Inter-university Semiconductor Research Center (ISRC) and the Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea.
Nanotechnology. 2017 Oct 6;28(40):405202. doi: 10.1088/1361-6528/aa86f8. Epub 2017 Aug 18.
Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.
脑启发式神经形态系统作为一种新的节能计算范式引起了广泛关注。在这里，我们报告了一种具有两个独立电栅的硅突触晶体管，以实现无需任何开关元件的基于硬件的神经网络系统。测量和分析了突触器件的尖峰时间依赖可塑性特性。借助基于测量数据的器件模型，使用修改后的国家标准与技术研究所手写数据集展示了基于硬件的尖峰神经网络系统的模式识别能力。通过比较具有和不具有抑制性突触部分的系统，证实了抑制性突触部分是获得有效和高模式分类能力的必要元素。
