In this Letter, synchronization of micromechanical oscillators with a frequency ratio of 3:1 is reported. Two electrically coupled piezoresistive micromechanical oscillators are built for the study, and their oscillation frequencies are tuned via the Joule heating effect to find out the synchronization region. Experimental results show that the larger coupling strength or bias driving voltage is applied and a wider synchronization region is obtained. Interestingly, however, the oscillator's frequency tunability is dramatically reduced from –809.1 Hz/V to –23.1 Hz/V when synchronization is reached. A nearly 10-fold improvement of frequency stability at 1 s is observed from one of the synchronized oscillators, showing a comparable performance of the other. The stable high order synchronization of micromechanical oscillators is helpful to design high performance resonant sensors with a better frequency resolution and a larger scale factor.

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Brain-inspired intelligent systems demand diverse neuromorphic devices beyond simple functionalities. Merging biomimetic sensing with weight-updating capabilities in artificial synaptic devices represents one of the key research focuses. Here, we report a multiresponsive synapse device that integrates synaptic and optical-sensing functions. The device adopts vertically stacked graphene/h-BN/WSe2 heterostructures, including an ultrahigh-mobility readout layer, a weight-control layer, and a dual-stimuli-responsive layer. The unique structure endows synapse devices with excellent synaptic plasticity, short response time (3 μs), and excellent optical responsivity (105 A/W). To demonstrate the application in neuromorphic computing, handwritten digit recognition was simulated based on an unsupervised spiking neural network (SNN) with a precision of 90.89%, well comparable with the state-of-the-art results. Furthermore, multiterminal neuromorphic devices are demonstrated to mimic dendritic integration and photoswitching logic. Different from other synaptic devices, the research work validates multifunctional integration in synaptic devices, supporting the potential fusion of sensing and self-learning in neuromorphic networks.