Jetty Prabana, Kannan Udaya Mohanan, Narayana Jammalamadaka S
Magnetic Materials and Device Physics Laboratory, Department of Physics, Indian Institute of Technology Hyderabad, Hyderabad, 502284, India.
Department of Electronic Engineering, Gachon University, Seongnam-si, Gyeonggi-do 13120, Republic of Korea.
Nanotechnology. 2023 Nov 28;35(7). doi: 10.1088/1361-6528/ad0bd1.
In this manuscript, we report on the paramagnetic HoO-based synaptic resistive random-access memory device for the implementation of neuronal functionalities such as long-term potentiation, long-term depression and spike timing dependent plasticity respectively. The plasticity of the artificial synapse is also studied by varying pulse amplitude, pulse width, and pulse interval. In addition, we could classify handwritten Modified National Institute of Standards and Technology data set (MNIST) using a fully connected neural network (FCN). The device-based FCN records a high classification accuracy of 93.47% which is comparable to the software-based test accuracy of 97.97%. This indicates the highly optimized behavior of our synaptic device for hardware neuromorphic applications. Successful emulation of Pavlovian classical conditioning for associative learning of the biological brain is achieved. We believe that the present device consists the potential to utilize in neuromorphic applications.
在本手稿中，我们报告了基于顺磁性HoO的突触电阻式随机存取存储器装置，该装置分别用于实现诸如长时程增强、长时程抑制和脉冲时间依赖可塑性等神经元功能。还通过改变脉冲幅度、脉冲宽度和脉冲间隔来研究人工突触的可塑性。此外，我们可以使用全连接神经网络（FCN）对手写修改后的美国国家标准与技术研究所数据集（MNIST）进行分类。基于该装置的FCN记录了93.47%的高分类准确率，这与基于软件的97.97%的测试准确率相当。这表明我们的突触装置在硬件神经形态应用中具有高度优化的性能。成功实现了对生物大脑关联学习的巴甫洛夫经典条件反射的模拟。我们相信，目前的装置具有在神经形态应用中使用的潜力。
