(Aug 23) Material challenges and opportunities in next generation electronics: from non-silicon electronics to artificial neural networks

The current electronics industry has been completely dominated by Si-based devices due to its
exceptionally low materials cost. However, demand for non-Si electronics is becoming substantially high
because current/next generation electronics requires novel functionalities that can never be achieved by
Si-based materials. Unfortunately, the extremely high cost of non-Si semiconductor materials prohibits
the progress in this field. I will discuss about my group’s efforts to address these issues. Our team has
recently conceived a new crystalline growth concept, termed as “remote epitaxy”, which can copy/paste
crystalline information from the wafer remotely through graphene, thus generating single-crystalline films
on graphene [1-2]. These single-crystalline films can be easily released from the slippery graphene
surface, and the graphene-coated substrates can be reused infinitely to generate single-crystalline films.
Therefore, the remote epitaxy technique can produce expensive non-Si semiconductor films with
unprecedented cost efficiency while allowing additional flexible device functionality required for current
ubiquitous electronics.
Lastly, I will discuss about an ultimate alternative computing solution that does not follow the
conventional von Neuman method. As Moore’s law approaches its physical limits, brain-inspired
neuromorphic computing has recently emerged as a promising alternative because of its compatibility
with AI. In the neuromorphic computing system, resistive random access memory (RRAM) can be used
as an artificial synapse for weight elements in neural network algorithms. RRAM typically utilizes a
defective amorphous solid as a switching medium. However, due to the random nature of amorphous
phase, it has been challenging to precisely control weights in artificial synapses, thus resulting in poor
learning accuracy. Our team recently demonstrated single-crystalline-based artificial synapses that show
precise control of synaptic weights, promising superior online learning accuracy of 95.1% – a key step
paving the way towards post von Neumann computing [3]. I will discuss about how we design the
materials and devices for this new neuromorphic hardware.