Material Challenges and Opportunities in Next Generation Electronics: From Non-Silicon Electronics to Artificial Neural Networks: Nano Seminar Series
Seminar | April 26 | 2-3 p.m. | 4 LeConte Hall
Prof. Jeehwan Kim, MIT, Mechanical Engineering & MSE
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. Recently our team has invented a new crystalline growth concept, termed as remote epitaxy, which can copy/paste crystalline information of the wafer remotely through graphene, thus generating single-crystalline films on graphene. These single-crystalline films are easily released from the slippery graphene surface and the graphene-coated substrates can be infinitely reused to generate single-crystalline films.
Thus, the remote epitaxy technique can cost-efficiently produce freestanding single-crystalline films. This allows unprecedented functionality of flexible device functionality required for current ubiquitous electronics. In addition, we have recently demonstrated a manufacturing method to manipulate wafer-scale 2D materials with atomic precision to form monolayer-by-monolayer stacks of wafer-scale 2D material heterostructures.
In this talk, I will discuss the implication of this new technology for revolutionary design of next generation electronic/photonic devices with combination of 3D/2D materials.
Lastly, I will discuss about an ultimate alternative computing solution that does not follow the conventional von Neuman method. As Moores 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. I will discuss about how we design the materials and devices for this new neuromorphic hardware.