A non-layered two-dimensional semiconductor for p-type transistors
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Switching on and off the spin polarization of the conduction band in antiferromagnetic bilayer transistors
Antiferromagnetic conductors with suitably broken spatial symmetries host spin-polarized bands, which lead to transport phenomena commonly observed in metallic ferromagnets. In bulk materials, it is the given crystalline structure that determines whether symmetries are broken and spin-polarized bands are present. Here we show that, in the two-dimensional limit, an electric field can control the relevant symmetries. To this end, we fabricate a double-gate transistor based on bilayers of van der Waals antiferromagnetic semiconductor CrPS4 and show how a perpendicular electric displacement field can switch the spin polarization of the conduction band on and off. Because conduction band states with opposite spin polarizations are hosted in the different layers and are spatially separated, these devices also give control over the magnetization of the electrons that are accumulated electrostatically. Our experiments show that double-gated CrPS4 transistors provide a viable platform to create gate-induced conductors with near unity spin polarization at the Fermi level, as well as devices with a full electrostatic control of the total magnetization of the system.
Brine management with zero and minimal liquid discharge
Zero liquid discharge (ZLD) and minimal liquid discharge (MLD) are brine management approaches that aim to reduce the environmental impacts of brine discharge and recover water for reuse. ZLD maximizes water recovery and avoids the needs for brine disposal, but is expensive and energy-intensive. MLD (which reduces the brine volume and recovers some water) has been proposed as a practical and cost-effective alternative to ZLD, but brine disposal is needed. In this Review, we examine the concepts, technologies and industrial applications of ZLD and MLD. These brine management strategies have current and potential applications in the desalination, energy, mining and semiconductor industries, all of which produce large volumes of brine. Brine concentration and crystallization in ZLD and MLD often rely on mechanical vapour compression and thermal crystallizers, which are effective but energy-intensive. Novel engineered systems for brine volume reduction and crystallization are under active development to achieve MLD and/or ZLD. These emerging systems, such as membrane distillation, electrodialytic crystallization and solvent extraction desalination, still face challenges to outcompete mechanical vapour compression and thermal crystallizers, underscoring the critical need to maximize the full potential of reverse osmosis to attain ultrahigh water recovery. Brine valorization has potential to partially offset the cost of ZLD and MLD, provided that resource recovery can be integrated into treatment trains economically and in accordance with regulations.
Latent circuit inference from heterogeneous neural responses during cognitive tasks
Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by confirming the behavioral effects of patterned connectivity perturbations predicted by the latent circuit model. We find similar suppression of irrelevant sensory responses in the prefrontal cortex of monkeys performing the same task. We show that incorporating causal interactions among task variables is critical for identifying behaviorally relevant computations from neural response data.
The logarithmic memristor-based Bayesian machine
The demand for explainable and energy-efficient artificial intelligence (AI) systems for edge computing has led to growing interest in electronic systems dedicated to Bayesian inference. Traditional designs of such systems often rely on stochastic computing, which offers high energy efficiency but suffers from latency issues and struggles with low-probability values. Here, we introduce the logarithmic memristor-based Bayesian machine, an innovative design that leverages the unique properties of memristors and logarithmic computing as an alternative to stochastic computing. We present a prototype machine fabricated in a hybrid CMOS/hafnium-oxide memristor process. We validate the versatility and robustness of our system through experimental validation and extensive simulations in two distinct applications: gesture recognition and sleep stage classification. The logarithmic approach simplifies the computational model by converting multiplications into additions and enhances the handling of low-probability events, which are crucial in time-dependent tasks. Our results demonstrate that the logarithmic Bayesian machine achieves superior performance in terms of accuracy and energy efficiency compared to its stochastic counterpart, particularly in scenarios involving complex probabilistic models. This approach enables the development of energy-efficient and reliable AI systems for edge devices.
Photonic-crystal surface-emitting lasers
High-performance lasers are important to realize a range of applications including smart mobility and smart manufacturing, for example, through their uses in key technologies such as light detection and ranging (LiDAR) and laser processing. However, existing lasers have a number of performance limitations that hinder their practical use. For example, conventional semiconductor lasers are associated with low brightness and low functionality, even though they are compact and highly efficient. Conventional semiconductor lasers therefore require external optics and mechanical elements for reshaping and scanning of emitted beams, resulting in large, complicated systems for various practical uses. Furthermore, even with such external elements, the brightness of these lasers cannot be sufficiently increased for use in laser processing. Similarly, gas and solid-state lasers, while having high-brightness, are also large and complicated. Photonic-crystal surface-emitting lasers (PCSELs) boast both high brightness and high functionality while maintaining the merits of semiconductor lasers, and thus PCSELs are solutions to the issues of existing laser technologies. In this Review, we discuss recent progress of PCSELs towards high-brightness and high-functionality operations. We then elaborate on new trends such as short-pulse and short-wavelength operations as well as the combination with machine learning and quantum technologies. Finally, we outline future research directions of PCSELs with regard to various applications, including not only LiDAR and laser processing, as described above, but also communications, mobile technologies, and even aerospace and laser fusion.
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