The increase in global population and urbanization is presenting significant challenges to society: space is becoming increasingly scarce, demand is exceeding capacity for deteriorating infrastructure, transportation is fraught with congestion, and environmental impacts are accelerating. Underground space, and particularly tunnels, has a key role to play in tackling these challenges. However, the cost, risk, uncertainty, and complexity of the tunneling process have impeded its growth. In this paper, we envision several technological advancements that can potentially innovate and transform the mechanized tunneling industry, including artificial intelligence (AI), autonomous, and bio-inspired systems. The proliferation of AI may assist human engineers and operators in making informed decisions systematically and quantitatively based on massive real-time data during tunneling. Autonomous tunneling systems may enable precise and predictable tunneling operations with minimal human intervention and facilitate the construction of massive and large-scale underground infrastructure projects that were previously challenging or unfeasible using conventional methods. Bio-inspired systems may provide valuable references and strategies for more efficient tunneling design and construction concepts. While these technological advancements can offer great promise, they also face considerable challenges, such as improving accessibility to and shareability of tunneling data, developing robust, reliable, and explainable machine learning systems, as well as scaling the mechanics and ensuring the applicability of bio-inspired systems from the prototype level to real-world applications. Addressing these challenges is imperative to ensure the successful implementation of these innovations for future tunneling.

A scanning tunneling microscope (STM) requires precise control of the tip–sample distance to maintain a constant set-point tunneling current. Typically, the tip–sample distance is controlled through the use of a control algorithm. The control algorithm takes in the measured tunneling current and returns a correction to the tip–sample distance in order to achieve and maintain the set-point value for tunneling current. We have developed an STM simulator to test the accuracy and performance of four control algorithms. The operation and effectiveness of these control algorithms are evaluated.

Half-metals are unusual ferromagnets that have electrons at the Fermi level in a single spin state, either spin up or spin down. Of potential interest as sources and analyzers of polarized electrons in spintronic devices, they are usually identified from spin-dependent band-structure calculations. We present a classification scheme for half-metals and then discuss methods for measuring spin polarization based on point contacts or tunnel junctions with ferromagnetic or superconducting counter electrodes. Oxide examples include CrO2, the best-studied half-metal. The half-metallicity tends to be destroyed by increasing temperature and by structural defects. The half-metals that currently offer the best prospects for spintronics applications are those with the highest Curie temperatures, such as magnetite, Fe3O4, and perhaps oxide semiconductors such as Co-doped ZnO.