Self-Learning Functions in Electron Devices and Their Applications to Neural Networks

电子器件中的自学习功能及其在神经网络中的应用

基本信息

批准号：
04402035
负责人：
ISHIWARA Hiroshi
金额：
$ 10.18万
依托单位：
Tokyo Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (A)
财政年份：
1992
资助国家：
日本
起止时间：
1992 至 1994
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-04402035/
关键词：
Neural-Networks Ferroelectrics Si Self-Learning Adaptive-Learning MISFET 自己学習機能 MOSFET ニューロ素子

项目摘要

In this research project, in order to extract self-learning functions in electron devices we proposed a novel adaptive-learning neurodevice and a neuron circuit using ferroelectric materials. In the proposed neuron circuit, the adaptive-learning MISFETs are employed to realize synaptic operations and an UJT (uni-junction transistor) is used as a switching device to obtain the output signal.First, we studied partial switching characteristics of PbZrTiO_3(PZT) films, which is a well-known ferroelectric material, when short input pulses are applied. It was found that ferroelectric PZT has adaptive-learning functions and it is applicable to neurodevices. Next, to fabricate the adaptive-learning neurodevices, we formed PZT films on Si substrates. However, it was shown tha the interdiffusion between PZT and Si was extremely serious and that PZT films with good-crystalline quality could not obtained. To overcome the difficulty, we proposed here to use a SrTiO_3(STO) as a buffer layr. The STO buffer layr suppressed the interdiffusion and PZT films with good crystalline quality were successfully grown on Si substrates. Furthermore, we studied on a new ferroelectric material, BaMgF_4(BMF) which can be grown on Si or GaAs substrates. BMF films were prepared on Si substrates by molecular beam epitaxy(MBE) and a remanent polarization of 1muC/cm^2 was obtained.In addition, we experimentally and theoretically studied on UJTs, which are used in the neuron circuits. We fabricated various size of UJTs on SOI(silicon on insulator) substrates and demonstrated that the output frequency can be controlled by a CR time constant in the neuroncircuit. Furthermore, we theoretically calculated the electrical characteristics of UJTs and showed that the characteristics of UJTs were strongly affected by surface recombinations at the Si/SiO_2 interface.

在该研究项目中，为了在电子设备中提取自学习功能，我们提出了一种新型的自适应学习神经电视和使用铁电材料的神经元回路。在拟议的神经元电路中，使用自适应学习失误来实现突触操作，并将UJT（UNI-JUNTICTOR晶体管）用作获得输出信号的开关设备。首先，我们研究了PBZRTIO_3（PZT）（PZT）的部分切换特性当应用短输入脉冲时，膜是一种众所周知的铁电材料。发现铁电PZT具有自适应学习功能，并且适用于神经发行版。接下来，为了制造自适应学习神经退行版，我们在SI底物上形成了PZT膜。但是，这表明PZT和SI之间的扩散非常严重，并且无法获得具有良好稳定质量的PZT膜。为了克服困难，我们在这里提议将SRTIO_3（STO）用作缓冲区layr。 STO缓冲液Layr抑制了扩散型，并以良好结晶质量的PZT膜成功地在SI底物上生长。此外，我们研究了可以在SI或GAAS底物上生长的新型铁电材料BAMGF_4（BMF）。通过分子束外延（MBE）在SI底物上制备BMF膜，并获得了1MUC/CM^2的远期极化。此外，我们在UJTS上进行了实验和理论研究，在UJTS上使用，这些偏振在神经元电路中使用。我们在SOI（绝缘子上的硅）底物上制造了各种大小的UJT，并证明了输出频率可以通过神经循环中的Cr时间常数来控制。此外，我们从理论上计算了UJT的电特性，并表明UJT的特性受到SI/SIO_2界面上表面重组的强烈影响。