STM (Scanning Tunneling Microscopy)

last updated 2026-08-31 · +8 sources in last 30d
Assessment draft · unreviewed
Viability
4/5
Drivers
3/5
Novelty
3/5
Diffusion
3/5
Impact
3/5

Timing Now (0-2yr) · Read Fairly rated

ARPES (Angular-Resolved Photoemission Spectroscopy) ARPES (Angular-Reso… Electron-scale metrology Electron-scale metr… STM (Scan…

Scanning tunnelling microscopy resolves the electronic structure of surfaces atom by atom, and across 2026 it is simultaneously the default characterisation tool for quantum materials and an early-stage fabrication tool being pushed towards positionally controlled atom-by-atom synthesis.

Summary

Scanning tunnelling microscopy (STM) brings a sharp conductive tip within a fraction of a nanometre of a conductive surface and measures the current that tunnels across the gap. Because that current depends exponentially on the gap width, a feedback loop holding it constant while the tip raster-scans produces a map with atomic lateral resolution. Sweeping the bias instead of scanning gives scanning tunnelling spectroscopy (STS), which reads out the local density of electronic states at a chosen point, so the instrument returns both structure and local electronic structure from the same measurement. Variants extend this: spin-polarised STM uses a magnetic tip to read spin contrast and to couple magnetically to the sample, the field-emission regime accesses image potential states above the surface, and quasiparticle interference (QPI) mapping around defects converts real-space standing waves into momentum-space information.

The manufacturing claim is separate and much younger. Inverted-mode STM with functionalised molecular tools has been used to demonstrate positionally controlled donation of carbon atoms and abstraction of silicon atoms at a model build site on an atomically clean, cryogenic silicon surface. That is a chemistry demonstration at single-site scale, not a process.

The parameters that decide STM’s usefulness are: sample conductivity (insulating films need workarounds, which is why photoexcitation sensing on dielectric films has been moved to AFM instead); vacuum and cryogenic stability; the unknown and drifting state of the tip; serial single-tip throughput; and the human labour of labelling and interpreting the images, which is now being attacked with few-shot and unsupervised segmentation and with machine-learning regression of physical parameters directly from image data.

Evidence base

Open questions


Assessment drafted 2026-08-31 from up to 18 KB sources using the technology-scorecard framework; scores are a draft read pending review.

Related concepts

Frontier questions