Voltage-controlled dynamics of metal-to-insulator phase transition in La_0.67 Sr_0.33MnO₃ thin films exploiting negative differential resistance

Neuromorphic computing has gained ample interest due to energy-efficient solutions beyond the non-von Neumann approach, but at the hardware level, the present system lacks rich non-linear phenomenon that occurs in biological systems. An experimental realization of such a non-linear system requires tunable neuronal spiking, realized in Mott memristors that exhibit structural transition-induced electronic instabilities and manifest as a negative differential resistance (NDR). However, repeated structural transitions required to deliver such electronic instabilities hinder an efficient demonstration of spiking neural networks (SNN) due to internal strain build-up. Here we report on a new approach using the intrinsic metal-to-insulator coupled transition in La_0.67Sr_0.33MnO₃ without the requirement of a structural transition. Two distinct ‘S’ type NDR at different bias regimes have been observed on LSMO thin-film devices grown on textured LaAlO₃ substrate. Both bias regimes have been exploited to demonstrate voltage control oscillators with tunable frequencies. The textured substrate also provides anisotropic thermal interactions among multiple oscillatory devices, driving them either synchronously or asynchronously and is a crucial route towards stabilizing spiking neural networks.