LCVoltageControlledOscillatorBased Biosensor in 180nm CMOS Process Targeting xcexb2Dispersion for Detecting Exosomes Recently, minimally invasive cancer care represented by liquid biopsy has attracted considerable interest. Some substances in blood, such as circulating tumor cells and exosomes, are receiving increasing attention as potential biomarkers for liquid biopsy. In this paper, we present an LCvoltagecontrolledoscillatorbased biosensor for detecting exosomes. In order to detect exosomes, we focused on dielectric xcexb2dispersion and utilized the frequency band in which xcexb2dispersion occurs. To validate the operation of the proposed system, we performed electromagnetic (EM) analysis. The results of EM simulation demonstrated the possibility of detecting exosomes. In addition, a test chip was fabricated in 180nm CMOS process. We performed the test chip measurement by adding a drop of water on the inductor, and an average measured frequency shift of 5.723 kHz was confirmed, which demonstrates the feasibility of the proposed biosensor in detecting exosomes.

A Neuromorphic Device for Detecting HighFrequency Oscillations in Human iEEG Among diagnostic biomarkers, high frequency oscillations in human iEEG are used to identify epileptogenic brain tissue during epilepsy surgery. However, current methods typically analyse the raw data offline using complex timeconsuming algorithms. We developed a compact neuromorphic sensoryprocessing systemonchip that can monitor the iEEG signals and detect high frequency oscillations in realtime using spiking neural networks. To this end, we present an integrated device with an analog frontend that can extract predefined spectral features and encode them as addressevents, and a neuromorphic processor core that implements a network of integrate and fire neurons with dynamic synapses. The device was fabricated using a standard  0.18mumathrmm  CMOS technology node. The estimated power consumption of the frontend is  6.2mumathrmW channel and the areaonchip for a single channel is 0.15 square millimetres. The SNN classifier provides 90.5% sensitivity and 67.7% specificity for detecting high frequency oscillations. This is the first feasibility study towards identifying relevant features in intracranial human data in realtime onchip using eventbase processors.