基于CsPbBr_(3)量子点/PDVT-10共轭聚合物杂化薄膜的光突触晶体管用于高效的神经形态计算

CsPbBr_(3)quantum dots/PDVT-10 conjugated polymer hybrid film-based photonic synaptic transistors toward high-efficiency neuromorphic computing

光突触晶体管被视为有潜力的神经形态计算系统,有望克服基于冯诺依曼架构运算的固有限制.然而,具备简单制备工艺和高效信息处理能力的光突触晶体管的设计和构建面临着巨大的挑战.本文报道了一种通过旋涂CsPbBr_(3)钙钛矿量子点(QDs)和PDVT-10共轭聚合物共混物来制备光突触晶体管的新方法.由CsPbBr_(3)QDs和PDVT-10组成的杂化薄膜具有平坦的表面、优异的光吸收和良好的电荷传输性能,有助于此类钙钛矿基突触实现高效的光电转换.因此,基于CsPbBr_(3)QDs和PDVT-10杂化薄膜的光突触晶体管表现出了优异的器件性能,并具有基本的突触功能,包括兴奋性突触后电流、双脉冲促进和长程记忆.通过利用光增强和电抑制特性,基于钙钛矿的光突触晶体管被成功应用于神经形态计算,其模式识别精度高达89.98%,这是迄今为止用于模式识别的突触晶体管的最高值之一.这项工作为制备高模式识别精度的钙钛矿基神经形态系统提供了一条有效且方便的途径.

Photonic synaptic transistors are promising neuromorphic computing systems that are expected to circumvent the intrinsic limitations of von Neumann-based computation.The design and construction of photonic synaptic transistors with a facile fabrication process and highefficiency information processing ability are highly desired,while it remains a tremendous challenge.Herein,a new approach based on spin coating of a blend of CsPbBr_(3) perovskite quantum dot(QD)and PDVT-10 conjugated polymer is reported for the fabrication of photonic synaptic transistors.The combination of flat surface,outstanding optical absorption,and remarkable charge transporting performance contributes to high-efficiency photon-to-electron conversion for such perovskite-based synapses.High-performance photonic synaptic transistors are thus fabricated with essential synaptic functionalities,including excitatory postsynaptic current(EPSC),paired-pulse facilitation(PPF),and long-term memory.By utilizing the photonic potentiation and electrical depression features,perovskite-based photonic synaptic transistors are also explored for neuromorphic computing simulations,showing high pattern recognition accuracy of up to 89.98%,which is one of the best values reported so far for synaptic transistors used in pattern recognition.This work provides an effective and convenient pathway for fabricating perovskite-based neuromorphic systems with high pattern recognition accuracy.