Neuromorphic devices based on 2D layered materials heterostructures

Lead Research Organisation: University College London
Department Name: Electronic and Electrical Engineering


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


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Project Reference Relationship Related To Start End Student Name
EP/R513143/1 01/10/2018 30/09/2023
2570030 Studentship EP/R513143/1 01/10/2021 30/09/2025 Aferdita Xhameni