Kwon Ojun, Oh Seyoung, Park Heejeong, Jeong Soo-Hong, Cho Byungjin, Park Woojin
Department of Advanced Material Engineering, Chungbuk National University, Chungdaero 1, Cheongju, 28644, Korea (the Republic of).
Department of Advanced Material Engineering, Chungbuk National University, chungdaero 1, Cheongju, 28644, Korea (the Republic of).
Nanotechnology. 2022 Feb 9. doi: 10.1088/1361-6528/ac533e.
The reliable conductance modulation of synaptic devices is key when implementing high-performance neuromorphic systems. Herein, we propose a floating gate IGZO synaptic device with an aluminum trapping layer to investigate the correlation between its diverse electrical parameters and pattern recognition accuracy. Basic synaptic properties such as excitatory postsynaptic current, paired pulse facilitation, long/short term memory, and long-term potentiation/depression are demonstrated in the IGZO synaptic transistor. The effects of pulse tuning conditions associated with the pulse voltage magnitude, interval, duration, and cycling number of the applied pulses on the conductance update are systematically investigated. It is discovered that both the nonlinearity of the conductance update and cycle-to-cycle variation should be critically considered using an artificial neural network simulator to ensure the high pattern recognition accuracy of Modified National Institute of Standards and Technology (MNIST) handwritten digit images. The highest recognition rate of the MNIST handwritten dataset is 94.06% for the most optimized pulse condition. Finally, a systematic study regarding the synaptic parameters must be performed to optimize the developed synapse device.
在实现高性能神经形态系统时，突触器件可靠的电导调制至关重要。在此，我们提出一种具有铝俘获层的浮栅铟镓锌氧化物（IGZO）突触器件，以研究其各种电学参数与模式识别精度之间的相关性。铟镓锌氧化物突触晶体管展示出了诸如兴奋性突触后电流、双脉冲易化、长/短期记忆以及长时程增强/抑制等基本突触特性。系统地研究了与施加脉冲的脉冲电压幅度、间隔、持续时间和循环次数相关的脉冲调节条件对电导更新的影响。研究发现，使用人工神经网络模拟器时，必须严格考虑电导更新的非线性和逐周期变化，以确保改进的美国国家标准与技术研究院（MNIST）手写数字图像具有较高的模式识别精度。对于最优化的脉冲条件，MNIST手写数据集的最高识别率为94.06%。最后，必须对突触参数进行系统研究，以优化所开发的突触器件。
