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Xuanzhe Sha

Publications and source records attributed to Xuanzhe Sha.

2 recordsLinked to original sources

In-situ operation of amorphous circuits under heavy-ion irradiation

Radiation-hardened electronics using semiconductors beyond silicon are essential for computation and control in extreme environments. Yet complex digital circuits based on such material platforms operating in situ under heavy-ion irradiation remain largely unexplored. Here, we show a timing circuit based on amorphous thin-film semiconductors at the 100-transistor scale, and demonstrate its robust operation through a functional "Hello World" ASCII output sequence. Beyond static device characterization, we evaluate the circuit under powered heavy-ion irradiation using tantalum ions, providing an operationally relevant assessment of radiation tolerance at the system level. Under a high particle flux of 2.5 x 10^3 ions cm^-2 s^-1, the circuit maintains stable operation during the irradiation test, achieving a total fluence of 1 x 10^6 ions cm^-2, establishing a milestone of prolonged powered digital operation under extreme conditions. Our work expands the design space of radiation-tolerant electronics, highlighting amorphous semiconductors as a promising foundation for digital circuits deployed in harsh environments.

cond-mat.mtrl-sci

An AI-driven robotic system for two-dimensional hetero-assemblies

Nanomaterials stacked on-demand, such as rotationally assembled two-dimensional (2D) van der Waals (vdW) layered compounds, provides a versatile platform for quantum simulation and the exploration of exotic electronic phases. Currently, however, such nanoassemblies remain largely confined to inefficiency, manually operated process, limiting their potential for probing emergent physical phenomena. There is a pressing need in the field for high-precision, automated assembling techniques, especially for the scalable fabrication of 2D twistronic heterostructures. Here, we present an intelligent automation system dedicated to the fabrication of van der Waals stacks, following the state-of-the-art protocol for dry transfer of exfoliated 2D materials. The system further employs metadata generated from each automated stacking procedure to perform reinforcement learning, thereby continuously bettering its performances. As a concrete demonstration, we fabricate twisted bilayer graphene (TBLG) -- known for its challenging preparation -- and exhibit its unconventional superconductivity near the magic angle. Our work may pave the way for high-throughput fabrication of low-dimensional nanomaterials including twistronic heterostructures, where integrating data mining and artificial intelligence can accelerate the discovery of novel physical phenomena.

cond-mat.mes-hall