Wafer-scale two-photon lithography; detecting hidden defects; AFM plus AI.
Researchers from Lawrence Livermore National Laboratory (LLNL) and Stanford University demonstrated a two-photon lithography (TPL) platform for wafer-scale manufacturing.
The TPL platform uses large arrays of metalenses to split a femtosecond laser into more than 120,000 coordinated focal spots that write simultaneously across centimeter-scale areas. The approach can create 3D architectures with minimum feature sizes of 113 nanometers. It is also much faster than current systems.
“During the project, we realized that by dynamically switching the focal spots on and off and carefully planning the printing trajectory, we can actually print fully stochastic structures with a high degree of parallelization,” said Xiaoxing Xia, an LLNL materials engineer, in a press release.
To print structures that are not fully periodic, the team integrated a spatial light modulator that adjusts the intensity of each focal spot in real time. The system can switch beams on or off, tune linewidths with grayscale control, and choreograph beams to form larger patterns layer by layer.
“It means TPL finally has the potential for industry adoption,” said Songyun Gu, a postdoctoral researcher at LLNL, in a press release. “Previously it was purely an experimental tool for researchers. With wafer-scale nanomanufacturing, we have the potential to make nanomaterials and microdevices the same way we make computer chips, which are highly complex but can be made in volume at very low unit cost. And meta-optics is exactly the solution.” [1]
Researchers from the Korea Advanced Institute of Science and Technology (KAIST), IBM T. J. Watson Research Center, and Korea Institute of Energy Research developed a measurement technique that can simultaneously analyze electronic trap defects that hinder electrical transport as well as charge carrier transport properties inside semiconductors.
The approach uses Hall measurements with the addition of controlled light illumination and temperature variation. Under weak illumination, newly generated electrons are first captured by electronic traps. As the light intensity is gradually increased, the traps become filled, and subsequently generated electrons begin to move freely. By analyzing this transition process, the researchers were able to precisely calculate the density and characteristics of electronic traps. The researchers say the method has approximately 1,000 times higher sensitivity than conventional techniques.
“This study presents a new method that enables simultaneous analysis of electrical transport and the factors that hinder it within semiconductors using a single measurement,” said Byungha Shin, a professor in the Department of Materials Science and Engineering at KAIST, in a statement. “It will serve as an important tool for improving the performance and reliability of various semiconductor devices, including memory semiconductors and solar cells.” [2]
Researchers at Oak Ridge National Laboratory (ORNL), University of Texas at Arlington, and National Cheng Kung University modified a commercial atomic force microscope with AI to assemble and detect patterns in bismuth ferrite, a potential ferroelectric data storage material.
“With AI, we can use the atomic force microscopy tip to align the electric polarization at the nanoscale, so we can write, read and erase these patterns — known as topological structures — on demand,” said Marti Checa, an R&D staff scientist at ORNL, in a statement.
The method allows for the creation of highly tunable and intricate topological domain structures that exhibit distinct polarization configurations without invasive electrode deposition. Such topological domains could enable beyond-binary memory devices. [3]
[1] S. Gu, C. Mao, A. Guell Izard, et al. 3D nanolithography with metalens arrays and spatially adaptive illumination. Nature 648, 591–599 (2025). https://doi.org/10.1038/s41586-025-09842-x
[2] O. Gunawan, C. Kim, B. Nainggolan, et al. Electronic trap detection with carrier-resolved photo-Hall effect. Sci. Adv. 12, eadz0460 (2026). https://doi.org/10.1126/sciadv.adz0460
[3] M. Checa, R. Millan-Solsona, Y. Liu, et al. Autonomous Multistate Nanoencoding Using Combinatorial Ferroelectric Closure Domains in BiFeO3. ACS Nano 2025 19 (30), 27692-27701 https://dx.doi.org/10.1021/acsnano.5c07423
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