Post-Doc candidate in 2D Materials-based Electronic Devices

Project

Focused research on optimizing 2D materials for novel device performance is pivotal for advancing beyond traditional CMOS devices. 2D materials, renowned for their distinctive properties at the nanoscale, provide new research paths and implementation for nanoelectronics. Oscillatory Neural Networks (ONNs) represent an innovative class of neural networks wherein information is encoded through the phase difference among oscillators. ONNs presented attractive attributes for associative memory tasks, and in our prior work we have demonstrated digital implementation on FPGA and analog realization via CMOS circuits. This project endeavors to explore the potentials and challenges of utilizing 2D material devices in ONN implementations.

Transistors and memristors based on 2D Transition Metal Dichalcogenides (TMDs) present relevant properties suitable for developing ONN network arrays. The primary focus will be on investigating 2D Field-Effect Transistor (FET) devices to develop relaxation oscillators and 2D memristor devices for establishing analog resistive coupling elements between oscillators. The goal is to create a proof-of-concept 2D material-based ONN circuits.

This innovative development aims to investigate the impact of nonuniformities, noise, and stochastic behavior inherent in 2D TMD devices on computing within the ONN framework. By implementing and analyzing the proposed circuit, allows to explore the advantages and limitations of employing 2D materials in ONNs, paving the way for more robust and energy efficient neural network architectures.

Candidate

We are seeking highly motivated and talented Post-Doc candidates to join our research team in the exciting field of 2D materials and devices from modeling, simulation to device fabrication and characterization. This position offers an exceptional opportunity to contribute to cutting-edge research in the development of novel devices such as field effect, memristor and oscillator which contributes to enabling novel brain-inspired computing architectures for advanced machine learning and artificial intelligence applications.

• Processing of 2D materials-based devices (primarily MoS2), FET and memelements.
• Characterization using advanced techniques.
• Lithographic device fabrication employing electron beam lithography.
• Measurement of device transport properties at both high and low temperatures.
• Modeling and simulation of devices and calibration with experimental measurement data.
• Fabrication of large-scale memelements for functional circuits as a proof of concept on 2D TMD based ONN

Qualifications:

• Previous experience in cleanroom processing, electron microscopy, scanning probe microscopy, instrumentation programming, or device modeling and simulation.
• Excellent spoken and written English skills.
• Demonstrated proficiency in the relevant technical skills and methodologies.