Oscillatory neural networks based on insulator to metal transition of VO2 switches are implemented for image recognition. The VO2 oscillators are fabricated on silicon in a CMOS compatible process. A fully-connected network of coupled oscillators is investigated using programmable resistors as coupling elements. In this approach, input of the image information and data processing is performed in the time domain. In particular, tuning the coupling resistors allows to control the phaserelation between the oscillators. This is used to memorize and recognize patterns in an analog circuit. The concept is demonstrated experimentally on a three-VO2 oscillator network, network whereas simulations are performed on a larger 9-oscillators circuit.

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In this work we present an in-memory computing platform based on coupled VO2 oscillators fabricated in a crossbar configuration on silicon. Compared to existing platforms, the crossbar configuration promises significant improvements in terms of area density and oscillation frequency. Further, the crossbar devices exhibit low variability and extended reliability, hence, enabling experiments on 4-coupled oscillator. We demonstrate the neuromorphic computing capabilities using the phase relation of the oscillators. As an application, we propose to replace digital filtering operation in a convolutional neural network with oscillating circuits. The concept is tested with a VGG13 architecture on the MNIST dataset, achieving performances of 95% in the recognition task.