What is he best known for?
The fields of study he is best known for:
- Semiconductor
- Silicon
- Transistor
His primary scientific interests are in Optoelectronics, Electronic engineering, Layer, Silicon and Electrical engineering.
He is studying Doping, which is a component of Optoelectronics.
His study in Electronic engineering is interdisciplinary in nature, drawing from both Semiconductor materials, Semiconductor and MOSFET.
In the subject of general Layer, his work in Nitride is often linked to Anatase, thereby combining diverse domains of study.
Kevin K. Chan interconnects Threshold voltage and Voltage in the investigation of issues within Silicon.
His studies in Voltage integrate themes in fields like Quantum tunnelling and Nanostructure.
His most cited work include:
- A silicon nanocrystals based memory (1520 citations)
- Single charge and confinement effects in nano-crystal memories (324 citations)
- Self-aligned nanotube field effect transistor and method of fabricating same (259 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Optoelectronics, Layer, Electronic engineering, Silicon and Electrical engineering.
His Optoelectronics research is multidisciplinary, incorporating perspectives in Substrate and Epitaxy.
Kevin K. Chan combines subjects such as Oxide, Deposition and Base with his study of Layer.
His Electronic engineering study also includes
- MOSFET that intertwine with fields like Capacitance,
- Fin which connect with Fin field effect transistor.
As part of one scientific family, he deals mainly with the area of Silicon, narrowing it down to issues related to the Metal, and often Composite material.
His work investigates the relationship between Electrical engineering and topics such as Semiconductor device that intersect with problems in Organic field-effect transistor and Thin-film transistor.
He most often published in these fields:
- Optoelectronics (74.78%)
- Layer (30.87%)
- Electronic engineering (29.13%)
What were the highlights of his more recent work (between 2012-2020)?
- Optoelectronics (74.78%)
- Semiconductor (17.39%)
- Epitaxy (17.83%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Optoelectronics, Semiconductor, Epitaxy, Common emitter and Electronic engineering.
His Optoelectronics study combines topics in areas such as Layer and Transistor.
His Epitaxy research is multidisciplinary, relying on both Trench, Structural engineering and Dielectric.
His work deals with themes such as Heterojunction bipolar transistor, Bipolar junction transistor, Pedestal and Base, which intersect with Common emitter.
The study incorporates disciplines such as Metal gate, Semiconductor device, Silicon, Germanium and Metal in addition to Electronic engineering.
His Silicon research incorporates themes from Ion implantation and Wafer.
Between 2012 and 2020, his most popular works were:
- ECRAM as Scalable Synaptic Cell for High-Speed, Low-Power Neuromorphic Computing (34 citations)
- Self-forming spacers using oxidation (29 citations)
- High Mobility High-Ge-Content SiGe PMOSFETs Using Al 2 O 3 /HfO 2 Stacks With In-Situ O 3 Treatment (21 citations)
In his most recent research, the most cited papers focused on:
- Semiconductor
- Transistor
- Integrated circuit
His primary areas of investigation include Optoelectronics, Insulator, Bipolar junction transistor, Epitaxy and Common emitter.
His Optoelectronics research is multidisciplinary, incorporating elements of Electronic engineering and Passivation.
Kevin K. Chan studied Electronic engineering and Layer that intersect with Auger effect.
His research integrates issues of Semiconductor materials and Nanotechnology in his study of Insulator.
His work focuses on many connections between Bipolar junction transistor and other disciplines, such as Germanium, that overlap with his field of interest in Oxide thin-film transistor and Thin-film transistor.
His Common emitter study which covers Optics that intersects with Nucleation.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
