多阻态铁电HLO忆阻器及其在联想学习电路和人脸识别中的实现

Multilevel state ferroelectric La:HfO_(2)-based memristors and their implementations in associative learning circuit and face recognition

随着摩尔定律接近物理极限,传统的冯诺依曼架构面临挑战.忆阻器在多层存储、神经形态系统和模拟电路中的应用具有克服冯诺依曼架构瓶颈的潜力.在这里,我们在硅衬底上生长了Pd/La:HfO_(2)(HLO)/La2/3Sr1/3MnO_(3)高性能忆阻器,其有利于与互补式氧化物半导体工艺兼容.该忆阻器器件表现出良好的循环稳定性和多级电阻状态存储能力以及器件的突触特性,如长时增强/抑制、短时记忆到长时记忆、尖峰时间依赖性可塑性和双脉冲促进.基于器件的类脑突触行为,在神经启发计算中识别人脸图像时,识别率高达91.11%.通过理论计算和硬件联想学习电路测试,基于铪基铁电忆阻器的生物联想学习行为得以实现.

As Moore’s Law approaches physical limits,traditional von Neumann buildings are facing challenges.The application of memristors in multilayer storage,neuromorphic systems and analog circuits has the potential to overcome the von Neumann architecture bottleneck.Here,we fabricated high-performance memristors based on the Pd/La:HfO_(2)/La2/3Sr1/3MnO_(3)device on silicon substrate,which facilitate the compatibility with complementary metal oxide semiconductor processes.The memristor devices exhibited good cycling stability and multilevel resistive state storage capabilities.And the synaptic properties of the device,such as long-term potentiation/depression,short-term memory to long-term memory,spike time-dependent plasticity,and double-pulse facilitation,were also shown.Based on the brainlike synaptic behavior of the device,a high recognition rate of91.11% was achieved in recognizing face images in neuralinspired computing.Through theoretical calculation and hardware associative learning circuit test,the hafnium-based ferroelectric memristor was successfully applied to biological associative learning behavior for the first time.