The device was developed by researchers led by Xiaoxian Zhang and Yongsheng Wang at Beijing Jiaotong University, in collaboration with Yuchao Yang and Yaoyu Tao at Peking University. Their approach combines an ambipolar semiconductor, tungsten diselenide, or WSe₂, with a thin ferroelectric layer made from hafnium zirconium oxide, known as HfₓZr₁₋ₓO₂ or HZO. The resulting architecture is designed to function not merely as a light sensor, but as a programmable optical computing element capable of changing how it responds to incoming light.

At the heart of the device is a sub-20-nanometer HZO ferroelectric film integrated with a split-gate structure. Ferroelectric materials possess a switchable internal electric polarization. Once that polarization is changed, it can continue influencing the electronic behavior of a device even after the programming voltage is removed. In this photodiode, the polarization of the HZO layer modifies the electrical environment of the WSe₂ channel, allowing the researchers to control the polarity of the device and reconfigure its photocurrent response.

WSe₂ is particularly useful for this purpose because it is ambipolar. Depending on the surrounding electric field and gate conditions, it can conduct through either electrons or positively charged holes. The split-gate design takes advantage of this dual behavior to create a reconfigurable homojunction, a junction formed within the same semiconductor system rather than between conventional materials with different electronic properties. By switching the ferroelectric polarization, the researchers can alter the junction configuration and reverse the direction of the photocurrent.

One of the most notable features of the device is that it can switch its photocurrent polarity without requiring an external bias during operation. In conventional photodetectors, an applied voltage is often needed to drive current or tune the response, adding to energy consumption and complicating circuit integration. The reported architecture instead uses the stored polarization of the ferroelectric layer to establish the required electrostatic conditions internally. This enables light detection and signal modulation under zero external bias, an important step toward low-power sensing systems.

The programming process is also designed to be highly energy efficient. The researchers report a programming energy below one femtojoule, a scale that is extraordinarily small compared with the energy typically associated with moving data between a sensor and a processor. The device switches between states in approximately 50 microseconds and retains its programmed weight for more than 100 seconds. In this context, the “weight” represents the adjustable strength or sign of the device response, allowing it to act as an analog computational element rather than a simple on-or-off detector.

The team demonstrated that the photodiode could perform in-situ preprocessing of optical signals, including matrix-vector multiplication, a fundamental operation in artificial intelligence and neural-network algorithms. Matrix-vector multiplication normally requires large numbers of data transfers between memory and processing units. When implemented directly through the physical responses of devices, the operation can occur in parallel as light is detected, reducing the need for repeated digital conversion and memory access. The reconfigurable photocurrent of the WSe₂-HZO device provides a physical means of applying adjustable computational weights to optical inputs.

To test its practical potential, the researchers used the photodiode as a physical convolution kernel in simulated image edge-detection tasks. Convolution kernels apply mathematical weight patterns to neighboring pixels to identify features such as boundaries, contours, and fine structures. The device produced edge maps that were nearly indistinguishable from those generated by ideal software calculations. At its optimal programmed weight state, the system achieved a normalized mean squared error of approximately 3.2 × 10⁻⁴, indicating a close match between the hardware-generated and software-generated results.

The researchers say the work addresses several limitations that have slowed the development of neuromorphic vision hardware. Many existing devices provide only a unidirectional photocurrent, limiting their ability to represent positive and negative computational weights. Others depend on continuous external bias or require high programming voltages. Device concepts based on Schottky barriers or polymer ferroelectrics such as PVDF can also face challenges involving reliability, manufacturing compatibility, and scaling. By using HZO, a ferroelectric material more closely aligned with established semiconductor processing, the new design points toward a potentially CMOS-compatible route for integrating sensing, memory, and computation.

The result does not yet represent a complete commercial vision processor, but it provides a compact building block for future systems in which pixels detect, remember, and interpret optical information at the same physical location. Such architectures could be valuable in cameras that need to respond rapidly while operating on limited power, from edge-AI devices and smart sensors to wearable systems and autonomous machines. The study, published in Nano Research, illustrates how ferroelectric polarization and two-dimensional semiconductors can be combined to make photodetectors programmable, computational, and substantially more energy efficient.

Subject of Research: Reconfigurable ferroelectric photodiodes and in-sensor computing using HZO and ambipolar WSe₂.

Article Title: Achieving energy-efficient in-Sensor computing via ferroelectric reconfigurable homojunctions

News Publication Date: 12-May-2026

Web References: https://doi.org/10.26599/NR.2026.94908610; https://www.sciopen.com/journal/1998-0124

References: Nano Research, DOI: 10.26599/NR.2026.94908610

Image Credits: Nano Research, Tsinghua University Press

Keywords

In-sensor computing, neuromorphic vision, ferroelectric electronics, HfₓZr₁₋ₓO₂, HZO, WSe₂, ambipolar semiconductors, reconfigurable photodiodes, CMOS-compatible devices, edge detection, optical computing, low-power electronics

At the heart of this development lies the innovative design of metasurfaces, which are flat structures far thinner than conventional optical components yet able to modulate light’s phase, amplitude, and polarization at will. Traditionally, image processing tasks rely heavily on electronic computation, which introduces latency and energy inefficiencies. The newly devised double-phase metasurface operators bypass these limitations by harnessing the unique properties of light itself—effectively embedding mathematical operations within the metasurface’s nanoscale architecture. This approach allows for real-time, ultrafast image processing without converting optical signals back into electronic data.

The concept of a “double-phase” metasurface hinges on precise control over two phase profiles simultaneously. By engineering these carefully tailored phase patterns onto a single metasurface, the researchers can perform complex linear transformations on incident light fields, which equate to essential image processing functions such as spatial filtering, edge detection, and pattern recognition. This remarkable functionality is achieved within an exceptionally compact device footprint, making it highly suitable for integration in next-generation optical systems where size, weight, and power consumption are critical constraints.

One particularly compelling aspect of this new technology is the all-optical nature of the information processing. Conventional image processing methods typically involve converting photons into electrons, digitizing signals, and applying algorithms through electronic processors. In contrast, double-phase metasurface operators enable the entire process to remain in the optical domain, eliminating data conversion bottlenecks. This could dramatically accelerate processing speeds, reduce energy usage, and enable new modalities of dynamic, real-time image analysis that are currently unattainable through purely electronic means.

Applications of this technology are broad and impactful. In telecommunications, it could streamline the handling of optical signals, enhancing bandwidth efficiency and reducing latency in data centers or communication networks. In medicine, the ability to process images optically in ultra-compact formats could advance portable diagnostic devices or real-time tissue imaging during surgeries. Moreover, the versatility of these metasurfaces allows for dynamic reconfiguration, hinting at future smart optical components that adapt to different computational tasks on the fly without physical alterations.

The fabrication of these double-phase metasurface operators involves sophisticated nanomanufacturing techniques. Researchers pattern subwavelength dielectric structures on high-index materials, encoding intricate phase distributions with nanometric precision. The resulting metasurface manipulates the incoming light wavefront by introducing spatially varying phase shifts that correspond to the desired computational function. This precise engineering requires extensive computational modeling and optimization to ensure that the metasurface operates efficiently across the targeted wavelength range, minimizing losses and aberrations.

Critically, the research team demonstrated experimentally that these metasurfaces could realize essential image processing functions such as differentiation and integration, fundamental building blocks for edge detection and image smoothing, respectively. By cascading multiple metasurfaces or combining phase profiles, they could implement compound operations, opening avenues for highly sophisticated all-optical computing architectures. The experimental validation underscores the readiness of this technology for real-world applications, moving beyond theoretical proposals into prototyped functional devices.

Perhaps most exciting is the potential scalability and compatibility of double-phase metasurface operators with existing semiconductor manufacturing. Unlike bulky optical components or complex systems requiring precise alignment, these metasurfaces can be integrated onto chips or optical fibers, interfacing seamlessly with current photonic infrastructures. This synergy supports the vision of compact and robust optical processors embedded within everyday technology, from smartphones to machine vision systems, dramatically enhancing performance while slimming down hardware footprints.

The implications for artificial intelligence are also profound. Many AI applications rely on rapid image recognition and pattern analysis, traditionally constrained by electronic processing speeds and power consumption. Employing metasurface-based optical computation could empower AI systems with instantaneous, energy-efficient image preprocessing, accelerating neural network inference and enabling novel real-time sensory processing. This convergence of photonics and AI heralds a paradigm shift, where optical devices themselves contribute to intelligent information processing.

Another intriguing facet of the research lies in the tunability and reconfigurability potential of metasurfaces. Although the current implementation relies on static phase patterns, future iterations may incorporate materials responsive to external stimuli—such as electrical signals, temperature shifts, or light intensity—enabling dynamically programmable optical operators. Such devices would usher in versatile, adaptive processing platforms capable of switching functionalities without physical replacement, substantially broadening the utility and impact of metasurface-based optical computing.

Despite these promising advancements, several challenges remain before widespread adoption. Ensuring fabrication consistency at scale, managing losses introduced by nanoscale structures, and achieving broad spectral bandwidth remain active areas of investigation. Furthermore, integrating these metasurfaces into larger optical systems requires overcoming alignment tolerances and interfacing with other photonic components. Nonetheless, the rapid progress illustrated by this research roadmap suggests these hurdles will be addressed in near future, propelling metasurface optics into mainstream technological applications.

In summary, the emergence of double-phase metasurface operators marks a significant leap forward in the field of optical information processing. By embedding complex computational functionalities directly into ultra-thin nanoscale structures, these metasurfaces facilitate ultrafast, energy-efficient all-optical image processing, circumventing traditional electronic bottlenecks. As fabrication techniques mature and integration challenges are overcome, this technology is poised to unlock new horizons across communication, medical imaging, artificial intelligence, and beyond, transforming how we manipulate and harness light for computing tasks.

As the scientific community continues to push the boundaries of metasurface capabilities, this landmark research embodies the convergence of nanotechnology, photonics, and information science. It exemplifies how innovative material engineering and design can revolutionize established paradigms, inspiring future explorations into the untapped potential of light-based computing. Within a decade, the seamless marriage between metasurface optics and all-optical computing may well catalyze a technological renaissance, delivering unprecedented processing speed, miniaturization, and adaptability.

Reflecting on this profound innovation, one is reminded that the future of image processing may no longer rest purely in silicon and electrons, but increasingly in the ethereal manipulation of photons through engineered surfaces. The work of Yu, Singh, Pietila, and colleagues signals the dawn of this exciting transition, heralding a future where light itself becomes the medium, the messenger, and the processor of information at the speed of nature’s fastest messenger.

Article References:
Yu, L., Singh, H.J., Pietila, J. et al. Double-phase metasurface operators for all-optical image processing. Light Sci Appl 15, 119 (2026). https://doi.org/10.1038/s41377-025-02153-w