFET的无线传感器可以是超紧凑、超小型、便携和灵活的，因此可能有利于物联网、POCT和工业4.0中的广泛应用。
Real-time monitoring of multiple (bio)chemical agents, molecules and gases is in a high demand, particularly for the future internet-of-things (IoTs) and point-of-care tests (POCT), that are connected via the 5G ecosystem. Here, we propose a lightweight, multi-agent (bio)-chemical wireless sensor based on graphene field-effect transistor (GFET) circuits, taking advantage of GFET’s dual functionalities, i.e., frequency modulation and (bio-)chemical sensing. The GFET-based radio-frequency (RF) modulators circuits can convert the continuous wave (CW) monotonic signal to multiple harmonics, with conversion efficiencies sensitively depending on densities of (bio-)chemical agents. Specifically, we exploit a machine learning (ML)-based readout method to extract the concentration levels of the (bio-)chemical dopants from the harmonic spectrum. Further, we show that by increasing the order of GFET circuits and thus the number of detectable harmonics, the neural network performance and the overall readout accuracy can be enhanced. The proposed GFET-based wireless sensor could be ultracompact, ultralow-profile, portable and flexible, thus potentially benefiting a wide range of applications in IoTs, POCTs, and Industry 4.0.