Optical microscopy image of the microLED chips.

March issue now out

This month’s issue features a self-healing conductive organogel, interconnects for superconducting quantum processors, and the wafer-scale alignment of microLEDs.


  • London skyline.

    To mark the first five years of Nature Electronics, we will be publishing a series of short articles in 2023 that explore key topics in the field through the research that has been featured in the pages of the journal.

Nature Electronics is a Transformative Journal; authors can publish using the traditional publishing route OR via immediate gold Open Access.

Our Open Access option complies with funder and institutional requirements.


  • A millimetre-wave dual-rail resonator that is incorporated into a suspended lithium niobate resonator can provide efficient electromechanical transduction in the sub-terahertz regime.

    • Jiacheng Xie
    • Mohan Shen
    • Hong X. Tang
  • The Curie temperature of Fe5+xGeTe2 thin films can be modulated from 260 to 380 K via iron doping, allowing the two-dimensional material to be used to create planar spiral inductors and low-pass Butterworth filters.

    • Zihan Li
    • Shanshan Liu
    • Faxian Xiu
  • An organogel that is based on poly(vinyl alcohol)–sodium borate and contains a percolating conductive network of silver particles and liquid metal microdroplets exhibits spontaneous mechanical and electrical self-healing, as well as an electrical conductivity of 7 × 104 S m−1.

    • Yongyi Zhao
    • Yunsik Ohm
    • Carmel Majidi
  • Micro-light-emitting diodes — microLEDs — could be used to create the next generation of displays, for use in smartwatches and augmented reality devices, if various fabrication issues can be addressed.

  • Micro-light-emitting-diode display applications are growing quickly as technology companies begin to use them in a range of products. Key to the development of these applications was the miniaturization of gallium nitride light-emitting diodes. Hongxing Jiang and Jingyu Lin recount how this was achieved.

    • Hongxing Jiang
    • Jingyu Lin
    Reverse Engineering

Neuromorphic computing

The rise of machine learning and artificial intelligence is asking questions about what is the best way to build a computer, and approaches that derive inspiration from the brain could provide an answer. Here, in a series of articles, we explore what such neuromorphic computing can do.