Study on memory characteristics of non-volatile ferroelectric-gate transistors for neural network applications

本课题的研究目标是利用硅集成电路技术实现人脑神经元的基本特性“学习”和“记忆”功能。在第一年（1997年），我们利用铁电SrBi_2Ta_2O_9 （SBT）薄膜制备了铁电门场效应晶体管（fet），并展示了该器件的非易失性记忆功能，但也发现其记忆保持时间短至1小时。因此，在1998年，我们分析了铁电栅场效应管的记忆保持特性，发现造成这种短的记忆保持时间主要有两个原因；一是在保留时间内施加去极化场，二是作用点在铁电膜的P-E特性的小回路上。为了改善保持特性，我们制作了金属-铁电-金属-绝缘体-半导体（MFMIS）场效应管。通过在大型MIS结构上使用小型MFM电容器，我们发现保留时间大大提高，最多可达1天以上。接下来，我们制作了一个铁电栅FET矩阵的突触器件。结果表明，神经网络中常用的加权和运算可以用铁电门场效应管矩阵来实现。最后，我们在SOT衬底上使用带有单结晶体管的铁电场效应管或CMOS shmitt触发脉冲振荡电路制造神经元集成电路。我们已经成功地证明了这种合成神经元电路的学习功能。

The objective of this research project is to realize "learning" and "memory" functions, which are basic properties of neurons in human brains, by using silicon integrated circuits technology, In the first year (1997), we fabricated ferroelectric-gate field effect transistors (FETs) using ferroelectric SrBi_2Ta_2O_9 (SBT) film and demonstrated non-volatile memory function of the device, However, it was also found that the memory retention time was as short as 1 hour. Hence, in 1998, we analyzed the memory retention characteristics of the ferroelectric-gate FETs and showed that such short retention time was caused by mainly two reasons ; one is that depolarization field was applied during the retention time, and the second is that the operation point is on a minor loop of P-E characteristics of the ferroelectric film. To improve the retention characteristics, we fabricated metal-ferroelectric-metal-insulator-semiconductor (MFMIS) FETs. By using a small MFM capacitor on a large MIS structure, we found that the retention time was drastically improved up to more than 1 day. Next, we fabricated synaptic device which is a ferroelectric-gate FET matrix. It was demonstrated that the weighted sum operation, which is commonly used in neural networks, can be performed by the ferroelectric-gate FET matrix. Finally, we fabricated the neuron integrated circuits on SOT substrates using a ferroelectric FET with a uni-junction transistor, or CMOS Shmit-trigger pulse oscillation circuit. We have succeeded to demonstrate the learning function of the fabricated neuron circuit.