Tactile tribotronic reconfigurable p-n junctions for artificial synapses

Abstract

The emulation of biological synapses with learning and memory functions and versatile plasticity is significantly promising for neuromorphic computing systems. Here, a robust and continuously adjustable mechanoplastic semifloating-gate transistor is demonstrated based on an integrated graphene/hexagonal boron nitride/tungsten diselenide van der Waals heterostructure and a triboelectric nanogenerator (TENG). The working states (p-n junction or n+-n junction) can be manipulated and switched under the sophisticated modulation of triboelectric potential derived from mechanical actions, which is attributed to carriers trapping and detrapping in the graphene layer. Furthermore, a reconfigurable artificial synapse is constructed based on such mechanoplastic transistor that can simulate typical synaptic plasticity and implement dynamic control correlations in each response mode by further designing the amplitude and duration. The artificial synapse can work with ultra-low energy consumption at 74.2 fJ per synaptic event and the extended synaptic weights. Under the synergetic effect of the semifloating gate, the synaptic device can enable successive mechanical facilitation/depression, short-/long-term plasticity and learning-experience behavior, exhibiting the mechanical behavior derived synaptic plasticity. Such reconfigurable and mechanoplastic features provide an insight into the applications of energy-efficient and real-time interactive neuromodulation in the future artificial intelligent system beyond von Neumann architecture.