3D Neuromorphic Hardware with Single Thin-Film Transistor Synapses Over Single Thin-Body Transistor Neurons by Monolithic Vertical Integration.

ABSTRACT: Neuromorphic hardware with a spiking neural network (SNN) can significantly enhance the energy efficiency for artificial intelligence (AI) functions owing to its event-driven and spatiotemporally sparse operations. However, an artificial neuron and synapse based on complex complementary metal-oxide-semiconductor (CMOS) circuits limit the scalability and energy efficiency of neuromorphic hardware. In this work, a neuromorphic module is demonstrated composed of synapses over neurons realized by monolithic vertical integration. The synapse at top is a single thin-film transistor (1TFT-synapse) made of poly-crystalline silicon film and the neuron at bottom is another single transistor (1T-neuron) made of single-crystalline silicon. Excimer laser annealing (ELA) is applied to activate dopants f

SUBMITTER: Han JK 

PROVIDER: S-EPMC10602577 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature