THz testing has been recently proposed to identify altered or damaged ICs. This method is based on the fact that a modern field-effect transistor (FET) with a sufficiently short channel can serve as a terahertz detector. The response can be recorded while changing the THz radiation parameters and location and compared to a trusted one for classification. We measured the THz response of original and damaged ICs for classification using different Transfer Learning models as a method of deep learning. We have achieved the highest classification accuracy of 98%.
KEYWORDS: Multiplexing, Analog electronics, Safety, Receivers, Mobile devices, Glucose, Digital electronics, Dielectric spectroscopy, Data conversion, Data acquisition
A miniaturized potentiostat integrated with a three-electrode system to monitor different analytes is presented. The potentiostat circuit has been designed to have the feature of four-channel multiplexing to operate different electrochemical cells simultaneously. It is Bluetooth-connected to a user-controlled mobile app through which the system is wirelessly controlled and data is acquired. The personalized data from the analysis are displayed and analyzed in the mobile app. The system is comprised of four units: digital to analog converter (DAC), multiplexing unit, control unit, and current to voltage converter (CVC). The circuit is run by Arduino NANO 33 BLE. The Arduino's digital pulse width modulator (PWM) signal is converted into an analog signal through the DAC unit to run the scanning in the voltage range of -1V to 2V. This output of the DAC unit is then fed into the multiplexing unit to distribute it to all four control units one at a time. Later, each control unit of the respective cells performs scanning through the three-electrode system connected to the control unit. The real-time scanning data collected from the cell, sent to the CVC unit, and converted into a voltage to be readable by the Arduino. With its small form factor, low power, and low cost the presented system can be used wearable health monitoring platforms.
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