Transistors

Researchers from Nanjing University of Aeronautics and Astronautics have demonstrated an atom‑thin sliding ferroelectric transistor that can reliably store 3,024 distinct, non‑volatile polarization states at room temperature - a record for ferroelectric neuromorphic hardware. The device is built from a well‑aligned monolayer graphene channel on hexagonal boron nitride (hBN), forming a moiré superlattice that enables fine, electrical control over ferroelectric polarization and charge localization within just a few atomic layers.

Performance comparison of Gr/hBN device and schematic diagram of its working mechanism. Credit: Nature Electronics (2026)

The transistor consists of an aligned graphene monolayer atop ferroelectric hBN, with source, drain and gate electrodes defined by standard nanofabrication, including electron‑beam evaporation for the metal contacts. Graphene serves as a high‑mobility, atomically thin channel whose Fermi level can be tuned electrostatically, while the underlying hBN provides sliding‑induced ferroelectricity and an atomically flat, low‑disorder interface. The lattice mismatch of about 1.8% between graphene and hBN generates a long‑wavelength moiré potential, which plays a central role in localizing injected carriers and stabilizing multiple polarization configurations.

As power consumption by machine learning technologies rises, demand grows for AI devices with low power consumption and high computational performance. "Physical reservoirs" are AI devices that perform efficient brain-inspired information processing called reservoir computing and are interesting thanks to their low computational load and low power consumption. However, their lower computational performance compared to software processing has thus far been a drawback.

A research team from NIMS, Tokyo University of Science, and Kobe University recently developed an ion-gel/graphene electric double layer (EDL) transistor-based ion-gating reservoir (IGR) that achieved high computational performance comparable to that of deep learning while reducing the computational load by orders of magnitude. By combining graphene, which has high electron mobility and ambipolar behavior, and an ion gel, various responses with different speeds (ions and electrons moving in various manners) develop through complex interactions, enabling the device to respond to input signals with time constants (rates of change) that vary over an extremely wide range.

Researchers from the University of Illinois Chicago, Wayne State, and Northwestern have shown that random defects in graphene transistors can be harnessed for next-generation hardware security. Their work demonstrates how the intrinsic disorder in graphene can generate unique electromagnetic “fingerprints,” signals so tied to each device’s atomic structure that they cannot be copied or predicted.

Traditional digital encryption relies on stored keys that can be stolen or cracked. By contrast, this graphene-based system uses a physical unclonable function (PUF), a hardware identity formed by the material’s natural randomness. When probed wirelessly, each graphene transistor produces a distinctive radio signal encoding its physical quirks - residues, strain, charge variations - into a one-of-a-kind signature.

Graphenea Semiconductor, a leading graphene foundry company and Melexis (Euronext Brussels: MELE), a global supplier of micro-electronic semiconductor solutions, have announced a strategic collaboration to accelerate the development and evaluation of Melexis’ integrated GFET-on-CMOS platform for advanced biosensing.

The initiative aims to address some of the key challenges in biosensor adoption: simplifying complex readout electronics and improving sensitivity while ensuring reliability and scalability. By combining Melexis’ expertise in semiconductor integration with Graphenea’s knowhow in graphene transistors, the collaboration represents a step from laboratory prototypes to real-world deployment. Potential applications include medical diagnostics, such as cancer biomarker detection, neurological and infectious diseases detection, and broader biosensing applications like environmental sensing of PFAS.

Adisyn has announced a significant advancement in its semiconductor research through its subsidiary, 2D Generation. The team has validated a critical early step in the process of integrating graphene interconnects into computer chips: preparing the silicon wafer surface to the exacting standards required for high-precision manufacturing. 

Achieving a perfectly clean and smooth wafer surface is a foundational step - any imperfection at this stage can compromise the entire chip production process, leading to lower yields and higher costs. By demonstrating reliable execution at this level, 2D Generation has shown it can master the fundamentals necessary for scaling up semiconductor fabrication.

A few days ago, the International Iberian Nanotechnology Laboratory (INL) and University of Minho (UMinho) signed a licensing agreement with IPLEXMED, a spin-out company specializing in advanced medical diagnostics. The agreement grants the company rights to commercialize a new graphene-based diagnostic platform. 

The technology, developed under the MULTIMAL project and patented in 2024 with the support of HORIZON 2020, utilizes monolayer graphene field-effect transistor (FET) sensors. These sensors are functionalized for the non-invasive, rapid, and highly sensitive detection of malaria and other diseases, delivering attomolar-level performance from saliva or urine samples. 

Archer Materials has reported that initial data from first-stage testing of its blood potassium graphene Biochip project have shown the technology suitable for silicon for point-of-care and at-home diagnostic applications. Archer expects the findings to de-risk its supply chain, reduce unit costs, and accelerate the pathway towards manufacture and commercialization.

The company now plans to finalize a working prototype and progress toward clinical trials in the new year as it moves towards project validation and commercial readiness.

Researchers at AMO, Graphenea Semiconductor and Emberion recently demonstrated a 200 mm processing platform for the large-scale production of graphene field-effect transistor-quantum dot (GFET-QD) hybrid photodetectors. Such sensors have many potential applications, ranging from surveillance, search and rescue, and vehicle safety to improved sorting of food and food packaging to reduce their environmental impact. 

Schematic of the device architecture of the tandem GFET‒QD photodetector. Image from: Scientific Reports

The team addressed several of the graphene wafer-scale integration challenges, including monolayer graphene chemical vapor deposition (CVD), transfer, and patterning, which fulfill the requirements for imaging functionalization and large area deposition of the multilayer QD absorber material in an inert atmosphere, as well as methods for encapsulating devices using thin film alumina (Al₂O₃) and hermetically sealed semiconductor packages. Such a demonstration elevates the initial concept to higher technology readiness levels in multiple dimensions, ranging from wafer-scale graphene device statistics to the development of custom CMOS circuits and the implementation of production-ready packaging.

LayerLogic, a Swedish deeptech startup spun out of Chalmers University of Technology, has secured €470,000 in pre-seed funding to accelerate development of its real-time food contamination detection platform. The round was led by Scientifica Venture Capital and follows LayerLogic’s selection in the Super Sapiens Europe initiative.

Founded in 2024, LayerLogic originated within Chalmers’ Department of Quantum Device Physics, Microtechnology, and Nanoscience. The team combines engineering and academic expertise in graphene-based technologies. At the core of the company’s innovation is a portable biosensor that uses graphene field-effect transistors (gFETs) to detect foodborne pathogens. The surface of the sensor is functionalized with selective receptor molecules, enabling rapid identification of bacteria, viruses, fungi, and contaminant compounds.

Tachmed and Paragraf have entered into a strategic partnership to bring scalable, cost-effective, and accurate diagnostic testing to homes and primary care clinics globally.

The partnership will focus on the integration of Paragraf’s proprietary graphene field-effect transistor (GFET) molecular sensor into Tachmed’s innovative digital health platform, TachShield. This cloud-based system combines rapid diagnostic tests, connected devices, software, and APIs through a single mobile app, offering a seamless experience for patients, health service providers, and clinicians.