In this work, a novel sensing structure based on Au nanoparticles/HfO 2 /fully depleted silicon-oninsulator (AuNPs/HfO 2 /FDSOI) MOSFET is fabricated. Using such a planar double gate MOSFET, the electrostatic enrichment (ESE) process is proposed for the ultrasensitive and rapid detection of the coronavirus disease 2019 (COVID-19) ORF1ab gene. The back-gate (BG) bias can induce the required electric field that enables the ESE process in the testing liquid analyte with indirect contact with the top-Si layer. It is revealed that the ESE process can rapidly and effectively accumulate ORF1ab genes close to the HfO 2 surface, which can significantly change the MOS-FET threshold voltage (V th ). The proposed MOSFET successfully demonstrates the detection of zeptomole (zM) COVID-19 ORF1ab gene with an ultralow detection limit down to 67 zM (∼0.04 copy/μL) for a test time of less than 15 min even in a high ionic-strength solution. Besides, the quantitative dependence of V th variation on COVID-19 ORF1ab gene concentration from 200 zM to 100 femtomole is also revealed, which is further confirmed by TCAD simulation.

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An enhanced operational amplifier (OPAMP) macro model based on artificial neural network (ANN) is developed for analog circuit simulation. The model uses ANN to capture the relation between the inputs and outputs (currents and voltages) of the circuit module. Both direct current (DC) and alternative current (AC) signals are considered in the model. By adopting the adaptive sampling algorithm, the amount of data required for model training can be significantly reduced and the accuracy of model fitting can be apparently improved. The model is also validated by the simulated data from OPAMP, Bandgap, and LDO circuits. Compared with SPICE model, enhanced ANN OPAMP macro model has nearly 8 times faster simulation speed without apparently degrading the model accuracy. The predictions made by the neural network are also compared to the experimental measurement results of LDO circuit fabricated in 0.18‐μm process. Both simulation and experimental results show the feasibility and accuracy of the proposed enhanced ANN OPAMP macro model.

SummaryA novel unified black‐box macro model for analog circuits is presented. This black‐box macro model enables the creation of a high‐accuracy DC and AC macro model by modeling the port voltages and currents of analog circuits using an artificial neural network (ANN). The modeling process for different analog circuit blocks is the same, as the model can be modeled by extracting only the port values. The operational amplifier (OPAMP), which is an important module of the analog circuit, is taken as an example for the verification of the model proposed in this work. The black‐box macro model of OPAMP is used for DC, AC, and transient simulations. The simulation speed of the black‐box macro model is much better than that of the transistor‐level model, and the trade‐off between accuracy and speed can be freely adjusted. The black‐box macro model of OPAMP is also validated by bandgap and low dropout regulators (LDO). To verify the accuracy of the model, the experimental measurement results of the LDO circuit fabricated in the 0.18 μm process have been obtained. Compared with the measurement results, the simulation results of both transistor and black‐box macro models have high accuracy. Those results demonstrate the great potential of the black‐box macro model in analog circuit design and simulation.

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