Cheng Ran, Goteti Uday S, Walker Harrison, Krause Keith M, Oeding Luke, Hamilton Michael C
Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.
Alabama Micro/Nano Science and Technology Center, Auburn University, Auburn, AL, United States.
Front Neurosci. 2021 Nov 8;15:765883. doi: 10.3389/fnins.2021.765883. eCollection 2021.
We explore the use of superconducting quantum phase slip junctions (QPSJs), an electromagnetic dual to Josephson Junctions (JJs), in neuromorphic circuits. These small circuits could serve as the building blocks of neuromorphic circuits for machine learning applications because they exhibit desirable properties such as inherent ultra-low energy per operation, high speed, dense integration, negligible loss, and natural spiking responses. In addition, they have a relatively straight-forward micro/nano fabrication, which shows promise for implementation of an enormous number of lossless interconnections that are required to realize complex neuromorphic systems. We simulate QPSJ-only, as well as hybrid QPSJ + JJ circuits for application in neuromorphic circuits including artificial synapses and neurons, as well as fan-in and fan-out circuits. We also design and simulate learning circuits, where a simplified spike timing dependent plasticity rule is realized to provide potential learning mechanisms. We also take an alternative approach, which shows potential to overcome some of the expected challenges of QPSJ-based neuromorphic circuits, via QPSJ-based charge islands coupled together to generate non-linear charge dynamics that result in a large number of programmable weights or non-volatile memory states. Notably, we show that these weights are a function of the timing and frequency of the input spiking signals and can be programmed using a small number of DC voltage bias signals, therefore exhibiting spike-timing and rate dependent plasticity, which are mechanisms to realize learning in neuromorphic circuits.
我们探索了超导量子相位滑移结（QPSJs）在神经形态电路中的应用，它是约瑟夫森结（JJs）的电磁对偶体。这些小型电路可作为用于机器学习应用的神经形态电路的构建模块，因为它们具有理想的特性，如每次操作固有的超低能量、高速、密集集成、可忽略的损耗以及自然的尖峰响应。此外，它们具有相对简单的微纳制造工艺，这为实现复杂神经形态系统所需的大量无损互连展示了前景。我们模拟了仅含QPSJ的电路以及混合QPSJ + JJ电路，用于神经形态电路中的应用，包括人工突触和神经元以及扇入和扇出电路。我们还设计并模拟了学习电路，其中实现了一种简化的基于尖峰时间的可塑性规则，以提供潜在的学习机制。我们还采用了另一种方法，通过基于QPSJ的电荷岛耦合在一起以产生非线性电荷动力学，从而导致大量可编程权重或非易失性存储状态，这显示出克服基于QPSJ的神经形态电路一些预期挑战的潜力。值得注意的是，我们表明这些权重是输入尖峰信号的时间和频率的函数，并且可以使用少量直流电压偏置信号进行编程，因此表现出尖峰时间和速率依赖性可塑性，这是在神经形态电路中实现学习的机制。
