Merlyne of University of Sheffield

"Innovation distinguishes between a leader and a follower." 
 -Steve Jobs 

🚀 Dive into  the Future of Computing! 

🔬 Physical Reservoir Computing Using a Solid Electrolyte FET 

Join us for an exclusive Distinguished Lecture Program featuring groundbreaking insights into neuromorphic computing and next-gen semiconductor technologies. 

📅 Date: February 15, 2025
🕒 Time: 6:30 PM
🌐 Mode: Online (Google Meet)

🎤 Expert Speaker: 
Dr. Maria Merlyne De Souza
Professor of Microelectronics, University of Sheffield

💡 Cutting-edge Technology: Learn how solid electrolyte FETs are shaping AI hardware.
🧠 Neuromorphic Advancements: Understand the role of physical reservoir computing in real-time data processing.
🚀 Future Prospects: Explore innovations in energy-efficient semiconductor devices.

Biography:

I graduated with a BSc in Physics and Mathematics (1985) from the University of Mumbai, a BE. in Electronics and Communications Engineering (1988) from the Indian Institute of Science, Bangalore and a PhD from the University of Cambridge (1994).

I joined as a Junior Research fellow in ‘95, was promoted to a Senior Research fellow in ‘98 and was appointed Professor in Electronics and Materials at the Emerging Technologies Research Centre, De Montfort University in 2003.

I joined the EEE department at Sheffield as Professor of Microelectronics in 2007. I work in multi-disciplinary research focused on the physics of devices, materials and their microelectronic applications in computing, communications and energy conversion.

Until now, microelectronics has relied on the versatility of silicon CMOS to deliver enhancement in performance by scaling the MOS transistor. I have worked on various aspects of CMOS such as reliability, high-k gate oxides and Indium for retrograde channels, first introduced in production at the 65 nm node.

However, scaling (as we know it) is now nearing an end and alternate materials and device architectures are required for future semiconductor applications.

Supervised learning for image and speech recognition, autonomous driving and medical diagnosis in Artificial Intelligence (AI) presently rely on CMOS based deep neural networks.

These are inherently power-hungry due to a continuous exchange of information between the required large volume of memory and processing units.

It is expected that such Von Neumann architectures will be replaced by neuromorphic systems that are more akin to a biological brain.

Our team has recently demonstrated ZnO/Ta2O5 solid electrolyte thin film transistors with synaptic capabilities.

I am interested in exploring such memristive devices in neuromorphic applications, electrochemical storage and flexible electronics for health.

My interest in more efficient semiconductors, smart materials and systems that leave a smaller footprint on the environment, spans to GaN for power and RF applications, that I have previously explored in equivalent silicon- based device technologies such as the IGBT and the RF LDMOSFET.

These are driven by the automotive, aerospace, space, renewables, telecoms and consumer/industrial electronics sectors.

Our recent work includes a new class of harmonic RF power amplifiers with record efficiency and output power prototyped using commercial GaN devices.

We are also working towards a p-type MOSHFET and magnetic thin films for “CMOS in GaN” in power management integrated circuits and current sensors.

Email:

Address:Professor of Microelectronics, University of Sheffield, , Sheffield, United Kingdom