SZU-NUS Collaborative Center and International Collaborative Laboratory of 2D Materials for Optoelectronic Science & Technology, Engineering Technology Research Center for 2D Materials Information Functional Devices and Systems of Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Department of Chemistry and Centre for Advanced 2D Materials (CA2DM), National University of Singapore (NUS), 3 Science Drive 3, Singapore 117543, Singapore.
ACS Nano. 2020 Jun 23;14(6):7628-7638. doi: 10.1021/acsnano.0c03869. Epub 2020 Jun 8.
Two-dimensional ferroelectrics is attractive for synaptic device applications because of its low power consumption and amenability to high-density device integration. Here, we demonstrate that tin monosulfide (SnS) films less than 6 nm thick show optimum performance as a semiconductor channel in an in-plane ferroelectric analogue synaptic device, whereas thicker films have a much poorer ferroelectric response due to screening effects by a higher concentration of charge carriers. The SnS ferroelectric device exhibits synaptic behaviors with highly stable room-temperature operation, high linearity in potentiation/depression, long retention, and low cycle-to-cycle/device-to-device variations. The simulated device based on ferroelectric SnS achieves ∼92.1% pattern recognition accuracy in an artificial neural network simulation. By switching the ferroelectric domains partially, multilevel conductance states and the conductance ratio can be obtained, achieving high pattern recognition accuracy.
二维铁电体因其低功耗和适用于高密度器件集成而受到突触器件应用的青睐。在这里,我们证明了厚度小于 6nm 的二硫化锡(SnS)薄膜在平面铁电模拟突触器件中作为半导体沟道表现出最佳性能,而较厚的薄膜由于载流子浓度较高的屏蔽效应,铁电响应要差得多。SnS 铁电器件具有高度稳定的室温工作、在增强/抑制时具有高线性度、长保持时间和低循环间/器件间变化的突触行为。基于铁电 SnS 的模拟器件在人工神经网络模拟中实现了约 92.1%的模式识别准确率。通过部分切换铁电畴,可以获得多级电导状态和电导比,从而实现高的模式识别准确率。