A glimpse into the future of computing drew strong interest today as leading researcher Prof. Francesca Iacopi outlined how nanoelectronics could transform everything from artificial intelligence to brain–computer interfaces. Her talk, part of the QAMSS lecture series at the Cambridge Graphene Centre, challenged the limits of current technology while offering a roadmap toward faster, more energy-efficient systems.
Prof. Iacopi explained that simply shrinking devices is no longer enough to meet growing computational demands. Instead, the field is shifting toward system-level co-optimization and entirely new architectures. Traditional von Neumann computing, she noted, struggles with complex optimisation and decision-making tasks due to high energy use and slow processing times, limitations that become critical in autonomous systems.
A key focus of the talk was in-memory computing, particularly approaches based on magnetic tunnel junctions and magnetoresistive random access memory (MRAM) devices. These technologies enable data processing directly where information is stored, dramatically reducing energy consumption while improving performance in large-scale optimisation and edge inference tasks.
Layered materials were another central theme, with graphene highlighted as a versatile and dynamically tuneable option that can complement conventional complementary metal-oxide-semiconductor (CMOS) technologies. Prof. Iacopi described how such materials could unlock compact, multifunctional nanosystems with capabilities beyond today’s electronics.
She concluded by presenting a wafer-scale graphene-on-silicon-carbide-on-silicon platform. This emerging technology supports applications ranging from integrated energy storage and reconfigurable mid-infrared metasurfaces to advanced electroencephalography electrodes for next-generation brain–computer interfaces.
The talk painted a clear picture of a future where breakthroughs in materials and architecture redefine what electronics can achieve.