Ruomeng studied Physics in China, receiving a BSc in 2008 and a MEd in 2009. He came to Southampton in 2009 where he obtained a MSc in Nanoelectronics and Nanotechnology in 2010 and a PhD investigating the confined nanoscale chalcogenide phase change material and memory in 2015.
Following his PhD, Ruomeng was awarded an EPSRC Doctoral Prize fellowship in 2015 to start his independent research in functional chalcogenides and metal oxides with particular focus on application of novel non-volatile memory technologies (phase change memory and resistive switching memory) and thermoelectric (TE) materials and energy harvester. In 2018, he was appointed a lectureship in the Sustainable Electronic Technologies Group in the School of Electronics and Computer Science in the University of Southampton. He was a Co-Investigator in a STFC grant (Selective Chemical Vapour Deposition for Production of Thermoelectric Micro-Generators for Energy Harvesting, £363k) for the development of thin film thermoelectric generators. He is also working in a EPSRC program grant (ADEPT – Advanced Devices by ElectroPlaTing, £6.33m) which explores the state of the art of electrodeposition and device design at the nanoscale in the areas of thermoelectrics, infrared detection, and phase change materials.
Ruomeng has published over 50 journal papers (Google Scholar) and delivered over 40 oral/poster presentations at national and international conferences. He is a regular reviewer of several journals from ACS, RSC and IEEE.
- 1. Thermoelectric materials and generators
- High performance thermoelectric binary and ternary materials (e.g. SnSe, Bi2Te3, Sb2Te3, BiSeTe, etc.)Thermoelectric generator design and optimisation enabled by AI technologies (e.g. deep learning) 2. Novel non-volatile memory and neuromorphic devices
- Chalcogenide materials (e.g. GeSbTe) based phase change and resistive switching memory via electrodepositionTunable metal oxdie thin film (e.g. ZnO, ZrOx, HfO2) based resistive switching memoryBack-end-of-line SiC thin film based resistive switching and neuromorphic devicesSolution based resistive switching and neuromorphic devices on flexible substrates 3. Deep-learning enabled structural colour design and optimisation
- Inverse design of F-P cavity based colour filter by deep learning technologyThermal and electrical controlled dynamic structural colour
ELEC1206: Electrical Materials and Fields
ELEC2230: Semiconductor Devices, Materials and Sensors
ELEC3207/6256: Nanoelectronic Devices
ELEC3208: Analogue and Signal Processing
ELEC6200: Group Design Project