MSE Department Seminar

Progress in neural interfacing devices is transforming our understanding of the brain and the treatment of neurological conditions and injuries. As Richard Feynman famously claimed in 1959, “there’s plenty of room at the bottom” regarding nanotechnology, I argue that the same is true today about neural device engineering. In this talk, I will support this argument by presenting recent neuroelectronic advances from my group, all of which are enabled by fundamental innovations in materials and devices, often at the nanoscale.

Nature creates remarkable material by controlling the hierarchical assembly of molecules that are broken down at the end of their useful life. Inspired by natural systems, my group’s research combines molecular design with directed assembly via extrusion-based 3D printing to program the structure and function of polymer-based materials across length scales. First, I will describe how in both liquid crystalline polymers and block copolymers we can control the extent of nanostructure alignment and functional anisotropy via the flow history the material undergoes during 3DP.

Oxide semiconductors such as In₂O₃ and ZnO combine high electron mobility with a large breakdown field and optical transparency in the visible spectrum. Indium-based oxides retain these desirable properties even in the amorphous phase, a feature that enabled the commercial adoption of InGaZnO (IGZO) n-channel field-effect transistors in pixel driver circuits for flat-panel displays more than a decade ago. Building on this success, oxide semiconductors are now being explored for advanced logic and memory applications.

Materials are at the heart of everything in the semiconductor industry, from the cell phones in our pockets to the mainframes powering global financial transactions. This talk will highlight some of the key challenges and opportunities for materials engineering in leading-edge semiconductor architectures. State-of-the-art interconnects can be fabricated with atomic-level precision to co-optimize electrical performance and reliability. First-principles-based simulation can help us identify promising candidate materials for next-generation interconnect wiring.

Deep Learning (DL) algorithms, recognized for their extensive capabilities in efficient image and video processing, have emerged as promising candidates for deployment within low-latency, mission-critical systems at the edge. Continuous research aimed at achieving high-performance DL execution has resulted in the development of several specialized, low-overhead inference hardware accelerators.

Quasicrystals are exotic materials that have symmetries that once thought to be impossible for matter. The first known examples were synthesized in the laboratory 40 years ago, but could Nature have beaten us to the punch? Many thought this was impossible. This talk will describe the decades-long search to prove them wrong, resulting in one of the strangest scientific stories you are ever likely to hear.

Transport phenomena play an essential role in designing and engineering materials with tailored functionalities. In this talk, I will highlight our group’s efforts in integrating ultrafast spectroscopy and material engineering to uncover the fundamental mechanisms and new phenomena that govern thermal transport and magnetization dynamics in functional materials.

There are two major challenges in integrating crystalline piezoelectric materials with soft biological systems for medical applications. First, they are inherently brittle and rigid, leading to significant mechanical mismatches with biological systems. Second, traditional piezoelectric materials are not degradable, either requiring removal or risking accumulation once implanted. In this talk, I will first discuss my works in engineering piezoelectric structures and devices with tissue/organ-mimetic properties through electric-field-assisted 3D printing.

My research group’s expertise lies in the development of physics-based molecular models and simulation methods as well as data-driven machine learning models for designing and characterizing soft macromolecular materials. In the past few years we have devoted significant efforts towards the development of machine learning based computational methods to accelerate and automate interpretation of structural characterization data from scattering and microscopy techniques.

Metallic delafossites such as PdCoO2 are layered oxides with a unique crystal structure: conductive Pd+ sheets alternate with insulating [CoO2]− layers. This layered crystal structure gives rise to two remarkable features. First, the Pd+ layers host quasi–two-dimensional electrons with conductivities comparable with elemental metals, enabling long mean free paths and unusual transport phenomena even in ultrathin films. Second, the polar stacking of charged layers produces surfaces and interfaces with strongly contrasting electronic properties.