Neurotransistor-based Memristive Crossbar Memcomputing (NeuroMCross)

大脑激发的脉冲神经网络（snn）通过相互连接的神经元的时空脉冲并行和异步运行。电容性神经网络对电容性mems元件进行充电和放电，导致能量从信号源移除或返回，与执行大量矩阵向量乘法的传统人工神经网络（ANN）结构相比，可以大大降低能耗。在这方面，记忆晶体管神经元结合记忆阻/记忆电容器件和晶体管结构可以实现神经元的基本特征，如泄漏-集成-点火（LIF）行为。这可以构建成一个神经形态的memcomputing构建块，用于通用SNN架构。在这个项目中，我们的目标是开发一种新的神经横条结构，它结合并扩展了记忆/记忆电容横条和神经晶体管的各个组件的功能，这些组件基于在之前的BioMCross项目中开发的Al2O3/Nb2O5器件。与以前的mem晶体管方法不同，我们在硅基n沟道场效应晶体管（nFET）的栅极处制造了一个集成的多输入memcapacitive横条结构，它能够沿着晶体管沟道创建受控的1D和2D渗透路径。LIF神经交叉杆集成了高复杂性，例如用于多个输入的完全连接层，用于一维渗透或or连接的二维渗透路径的全或全特征提取，以及单个设备结构中的循环连接。为此目的，拟议的工作将包括几个基本方面：(i)使用3D TCAD仿真的神经交叉棒器件建模，（ii）在晶体管结构上集成多个Al2O3/Nb2O5记忆/记忆电容器件的神经交叉棒制造和参数优化，（iii）神经交叉棒电路系统建模和读/写操作分析，（iv）循环snn的时间编码信号设计，硬件感知离线训练，性能优化以及局部学习规则的实现。最后，我们将研究各种时间序列数据集，以证明循环SNN互连神经交叉杆结构的实现，并与传统的基于cmos的SNN和纯记忆性ANN加速系统相比，评估系统的性能。本研究旨在通过一种新的神经交叉棒构建块来扩展当前的技术状态，并提供对基于高度集成器件概念的memcapacitive SNN系统实际实现可行性的全面理解。

Brain-inspired Spiking Neural Networks (SNNs) operate in parallel and asynchronously via spatio-temporal spikings of interconnected neurons. Capacitive neural networks that charge and discharge capacitive mem-elements, resulting in the removal or return of energy from/to the signal sources, can lead to considerably lower energy consumption in comparison to conventional artificial neural network (ANN) structures performing massively matrix-vector multiplications. In this regards memtransistor neurons combining memristive/memcapacitive devices and transistor structures can enable fundamental features of neurons like the leaky-integrate-and-fire (LIF) behavior. This can be built into a neuromorphic memcomputing building block for versatile SNN architectures. In this project we aim at the development of a novel neuro-crossbar structure, which combines and extends the functionalities of the individual components of the memristive/memcapacitive crossbar and neurotransistor based on Al2O3/Nb2O5 devices developed in the preceding BioMCross project. Unlike previous memtransistor approaches, we fabricate an integrated multi-input memcapacitive crossbar structure at the gate of a silicon-based n-channel field effect transistor (nFET), which is able to create a controlled 1D and 2D percolation path along the transistor channel. The LIF neuro-crossbar integrates high complexity, such as a fully connected layer for multiple inputs, an all-or-nothing feature extraction for 1D percolation or OR-linked 2D percolation paths as well as recurrent connection in a single device structure. For this purpose, the proposed work will cover several essential aspects: (i) the neuro-crossbar device modeling using 3D TCAD simulation, (ii) the neuro-crossbar fabrication and parameter optimization by integration of multiple Al2O3/Nb2O5 memristive/memcapacitive devices on top of the transistor structure, (iii) the neuro-crossbar circuit system modeling and analysis of read/write operation and (iv) the design of temporal coded signals for recurrent SNNs, hardware-aware offline training, performance optimization as well as implementation of local learning rules. Finally, we will investigate various time series datasets to demonstrate the realization of recurrent SNNs interconnected neuro-crossbar structures and evaluate the system's performance compared to conventional CMOS-based SNN and purely memristive ANN accelerator systems. This research aims to extend the state of the art by a new neuro-crossbar building block and to provide a comprehensive understanding of the feasibility of practical realization of memcapacitive SNN systems based on highly integrated device concepts.