ArticleSmall (Weinheim an der Bergstrasse, Germany)2026
Virtualization as a New Scaling Law for Semiconductor Devices Beyond Geometric Scaling.
Article in Small (Weinheim an der Bergstrasse, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As Moore's-law-driven geometric scaling nears physical, economic, and sustainability limits, semiconductor progress is increasingly constrained by the cost and latency of physical iteration. This Perspective argues that AI enables virtualization as a complementary scaling law: progress scales with how much trustworthy virtual evidence can replace exhaustive fabrication, testing, and qualification across the device lifecycle. We show how virtualization emerges in i) design and modeling via surrogate and physics-informed learning, inverse design, and uncertainty-aware exploration; ii) fabrication and packaging via digital twins, virtual metrology, and reinforcement learning; and iii) qualification via defect inference and reliability modeling that provide earlier risk signals. We outline boundary conditions-trust and uncertainty, cross-stage coherence, sustainability, and governance-and argue that future innovation will depend not only on geometric shrinkage, but also on the fidelity, integration, and stewardship of virtual evidence.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.