Reward-modulated spike-timing-dependent plasticity in van der Waals ferroelectric memtransistor for robotic...

Reward-modulated spike-timing-dependent plasticity (R-STDP) is a promising biomimetic learning rule in neuromorphic intelligent systems for implementing tasks in variable environments. Nevertheless, realizing R-STDP in a single synaptic device for building compact and energy-efficient neuromorphic systems remains challenging. Here, we report a two-dimensional ferroelectric memtransistor to emulate the R-STDP learning rule by effectively reconfiguring the STDP and anti-STDP. The thermionic emission and tunneling behavior of charges at the ferroelectric interface can be regulated via vertical electric field in a multi-terminal manner, allowing for controllable polarization reversal of synaptic plasticity and transition between STDP and anti-STDP. This enables faithful realization of the R-STDP feature in a single device with energy consumption of ∼1.3 nJ (the lowest known to date), approximately 10⁶ times lower than that of its complementary metal-oxide-semiconductor (CMOS) counterpart. By leveraging the synaptic characteristics in the hardware device, we construct spiking neural networks (SNNs) trained with R-STDP to perform robotic recognition and tracking tasks. The SNN achieves 95.1% accuracy on the MNIST dataset using only 8000 parameters, and faster convergence speed requiring only one data batch with 100% inference in the few-shot learning task. Moreover, a robotic arm motion control system configured with R-STDP exhibits 85.5% success rate in tracking both the static and moving targets, illustrating its outstanding adaptability to the dynamic environments. This work provides a potential hardware building block to support compact neuromorphic systems for the application of interactive artificial intelligence agents.