Neuromorphic devices based on 2D layered materials heterostructures

人工神经网络（ANN）是人工智能应用的核心。然而，人工神经网络通常是由“传统的”冯诺依曼架构，其中存储器和逻辑是分离的，需要连续的数据交换，导致处理瓶颈和大功耗。神经形态计算提供了人工神经网络的另一种方法，它从人脑中汲取灵感，并基于人工神经元和突触，其中记忆和逻辑位于同一位置。这个博士项目的重点是设计，模拟和测试基于异质结构的新型节能神经形态设备，该异质结构通过结合不同的2D/层状材料（2DLM），如石墨烯和过渡金属二硫属化物形成。实验上有数十种2DLM，理论上预测有2,000多种，并且可以以任意顺序和相互角度堆叠它们，这些材料在材料组合，原子精确界面和缺陷工程方面提供了前所未有的灵活性。该项目涉及不同神经形态设备的设计、（纳米）制造和电气测试。实验活动将通过有限元模拟完成。该项目将探索基于2DLM异质结构的不同垂直器件结构，用于节能神经形态计算。基于2DLM（如HfS 2）的热氧化和等离子体氧化的最新结果，该项目最初将专注于二维绝缘体的研究，旨在了解氧化过程，因此形成的氧化物和半导体/氧化物界面的特性。然后，重点将转向器件设计和制造，并将探索不同的结构和材料组合，并测试和优化单个器件的性能。最后，几个设备将组合成阵列，用于执行基本的机器学习任务，如模式识别或分类。在整个项目中，将特别关注材料和设备的可扩展性，以及与现有硅基ICT技术的集成。该项目将是实验和计算。该项目的实验部分将包括通过使用最先进的，精心设计的手套箱系统制造2DLM异质结构，该系统允许在惰性气氛中精确堆叠不同的2DLM，以及控制界面污染，高温退火，精确引入缺陷和等离子体蚀刻。将使用基于洁净室的微加工（光学和电子束光刻、反应离子蚀刻、金属蒸发等）完成神经形态设备的制造。将使用探针台和一系列测试设备（源/测量单元、脉冲发生器、阻抗分析仪、矢量网络分析仪、示波器、锁定放大器等）对器械性能进行测试。器械测试将包括直流扫描、电压脉冲、保持力和耐久性测试。该项目的计算部分将包括有限元模拟，主要通过技术计算机辅助设计（TCAD）工具Synopsys Sentaurus实现。需要时，TCAD模拟将由Comsol Multiphysics和CST Microwave Studio进行补充。该项目与EPSRC的不同领域保持一致，涵盖多个主题。特别是，该项目与“人工智能技术”，“微电子器件技术”和“石墨烯和碳技术”领域保持一致。该研究将与Kenyon教授和Mehonic博士（UCL电子和电气工程Dpt）密切合作，他们是电阻开关器件领域的领先专家，也是“IntrinSic”分拆公司的创始人和CSO/CTO，该公司设计和制造最先进的硅电阻随机存取存储器。公关

Artificial neural networks (ANNs) are at the core of artificial intelligence applications. However, ANN are usually implemented by "conventional" von Neumann architectures, where memory and logic are separated, requiring continuous data exchange which causes processing bottlenecks and large power consumption. An alternative approach to ANN is provided by neuromorphic computing, which takes inspiration from the human brain and is based on artificial neurons and synapses, where memory and logic are co-located. This PhD project focusses on design, simulation and testing of novel energy-efficient neuromorphic devices based on heterostructure formed by combining different 2D/layered materials (2DLM) such as graphene and transition metal dichalcogenides. With tens of 2DLM experimentally available and over 2,000 theoretically predicted and the possibility of stacking them in arbitrary order and mutual angle, such materials offer unprecedented flexibility in terms of combination of materials, atomically-precise interfaces and defect engineering. The project involves design, (nano)fabrication and electrical testing of different neuromorphic devices. The experimental activity will be completed by finite-element simulations.The project will explore different vertical device structures based on 2DLM heterostructures to be used for energy-efficient neuromorphic computing. Building on recent results on thermal and plasma oxidation of 2DLM such as HfS2, the project will initially focus on the investigation of two-dimensional insulators, aiming to understand of the oxidation process, the properties of the so-formed oxide and semiconductor/oxide interfaces. The focus will then shift towards device design and fabrication, and different structures and combinations of materials will be explored and the performance of individual devices will be tested and optimized. Finally, several devices will be combined to form arrays, which will be used to perform basic machine learning tasks such as pattern recognition or classification. Throughout the project, particular attention will be paid to scalability of materials and devices, as well as integration with existing silicon-based ICT technology. The project will be both experimental and computational. The experimental part of the project will consist in the fabrication of 2DLM heterostructures by using a state of the art, purposely-designed glovebox system which allows precise stacking of different 2DLM in an inert atmosphere, as well as control of interface contaminations, high-temperature annealing, precise introduction of defects and plasma etching. Fabrication of the neuromorphic devices will be completed using cleanroom-based microfabrication (optical and electron beam lithography, reactive ion etching, metal evaporation, etc). Device performance will be tested using a probe station coupled with an ensemble of testing equipment (source/measure units, pulse generators, impedance analysers, vector network analysers, oscilloscopes, lock-in amplifiers, etc.). Device testing will include DC sweeps, voltage pulses, retention and endurance tests. The computational part of the project will consist of finite-element simulations, achieved mainly via a Technology Computer Aided Design (TCAD) tool, Synopsys Sentaurus. TCAD simulations will be complemented by Comsol Multiphysics and CST Microwave Studio when needed. The project is well aligned to different EPSRC areas, encompassing multiple themes. In particular, the project is aligned with the "Artificial intelligence technologies", "Microelectronic device technology" and "Graphene and carbon technology" areas. The research will be conducted in close collaboration with Prof. Kenyon and Dr Mehonic (UCL Electronic and Electrical Engineering Dpt), leading experts in resistance-switching devices and founders and CSO/CTO of the "IntrinSic" spin-out company, which designs and manufactures state of the art silicon resistive random-access memories. The pr