Floating-gate based PN blending optoelectronic synaptic transistor for neural machine translation  

用于神经机器翻译的浮栅型PN共混光电突触晶体管

作　　者：Xianghong Zhang Enlong Li Rengjian Yu Lihua He Weijie Yu Huipeng Chen Tailiang Guo 张翔鸿;李恩龙;俞衽坚;何立铧;余伟杰;陈惠鹏;郭太良(Institute of Optoelectronic Display,National&Local United Engineering Lab of Flat Panel Display Technology,Fuzhou University,Fuzhou 350002,China;Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China,Fuzhou 350100,China)

机构地区：[1]Institute of Optoelectronic Display,National&Local United Engineering Lab of Flat Panel Display Technology,Fuzhou University,Fuzhou 350002,China [2]Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China,Fuzhou 350100,China

出　　处：《Science China Materials》2022年第5期1383-1390,共8页中国科学（材料科学（英文版）

基　　金：supported by the National Natural Science Foundation of China (61974029);the Natural Science Foundation for Distinguished Young Scholars of Fujian Province (2020J06012);Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China (2021ZZ129)。

摘　　要：Neural machine translation, which has an encoder-decoder framework, is considered to be a feasible way for future machine translation. Nevertheless, with the fusion of multiple languages and the continuous emergence of new words, most current neural machine translation systems based on von Neumann’s architecture have seen a substantial increase in the number of devices for the decoder, resulting in high-energy consumption rate. Here, a multilevel photosensitive blending semiconductor optoelectronic synaptic transistor(MOST) with two different trapping mechanisms is firstly demonstrated, which exhibits 8 stable and well distinguishable states and synaptic behaviors such as excitatory postsynaptic current, short-term memory, and long-term memory are successfully mimicked under illumination in the wavelength range of 480–800 nm. More importantly, an optical decoder model based on MOST is successfully fabricated,which is the first application of neuromorphic device in the field of neural machine translation, significantly simplifying the structure of traditional neural machine translation system.Moreover, as a multi-level synaptic device, MOST can further reduce the number of components and simplify the structure of the codec model under light illumination. This work first applies the neuromorphic device to neural machine translation, and proposes a multi-level synaptic transistor as the based cell of decoding module, which would lay the foundation for breaking the bottleneck of machine translation.具有编码器-解码器框架的神经机器翻译被认为是未来机器翻译的一种主要框架.然而,由于多种语言融合和新词不断涌现,目前大多数基于冯诺依曼架构的神经机器翻译系统的解码器设备数量大幅增加,导致系统功耗过高.本研究首先展示了具有两种不同捕获机制的多级光敏混合半导体光电突触晶体管(MOST),它在480–800 nm波长范围的光照下表现出8种稳定且易于区分的状态和突触行为,且均具备兴奋性突触后电流、短期记忆和长期记忆等突触性能.此外,本研究首次将神经形态器件应用在神经机器翻译领域,成功制作了基于MOST的光解码器模型,显著简化了传统神经机器翻译系统的结构.并且作为一种多级突触器件, MOST可以进一步减少器件数量,在光照下简化编解码模型的结构.这项研究首先将神经形态器件应用于神经机器翻译,并提出了一种多级突触晶体管作为解码模块的基础单元,为打破机器翻译的瓶颈奠定了坚实的基础.

关 键 词：optoelectronic transistor synaptic transistor synaptic plasticity modulation neural machine translation decoder 

分 类 号：TN321[电子电信—物理电子学] TP391.2[自动化与计算机技术—计算机应用技术]