CMUT-based biosensor with convolutional neural network signal processing

The improvement of the micromachined ultrasound transducer based (CMUT) biosensor fabrication technology and signal processing, which led to higher signal to noise ratio is reported. The biosensor contains interdigitally arranged CMUT structure with gold-coated analytical area. It is assembled with the plexiglass microchannels. CMUTs were fabricated with the wafer bonding technology for 5 MHz operation in immersion. For signal processing the convolutional neural network (CNN) was developed and trained to classify the sensor data to different propagation delay values. For training of the network 750 thousand signals representing different properties of the bioanalyte and different noise conditions was simulated by the finite time difference domain (FDTD) model. The capability of the CNN algorithm to classify the propagation delay data was compared with the adaptive passband filter signal processing algorithm used in our previous version of the senor. Both sensing channels were run simultaneously with the reference liquids in the microchannel: deionized water switching to 0.9% saline. It was found that CNN channel is capable to improve the signal to noise ratio for this experiment to 75 dB, when the same property for the passband filter channel was only 60 dB. This led to the generalization about the advantage of CNN channel to provide 15 dB less of instrumental noise. Finally, the real-time detection ability of the bovine serum albumin (BSA) deposition on the analytical area of improved sensor was demonstrated.