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- A Neuromorphic Ferroelectric field-effect Ultra-Scaled Chip for Spiking Neural Networks
A Neuromorphic Ferroelectric field-effect Ultra-Scaled Chip for Spiking Neural Networks
Supervisors
Dr Saeed Afshar & Dr Mohammad Khaleqi Qaleh
Description
Are you passionate about cutting-edge technology and eager to push the boundaries of neuromorphic computing? We are seeking a highly motivated PhD student to join our research team, focused on developing ultra-scaled neuromorphic hardware utilizing ferroelectric/anti-ferroelectric field-effect transistors. Ferroelectric field-effect Transistors (FeFETs) are emerging devices, in which a ferroelectric capacitor is integrated in the gate stack of a baseline transistor above the dielectric. The negative capacitance behavior of the FeFET leads to unique characteristics: (i) sub 60mV/decade sub-threshold swing for low-power logic and (ii) non-volatile memory applications (thanks to the polarization retention in the absence of an electric field). The memory and computation integration ability in a single FeFET, supports persistent learning and long-term memory functions in neuromorphic circuits.
Our team aims to explore and leverage FeFET technologies to create advanced neuromorphic hardware that can mimic the brain's efficiency and scalability. This project involves modeling and hardware designing using FeFET devices, integrating them into neuromorphic circuits, and evaluating their performance in real-world applications. Qualifications: Educational Background: Master’s degree in Electronics Engineering or Computer Engineering (Hardware). Technical Expertise: Strong background in semiconductor device modeling, electronic devices, VLSI circuits, and hardware design. Experience with FeFET technology and non-silicon-based devices is a privilege. Skills: Proficiency in relevant software tools (HSPICE, Cadence, and physical design) and familiarity with programming languages (Verilog-A (is mandatory), Python, MATLAB). Strong analytical and problem-solving skills.
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- Task-Driven Model Evaluation in Large-Scale Spiking Neural Networks
- A Neuromorphic Ferroelectric field-effect Ultra-Scaled Chip for Spiking Neural Networks
- Event Based Wavefront Sensing Modalities
- Physics-Based Encoding for Spiking Neural Networks
- Neuromorphic Computational Imaging
- Defining Performance Metrics for Closed Loop Event Based Imaging Systems
- A Neuromorphic Framework for Event-Based DNNs using Minifloats
- A RISC-V instruction set architecture (ISA) extensions for neuromorphic computing using minifloats
- Astrometry with Event-based Vision Sensors
- Automatic Evaluation of Bushfire Risk via Acoustic Scene Analysis
- Bio-inspired Sensors for Space Situational Awareness
- Building a Neuromorphic Auditory Pathway for Sensing the Surrounding Environment
- Cold Start Astrometry for High-Precision Airspace and Space Objects Tracking with Neuromorphic Cameras
- Control Systems Inspired by Insect Central Pattern Generators that can Adapt to Dynamic Environments.
- Design of Neuromorphic Spiking Neural Networks for Real-Time Processing
- Enhanced Maritime Situational Awareness with Neuromorphic Cameras
- Environmental Situational Awareness using Neuromorphic Vision Sensors and IMU-based SLAM
- Fault Tolerant Distributed Swarm Intelligence using Neuromorphic Computing and Local Learning Principles
- Honey Bee Waggle Dance Detection via Neuromorphic Engineering
- Integrated Circuit Design for Event-based Vision Sensors
- Low-Power Acoustic Ecological Monitoring in Remote Areas using Machine Learning and Neuromorphic Engineering
- Neuromorphic Computing in Extreme Environments
- Neuromorphic Cyber Security at the Edge
- Neuromorphic Engineering for Acoustic Aerial Drone Detection in Visually Obscured Environments
- Machine Learning-Based Tool for Therapists to Monitor Speech Progress in Late Talkers
- Machine Learning for Automated Child Reading Assessment and Intervention
- Underwater Acoustic Drone Detection via Neuromorphic Models of Marine Mammal Audition
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