Top 10 ground challenges in microsystems and nanoengineering

Microsystems & Nanoengineering volume 12, Article number: 34 (2026) Cite this article Microsystems and Nanoengineering—a cutting-edge discipline integrating microfabrication, nanotechnology, and interdisciplinary system design—boasts pivotal strategic value. Over the past two decades, explorations into the research and development of microdevices and integrated systems with high precision, low power consumption, and multifunctional characteristics have experienced an explosive growth. Technological innovations in this field have provided core support for humanity to tackle numerous critical challenges, leaving a profound imprint on areas ranging from intelligent medical sensing and industrial precision detection to aerospace miniaturized equipment, as well as hardware innovations in next-generation information technology. Like all academic fields in a period of rapid development, microsystems and nanoengineering still face a multitude of unresolved bottleneck issues. Fortunately, the number of research teams and engineering forces dedicated to exploring its underlying principles, breaking through manufacturing bottlenecks, and expanding application boundaries is larger than ever before. To commemorate the 10th anniversary of the journal’s founding, Microsystems & Nanoengineering has launched a global survey targeting scholars and industry experts in this field. The initiative aims to identify the most critical and innovative core scientific issues and technical challenges that must be addressed to promote the large-scale application and cross-domain integration of microsystems and nanoengineering. A central challenge in micro- and nano-systems engineering lies in overcoming the critical bottlenecks of escalating design complexity and constrained manufacturing efficiency. The core research question focuses on how AI-augmented methodologies can fundamentally transform the traditional, often sequential and experience-dependent, design-fabrication-operation cycle into an integrated, data-driven, and adaptive process. As device complexity increases—driven by demands for multi-functionality and miniaturization—traditional trial-and-error and physics-only simulations become prohibitively time-consuming and costly. They struggle to navigate vast design spaces and accurately predict real-world manufacturing outcomes, leading to suboptimal performance, low yield, and extended development cycles. This urgency is amplified by emerging applications in personalized healthcare, IoT, and advanced communications, which require highly customized and reliable microsys