Research

Based on well-established inorganic materials (i.e., Si, metal thin films, CVD- and ALD-deposited gate dielectrics, and etc.), electronic devices can be made stretchable by incorporating structurally-stretchable device designs. Stretchable devices made using this approach exhibit high electrical and mechanical performances, and we envision development of novel applications (particularly as biomedical devices) which were not possible using rigid electronic materials.

Intrinsically-stretchable devices can be more beneficial in various applications compared to structurally-stretchable devices, in terms of higher device density, higher mechanical freedom, and higher stability. Using state-of-the-art materials for conductors, semiconductors, and insulators, along with novel fabrication techniques, we envision development of intrinsically-stretchable electronic/optoelectronic devices with high durability and sustainability, and envision their use in versatile applications including bio-integrated systems, robotic systems, and AR/VR technologies.

Stretchable devices, either structurally stretchable or intrinsically stretchable, suffer from performance degradation due to fatigue accumulation after experiencing repetitive deformations. Recently, machine learning (ML) algorithms have been highlighted as effective tool that can overcome the fatigue-related issues in these soft devices. As such, there are numerous applications where ML algorithms can be employed to improve the device performance and enable new functionalities, and we envision their integration with our soft devices for development of multi-functional, highly-efficient systems which can be worn or implanted in our body to promote public health and help those in need.