New computation schemes inspired by biological processes are arising as an alternative to standard von-Neumann architectures, to provide hardware accelerators for information processing based on a neural networks approach. Systems of frequency-locked, coupled oscillators are investigated using the phase difference of the signal as the state variable rather than the voltage or current amplitude. As previously shown, these oscillating neural networks can efficiently solve complex and unstructured tasks such as image recognition. We have built nanometer scale relaxation oscillators based on the insulator–metal transition of VO₂. Coupling these oscillators with an array of tunable resistors offers the perspective of realizing compact oscillator networks. In this work we show experimental coupling of two oscillators. The phase of the two oscillators could be reversibly altered between in-phase and out-of-phase oscillation upon changing the value of the coupling resistor, i.e. by tuning the coupling strength. The impact of the variability of the devices on the coupling performances are investigated across two generations of devices.

由生物过程启发而来的新的计算方案正在替代标准的von-Neumann体系结构，从而为基于神经网络方法的信息处理提供硬件加速器。使用信号的相位差作为状态变量而不是电压或电流幅度来研究锁频耦合振荡器的系统。如前所示，这些振荡神经网络可以有效地解决复杂的非结构化任务，例如图像识别。我们基于VO ₂的绝缘体-金属跃迁建立了纳米尺度弛豫振荡器。将这些振荡器与可调电阻器阵列耦合提供了实现紧凑型振荡器网络的观点。在这项工作中，我们展示了两个振荡器的实验耦合。在改变耦合电阻器的值时，即通过调整耦合强度，可以在同相和异相振荡之间可逆地改变两个振荡器的相位。在两代器件中研究了器件可变性对耦合性能的影响。