Asymmetric conducting route and potential redistribution determine the polarization-dependent conductivity in layered ferroelectrics.

Precise control of the conductivity of layered ferroelectric semiconductors is required to make these materials suitable for advanced transistor, memory and logic circuits. Although proof-of-principle devices based on layered ferroelectrics have been demonstrated, it remains unclear how the polarization inversion induces conductivity changes. Therefore, function design and performance optimization remain cumbersome. Here we combine ab initio calculations with transport experiments to unveil the mechanism underlying the polarization-dependent conductivity in ferroelectric channel field-effect transistors. We find that the built-in electric field gives rise to an asymmetric conducting route formed by the hidden Stark effect and competes with the potential redistribution caused by the external field of the gate. Furthermore, leveraging our mechanistic findings, we control the conductivity threshold in α-In2Se3 ferroelectric channel field-effect transistors. We demonstrate logic-in-memory functionality through the implementation of electrically self-switchable primary (AND, OR) and composite (XOR, NOR, NAND) logic gates. Our work provides mechanistic insights into conductivity modulation in a broad class of layered ferroelectrics, providing foundations for their application in logic and memory electronics.

Hybrid neuromorphic hardware with sparing 2D synapse and CMOS neuron for character recognition.

Neuromorphic computing enables efficient processing of data-intensive tasks, but requires numerous artificial synapses and neurons for certain functions, which leads to bulky systems and energy challenges. Achieving functionality with fewer synapses and neurons will facilitate integration density and computility. Two-dimensional (2D) materials exhibit potential for artificial synapses, including diverse biomimetic plasticity and efficient computing. Considering the complexity of neuron circuits and the maturity of complementary metal-oxide-semiconductor (CMOS), hybrid integration is attractive. Here, we demonstrate a hybrid neuromorphic hardware with 2D MoS2 synaptic arrays and CMOS neural circuitry integrated on board. With the joint benefit of hybrid integration, frequency coding and feature extraction, a total cost of twelve MoS2 synapses, three CMOS neurons, combined with digital/analogue converter enables alphabetic and numeric recognition. MoS2 synapses exhibit progressively tunable weight plasticity, CMOS neurons integrate and fire frequency-encoded spikes to display the target characters. The synapse- and neuron-saving hybrid hardware exhibits a competitive accuracy of 98.8% and single recognition energy consumption of 11.4 µW. This work provides a viable solution for building neuromorphic hardware with high compactness and computility.

An Optoelectronic Synapse Based on Two-Dimensional Violet Phosphorus Heterostructure.

Neuromorphic computing can efficiently handle data-intensive tasks and address the redundant interaction required by von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. 2D phosphorene, such as violet phosphorene, show great potential in optoelectronics due to their strong light-matter interactions, while current research is mainly focused on synthesis and characterization, its application in photoelectric devices is vacant. Here, the authors combined violet phosphorene and molybdenum disulfide to demonstrate an optoelectronic synapse with a light-to-dark ratio of 106 , benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including a dynamic range (DR) of > 60 dB, 128 (7-bit) distinguishable conductance states, electro-optical dependent plasticity, short-term paired-pulse facilitation, and long-term potentiation/depression. Thanks to the excellent DR and multi-states, high-precision image classification with accuracies of 95.23% and 79.65% is achieved for the MNIST and complex Fashion-MNIST datasets, which is close to the ideal device (95.47%, 79.95%). This work opens the way for the use of emerging phosphorene in optoelectronics and provides a new strategy for building synaptic devices for high-precision neuromorphic computing.