In traditional computing architectures, particularly the von Neumann model, memory units and processing cores are separate. This separation necessitates constant data movement between the two, leading to significant latency and energy inefficiency. As computing scales up, the limitations of this model become increasingly pronounced. In contrast, neuromorphic photonics enables computation through the use of light, which inherently allows for ultra-high bandwidth and low latency operations. By leveraging the unique properties of light, researchers propose a more efficient way to execute critical computations, such as matrix-vector multiplication, significantly reducing power consumption and increasing operational speeds.

Central to this evolution in computational technology is the concept of integrated photonic neural networks (IPNNs). These networks utilize a trio of foundational building blocks: photonic synapses, photonic neurons, and photonic memristors. Photonic synapses play a crucial role in weight storage and loading, essential for neural network operations. Various devices can be used as photonic synapses, including microring resonators (MRRs), Mach-Zehnder interferometers (MZIs), and phase-change materials (PCMs). Each of these devices serves a specific purpose, maximizing energy efficiency while ensuring compactness and effectiveness in weight storage.

Photonic neurons are equally vital, serving as the nonlinear activation units within the network. By favoring all-optical activation schemes, the researchers highlight the potential for increased efficiency in neural network applications. Furthermore, photonic memristors provide crucial memory capabilities, allowing for both volatile and non-volatile storage. This ability to store and process data at optical speeds presents an advantage over traditional electronic counterparts, thereby facilitating real-time data manipulation.

The review also delves into various architectures that have been developed for integrated photonic neural networks. Among them are coherent networks, which may utilize structures such as MZI meshes to enable rapid on-chip training. Additionally, researchers discuss parallelized IPNNs that employ multiplexing techniques for a significant increase in data throughput. Integrated diffractive networks are identified as a promising architecture for low-latency inference tasks, while reservoir computing emerges as a versatile approach for processing dynamic signals.

Despite the promising advancements in IPNN technology, the researchers emphasize that several hurdles must be addressed for the successful deployment of these systems. Calibration and stability remain critical challenges that need to be navigated. The seamless integration of photonic and electronic components is paramount, particularly in efforts to develop programmable, general-purpose architectures capable of efficient training. Overcoming these obstacles is essential for translating research breakthroughs into practical applications that could revolutionize fields such as edge computing, autonomous driving, and intelligent manufacturing.

The outlook for IPNNs remains positively charged, with the researchers proposing that future developments in optoelectronic integration and programmable platforms will significantly improve robustness and performance. As the team continues to investigate and refine materials like phase-change compounds, microcombs, and advanced multiplexing techniques, the prospect of widespread adoption of photonic neuromorphic computing seems more attainable than ever. This could lead to a paradigm shift in artificial intelligence, transforming how computations are performed and ushering in a new era of energy-efficient, high-speed computing.

To achieve a broader understanding, the authors provide a roadmap that lays out the necessary advancements needed to realize the full potential of photonic neural networks. Breakthroughs in achieving low-energy nonlinearities are highlighted as key objectives, as are initiatives aimed at enhancing storage capabilities, calibration stability for large arrays, and improving photonic-electronic co-packaging. This proactive approach indicates a clear trajectory toward developing photonic AI systems that are not only capable of handling significant computational loads but are also energy-efficient and scalable.

In sum, the future of computing may very well lie in the intersection of photonics and artificial intelligence, as evidenced by the promising developments in integrated photonic neural networks. Researchers anticipate that as foundational technologies and architectures continue to evolve, photonic neuromorphic computing will become a reality, paving the way for intelligent systems that can operate at unprecedented speeds while minimizing energy consumption. This signals a revolutionary step forward, where traditional limits imposed by electronic processing may soon be eclipsed by the versatility and efficiency provided by light-based computation.

Subject of Research: Integrated Photonic Neural Networks
Article Title: Integrated photonic synapses, neurons, memristors, and neural networks for photonic neuromorphic computing
News Publication Date: 2025
Web References: https://www.oejournal.org/oet/archive_list_en
References: Han SF, Shen WH, Gu M, et al. Integrated photonic synapses, neurons, memristors, and neural networks for photonic neuromorphic computing. Opto-Electron Technol 1, 250011 (2025). DOI: 10.29026/oet.2025.250011
Image Credits: OET

One of the remarkable features of indium selenides is their exceptional electron mobility, which can exceed 1,000 cm² V⁻¹ s⁻¹. This high mobility enables faster charge transport, crucial for the operation of ultra-scaled transistors needed in modern computing applications. As the demand for rapid processing speeds increases, enabling technologies that can deliver higher mobility will play a critical role in enhancing the performance of electronic devices. Furthermore, the high thermal velocity—greater than 2 × 10⁷ cm s⁻¹—ensures that indium selenides can handle high-speed operations without significant energy loss.

Another advantage of these materials is their thickness-tunable bandgaps, ranging from 0.97 eV to 2.5 eV. This tunability allows for the design of energy-efficient devices that can operate across a wide spectrum of applications, from low-power electronics to high-performance photodetectors. The ability to tune the bandgap also facilitates the creation of devices with optimal performance characteristics tailored to specific needs, potentially leading to advances in ultrafast photonics and optoelectronics.

In addition to their electronic properties, indium selenides possess unique phase-dependent ferroelectric properties, enabling them to function as both logic devices and non-volatile memory elements within a single material system. This dual capability is essential for next-generation computing architectures that require efficient data storage, retrieval, and processing without the auxiliary circuitry typically associated with traditional semiconductor materials. The ability to integrate these functions into a single chip could significantly reduce manufacturing complexities and enhance overall device performance.

Recent advancements in ballistic transport in InSe transistors have laid the groundwork for next-generation computing devices. Ballistic transport refers to the regime where carriers move through the material without being scattered by defects or phonons, resulting in a significant improvement in device performance. Researchers have been able to demonstrate such ballistic transport in InSe transistors, highlighting their potential to outperform silicon-based devices in terms of speed and energy efficiency.

The development of tunnel field-effect transistors (TFETs) based on indium selenides marks another breakthrough in low-power electronics. TFETs leverage the unique band structure of indium selenides to achieve steep subthreshold slopes, which can enable lower operating voltages and thereby reduce power consumption. This is especially beneficial in modern computing applications where power efficiency and thermal management are paramount for sustaining high performance over extended periods.

In addition to their electronic properties, indium selenides also show promise in ferroelectric device applications. The exploitation of the ferroelectric characteristics of In₂Se₃ paves the way for innovative non-volatile memory solutions that can function alongside traditional logic devices. These ferroelectric memory elements can store data by inducing polarization within the material, offering advantages such as low power consumption and faster read/write times compared to conventional memory technologies.

However, challenges in the fabrication and processing of indium selenides remain a significant obstacle to their widespread adoption. Addressing these challenges is critical to translate their theoretical advantages into commercially viable solutions. Researchers are actively investigating scalable synthesis methods that can produce high-quality samples of indium selenides, which are essential for developing reliable electronic components.

Phase control is another key challenge when working with indium selenides. The ability to manipulate the phase states of these materials—given their complex phase diagrams—is critical for optimizing device performance. This includes transitioning between different structural phases, which can dramatically affect their electronic and optical properties. Implementing techniques for stabilized phase control will be vital for fostering the consistent performance of devices based on these materials.

Oxidation is also a significant concern that can impact the stability and performance of indium selenide devices. The exposure of these materials to ambient conditions may lead to undesirable oxidation, resulting in degradation of their electro-optical properties. Innovative strategies for oxidation prevention and encapsulation will be necessary to enhance the lifespan and reliability of indium selenide-based devices, particularly in real-world applications where environmental exposure is unavoidable.

Ultimately, bridging fundamental materials science with practical device engineering offers a roadmap for utilizing the exceptional properties of indium selenides in developing commercial low-power computing technologies. By focusing research efforts on the synthesis methods, phase stability, and oxidation prevention, scientists can overcome existing barriers and unlock the potential of indium selenides as alternatives to silicon-based technology.

The vision of integrating indium selenides into next-generation computing architectures entails the development of innovative devices capable of meeting the demands of modern electronics. As researchers continue to explore the capabilities of these materials, they pave the way for advanced applications that could reshape computing paradigms. The journey toward realizing indium selenides as a cornerstone of future electronics promises not only enhanced performance but also the evolution of how computing devices are conceived and utilized.

The endeavor to harness indium selenides for electronic applications is underpinned by a commitment to sustainable and efficient technology. As the industry faces mounting pressure to reduce energy consumption, the transition to low-power materials like indium selenides could represent a pivotal shift in electronic design and manufacturing. By prioritizing their adoption, we can ensure that next-generation devices are not only high-performing but also environmentally conscious, setting new standards for tech innovation in the years to come.

In summary, van der Waals indium selenides hold enormous promise for the future of low-power computing, standing at the intersection of material science and electronic engineering. Their unmatched electronic properties, combined with their unique functionalities, herald a new era of device possibilities that could ultimately challenge and surpass the longstanding dominance of silicon in the microelectronics sector.

Subject of Research: Indium Selenides for Low-Power Electronics

Article Title: Indium selenides for next-generation low-power computing devices

Article References:

Song, S., Altvater, M., Lee, W. et al. Indium selenides for next-generation low-power computing devices.
Nat Rev Electr Eng (2026). https://doi.org/10.1038/s44287-025-00251-w

Image Credits: AI Generated

DOI: 10.1038/s44287-025-00251-w

Keywords: Indium Selenides, Low-Power Electronics, Semiconductor Technology, Ballistic Transport, Ferroelectric Devices