Lawrence Berkeley National Lab Unveils Robotic System to Accelerate Qubit Development

Researchers at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) are dramatically accelerating the development of stable, reliable qubits with a new robotic system unveiled at the Molecular Foundry. The quantum information science (QIS) cluster tool combines fabrication and analysis tools in a single, automated environment, allowing scientists to experiment with diverse materials and methods for building quantum components. This closed vacuum system minimizes contamination and enables the rapid creation of complex materials—a process previously slow and prone to error. “It’s like a robot pizza chef sitting in the middle with a spatula,” said Aeron Tynes Hammack, a Berkeley Lab scientist, explaining how the robotic arm precisely manages an 8-inch wafer. The resulting data will also fuel AI models, promising to revolutionize qubit design and unlock the potential of quantum computing. QIS Cluster Tool Enables Automated Qubit Material Exploration A single atomic imperfection can render a qubit useless, a challenge Lawrence Berkeley National Laboratory is addressing with a novel robotic system designed to accelerate materials discovery for quantum computing. The newly installed Quantum Information Science (QIS) cluster tool at the Molecular Foundry isn’t simply automating existing processes; it’s enabling entirely new avenues of qubit material exploration, promising to drastically reduce the time required to build stable, reliable quantum devices. Supporting approximately one thousand researchers annually, the Foundry provides a unique environment for this ambitious undertaking. The core innovation lies in the tool’s ability to combine fabrication and analysis within a single, sealed vacuum environment. This eliminates contamination – a major hurdle in quantum component creation – and allows for the layered deposition of diverse materials with unprecedented precision. Experimenters can select from materials like aluminum, niobium, titanium, and their compounds, employing techniques ranging from atomic layer deposition to ion beam etching, all without exposing the delicate structures to external influences. The tool’s precision extends to creating features just a few atoms wide, a level of control previously unattainable. Specialized software orchestrates the workflow, enabling simultaneous processing of multiple samples, significantly boosting throughput. At the heart of the QIS cluster tool is a robotic arm managing an 8-inch wafer, shuttling it between stations dedicated to deposition and quality control. “The exciting thing is that it automates processes in a fully clean environment to make complex materials. You can do it very reliably, very reproducibly, and fine-tune the recipes.” This automation isn’t just about speed; it’s about generating the vast datasets needed to train artificial intelligence models. By linking fabrication parameters to qubit performance, researchers aim to leverage AI to identify optimal materials and designs. The initial focus is on refining the Josephson junction – a critical component in most quantum computers – but the tool’s versatility extends beyond this. It can also fabricate precision parts for microelectronics and other quantum computer elements like resonators and capacitors. Furthermore, the underlying technology has potential applications beyond computation, creating highly sensitive sensors for fields like dark matter detection and molecular analysis. “When you’re building quantum logic gates or other computational interfaces, you’re also getting really, really good sensors for free,” Hammack explained. Jim Ciston, deputy director of the Molecular Foundry, emphasized the importance of this capability, stating, “There are so many different variables…that you could possibly try. We need a tool that will autonomously explore and refine recipes for making these interfaces that lead to high-reliability, long-lived qubits.” Josephson Junctions: Core Component for Superconducting Qubit Fabrication Current efforts to build practical quantum computers heavily rely on the fabrication of stable and controllable qubits, with superconducting qubits leading the charge. These qubits often utilize the Josephson junction – a critical component demanding increasingly precise manufacturing techniques. Researchers at Lawrence Berkeley National Laboratory’s Molecular Foundry are addressing this challenge with a new quantum information science (QIS) cluster tool, designed to accelerate the discovery of optimal qubit materials and designs. This automated system allows for the rapid experimentation with diverse materials and fabrication methods, a capability previously hindered by the sensitivity of quantum components to environmental factors and the time-consuming nature of manual processes. This prevents contamination, a major impediment to reliable quantum component production, and enables the layering of materials with unprecedented control.

The Molecular Foundry has initially focused on refining traditional aluminum Josephson junctions, but is also exploring novel materials like hafnium, demonstrating the creation of high-quality junctions and their potential for supersensitive particle detectors. “We’re exploring the different types of materials that we can deposit and how the processes influence their grain structure, composition, superconducting temperature transitions, and tolerance to magnetic fields…the ‘boring’ material science stuff,” Hammack explained. This focus on fundamental materials science is a key differentiator, allowing the Molecular Foundry to explore options often overlooked in industrial settings. “I’m coming back to the Foundry from industry, and one of the challenges in industry is you wind up locked to the processes in your past that have been successful,” Hammack said. The ultimate goal is a self-optimizing system, capable of predicting qubit quality based on fabrication recipes, and ultimately, accelerating the path toward robust and scalable quantum computation. We’re exploring the different types of materials that we can deposit and how the processes influence their grain structure, composition, superconducting temperature transitions, and tolerance to magnetic fields…the ‘boring’ material science stuff. Multi-Material Fabrication & Analysis within a Vacuum Environment Beyond simply building qubits, the focus is on understanding the intricate relationship between material composition and quantum performance, a challenge previously hampered by the limitations of conventional fabrication methods. The new QIS cluster tool addresses this by enabling the deposition of diverse materials, layer upon layer, within a pristine vacuum environment, eliminating contamination that can derail delicate quantum states. This automated system isn’t merely about speed; it’s about control and data acquisition. The tool’s robotic arm meticulously moves an 8-inch wafer between stations dedicated to deposition, quality control, and analysis, allowing for the creation of complex materials with unprecedented reproducibility. Researchers are experimenting with materials beyond the traditional aluminum, including niobium, titanium, and compounds incorporating oxygen or nitrogen, utilizing techniques like sputtering, evaporation, and ion beam etching to achieve atomic-scale precision. The QIS cluster tool isn’t limited to simply making these junctions; it also analyzes them using electrons, x-rays, lasers, and infrared light to identify material composition and any potential impurities. This comprehensive analysis allows researchers to halt unsuccessful runs early, conserving valuable time and resources. Recent studies have demonstrated the tool’s capability in fabricating high-quality Josephson junctions from hafnium, opening possibilities for supersensitive qubit-based particle detectors. AI-Driven Data Analysis Accelerates Quantum Device Improvement At the heart of this advancement is the tool’s ability to meticulously record data during the creation of quantum components, linking fabrication parameters directly to qubit performance. This data isn’t simply archived; it’s actively used to train artificial intelligence models, enabling them to identify the characteristics of successful qubits and predict optimal designs. The system’s precision extends to creating features at the atomic level, crucial for the delicate structures within qubits. The QIS cluster tool particularly excels at fabricating Josephson junctions – tiny superconducting sandwiches essential to most quantum computers. These junctions rely on the quantum phenomenon of electron tunneling, and even minute imperfections can drastically impact their performance. Hammack envisions a system that can predict qubit quality based on a given recipe, streamlining the development process. This approach isn’t solely focused on improving existing qubit designs; it also opens doors to exploring unconventional materials and fabrication techniques. “But, you know, modern life is made out of really basic material science stuff, and the really basic material science stuff is what we have full carte blanche to explore.” The resulting insights will be publicly shared, fostering innovation across the quantum computing landscape. Slight imperfections on the atomic level can destroy the delicate coordinated dance of electrons that give rise to special quantum properties.Jim Ciston, deputy director of the Molecular Foundry Source: https://newscenter.lbl.gov/2026/02/11/a-robot-pizza-chef-serving-up-better-quantum-computers/ Tags: