Islam Rabiul, Shi Yu, de Oliveira Silva Gabriel Vinicius, Sachdev Manoj, Miao Guo-Xing
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo N2L 3G1, Ontario, Canada.
Institute for Quantum Computing, University of Waterloo, Waterloo N2L 3G1, Ontario, Canada.
ACS Nano. 2024 Aug 20;18(33):22045-22054. doi: 10.1021/acsnano.4c05137. Epub 2024 Aug 7.
We demonstrate a lithium (Li) imbued TiO iontronic device that exhibits synapse-like short-term plasticity behavior without requiring a forming process beforehand or a compliance current during switching. A solid-state electrolyte lithium phosphorus oxynitride (LiPON) behaves as the ion source, and the embedding and releasing of Li ions inside the cathodic like TiO renders volatile conductance responses from the device and offers a natural platform for hardware simulating neuron functionalities. Besides, these devices possess high uniformity and great endurance as no conductive filaments are present. Different short-term pulse-based phenomena, including paired pulse facilitation, post-tetanic potentiation, and spike rate-dependent plasticity, were observed with self-relaxation characteristics. Based on the voltage excitation period, the time scale of the volatile memory can be tuned. Temperature measurement reveals the ion displacement-induced conductance channels become frozen below 220 K. In addition, the volatile analog devices can be configured into nonvolatile memory units with multibit storage capabilities after an electroforming process. Therefore, on the same platform, we can configure volatile units as nonlinear dynamic reservoirs for performing neuromorphic training and the nonvolatile units as the weight storage layer. We proceed to use voice recognition as an example with the tunable time constant relationship and obtain 94.4% accuracy with a minimal training data set. Thus, this iontronic platform can effectively process and update temporal information for reservoir and neuromorphic computing paradigms.
我们展示了一种注入锂(Li)的TiO离子电子器件,该器件表现出类似突触的短期可塑性行为,无需预先进行形成过程或在切换过程中使用顺应电流。固态电解质氧氮化锂磷(LiPON)作为离子源,Li离子在阴极类似TiO的材料内部的嵌入和释放使器件产生可变的电导响应,并为模拟神经元功能的硬件提供了一个天然平台。此外,由于不存在导电丝,这些器件具有高度的均匀性和很强的耐久性。观察到了基于短期脉冲的不同现象,包括双脉冲易化、强直后增强和脉冲率依赖性可塑性,并具有自弛豫特性。基于电压激发周期,可以调整可变记忆的时间尺度。温度测量表明,离子位移诱导的电导通道在220 K以下会冻结。此外,经过电形成过程后,可变模拟器件可以配置成具有多位存储能力的非易失性存储单元。因此,在同一平台上,我们可以将可变单元配置为用于执行神经形态训练的非线性动态库,将非易失性单元配置为权重存储层。我们以语音识别为例,利用可调时间常数关系,在最小训练数据集的情况下获得了94.4%的准确率。因此,这个离子电子平台可以有效地处理和更新用于库和神经形态计算范式的时间信息。