My Ph.D. research primarily revolves around nanomaterial synthesis and nanoelectronics fabrication, including low-dimensional materials for transistors and memory devices, GaN-based LED arrays for optogenetics research with neural cells, solution-processed electronics for fundamental logic computations, etc. This focus has solidified my expertise in the fundamental aspects of semiconductor device physics, encompassing a wide range of components.
My extensive research experience has positioned me as a specialist in experimental work, with a profound understanding and adept hands-on skills in semiconductor fabrication processes. Moreover, I demonstrates a remarkable level of proficiency in materials characterizations and electronic device measurements, establishing me as a highly valuable asset to both the research and engineering communities. Beyond my professionalism, I exhibit an exceptional aptitude for swiftly grasping and mastering new techniques, showcasing a remarkable ability to promptly acquire and apply knowledge within a professional setting.
Check out more details of my selected Ph. D. Projects below!
Atomically Thin Amorphous Carbon Films Synthesized from Solution Precursor
A scalable and solution-based strategy to prepare large-area and freestanding atomically thin amorphous carbon films as novel dielectrics for nanoelectronics including 2D transistors and memristors with unprecedented performance and uniformity.
Solution-Processed Electronic Materials for High-Performance Thin-Film Transistors
This research projects targets at developing new solution-based approaches to deposit various chalcogenide-based electronic materials, mainly focusing on CuIn5Se8, for their integration into high performance thin film transistors. Compared with conventional vacuum-based material deposition processes such as physical vapor deposition and chemical vapor deposition, solution-based process has the potential to achieve much faster throughput under lower equipment capital cost.
CMOS-Compatible and Scalable Electrochemical Synaptic Transistor Arrays for Deep-Learning Accelerator
In-memory-computing architectures based on memristive crossbar-arrays can enhance the computing efficiency for deep-learning with massive parallelism. However, to fulfill their potential, the core memory devices must be capable of providing high-speed and symmetric analog programing with small variability, compatible with silicon technology, and scalable into nanometer-size footprint. We present an electrochemical synaptic transistor, built with CMOS-compatible metal oxides and operating by shuffling protons within a symmetric gate stack, to meet all these stringent requirements.
Synthetic Neurocomputers for Cognitive Information Processing
The neurocomputer prototype pursued in the project employs biological neuronal circuits engineered into well-defined 3D topologies reminiscent of deep-neural-network models as the information-processing units. Electronic and optoelectronic devices will be integrated with each neural cell to administer and monitor the neuronal and synaptic activities based on electrophysiology, optogenetics, and neurochemistry. The fabricated neurocomputer prototype will then be utilized to perform various learning and computing tasks such as image recognition and space navigation.