Molybdenum disulfide transistors reshape spiking neural networks

Researchers have created a new computing platform using reconfigurable molybdenum disulfide dual-gate transistors with a ferroelectric hafnium zirconium oxide layer, unifying logic, synaptic, and neuronal functions within a single device. The work addresses a core problem in neuromorphic systems: excessive energy consumption caused by processing irrelevant data; the team’s architecture co-designs front-end logic and spiking neural networks to suppress this “task-irrelevant information at the front-end” before spike generation. This platform uses hardware to select task-directed inputs, reshaping the scale of downstream networks and reducing unnecessary neuronal activation. The transistors, built from dual-gate molybdenum disulfide, allow for dynamic configuration between high-performance logic and neuromorphic operation, achieving a subthreshold slope of 63.8 mV dec−1 and an on/off current ratio exceeding 109. This performance facilitates accurate, logic-based data filtering and adaptable synaptic behavior essential for spiking neural network computation. The architecture directly addresses inefficiencies inherent in conventional neuromorphic systems, where all…