G-FET

Researchers from Princeton University, University of Michigan, California State University and Japan's National Institute for Materials Science have introduced Qumus, an embodied AI system that can autonomously create graphene and fabricate atomically thin graphene devices in a robotic mini-laboratory.

Qumus AI architecture and fully robotic minilab. a Defining characteristics of an AI experimentalist. b Key self-evolving modules of Qumus, including LLM-agents, memory and knowledge systems, and skills including instrumental workflows and materials/devices realization recipes. c Qumus architecture for efficient multi-agent collaboration and robust performance. d A compact, fully robotic minilab consisting of vacuum- and temperature-controlled stages for 2D material mechanical exfoliation, optical flake search, flake transfer and stacking, along with robotic arms, storage modules, cameras, and microscope systems. Image from : arXiv

Qumus is built around the complete graphene workflow: from exfoliating bulk crystals to isolating single-layer flakes and stacking them into functional van der Waals (vdW) devices, all without human intervention. The system combines generative AI, computer vision and robotics to handle the labor-intensive steps that typically limit graphene research, such as flake discovery, thickness assessment and submicron alignment during transfer.

Paragraf has announced the launch of its latest Graphene Field Effect Transistor (GFET), the PMF2000, marking a step forward in scalable graphene device manufacturing.

The PMF2000 builds on Paragraf’s existing GFET technology, retaining its hallmark contamination-free graphene while introducing production on six-inch silicon wafers. This transition is enabled by the company’s new large-wafer manufacturing facility in Huntingdon - described as the world’s first graphene foundry. The move to larger wafers improves device yield and consistency, setting a new benchmark for quality and enabling reliable high-volume production.

Researchers at Penn State have developed a graphene-based field-effect transistor (GFET) architecture that improves sensor stability and sensitivity in liquid environments, marking a step toward real-time molecular detection for health, environmental, and industrial applications.

The team engineered a dual‑gate GFET that integrates a high‑κ hafnium dioxide (HfO₂) local back gate with an electrolyte top gate, coupled through a real‑time feedback control loop. This novel configuration enables capacitive signal amplification while suppressing gate leakage and low‑frequency noise - two sources of instability that have long limited the performance of conventional single‑gate GFETs used in liquid sensing.

Paragraf, the UK-based company commercializing graphene-based electronics using standard semiconductor processes, has announced an expansion of its product offerings with the introduction of a GFET Discovery Kit.

The Discovery Kit, which provides molecular sensing researchers the ability to process samples immediately, is now available. The kit provides an easy-to-assemble platform for molecular sensing experiments. By integrating Paragraf’s GFET-PV01 devices with a PalmSens EmStat Pico MUX16 data acquisition system and pre-configured accessories, the Discovery Kit removes the complexity of hardware selection, wiring and setup that typically slows down laboratory exploration.

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.

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.