Alberto Rodriguez Walter Henry Gale (1929) Career Development Assistant Professor of Mechanical Engineering
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Principal Investigator Kevin Esvelt
This lecture will detail the creation of ultrasensitive sensors based on electronically active conjugated polymers (CPs) and carbon nanotubes (CNTs). Conceptually a single nano- or molecular-wire spanning between two electrodes would create an exceptional sensor if binding of a molecule of interest to it would block all electronic transport. Nanowire networks of CNTs modified chemically or in composites with polymers provide for a practical approximation to the single nanowire scheme. Creating chemiresistive and FET based sensors that have selectivity and accuracy requires the development of new methods. I will discuss covalent and non-covalent medication of CNTs with groups that impart selectivity for target analytes. This can involve reactions at the CNT sidewalls and rapping of the CNTs with CPs. Highly specific chemical processes orthogonal responses can be produced for mixtures of analytes through careful integration of chemical functionality. A prevailing problem in all chemiresistive schemes, which is seldom highlighted by researchers, is drift. This is intrinsic for systems that need to interface with their surroundings and changes in the position of ions of small changes in the organization of the CNTs relative to each other, the electrodes, or their surroundings can change the base resistance. I will detail different methods designed to lock the CNT networks in place. These novel compositions are also designed to accommodate functionality and I will demonstrate how we can use a diversity of transition metals to create selective responses to gases. We will also show that this scheme creates CNT networks that are robust enough for solution sensing and demonstrate chemiresistive based glucose sensing. I will also briefly discuss the successful use of CNT based gas sensors for the detection of ethylene and other gases relevant to agricultural and food production/storage/transportation and integrated systems that increase production, manage inventories, and minimize losses.
The 2026 MIT Korea Conference brings together global industry leaders, researchers, and entrepreneurs to explore breakthrough technologies shaping tomorrow’s industries. MIT Professors Mark Bathe, Juejun Hu, Steven Spear, and Kevin Chen will share insights in programmable biology, robotics, organizational innovation, and silicon photonics. Ten MIT-connected startups will showcase innovations in AI, biotechnology, advanced manufacturing, and sustainable chemistry. Designed for executives, senior managers, and thought leaders, the conference offers a rare opportunity to engage directly with MIT’s innovation ecosystem and uncover the ideas and collaborations driving the next decade of global innovation.
There is great interest in “digital twins” to improve many aspects of semiconductor manufacturing, from increased device yield and performance, reduced consumption of energy and materials, increased flexibility, and to enable rapid uptake and scaling of new material, equipment, and process innovations. The digital twin has both physical and virtual components, with bilateral communication and control; the hope is to enable a wide range of models (of equipment, processes, wafers) at different fidelities (physical to simplified empirical, and machine-learning enabled), to support a wide range of “smart” functionalities. The road to digital twins goes through and builds upon many well-trodden paths. Here, several lines of research at MTL since the late 1980’s are highlighted, beginning with elements of the MIT Computer Aided Fabrication Environment including process flow languages, to DOE/Opt methods for automated surrogate model construction, and run by run control to track and compensate for equipment state and wear in CMP and other unit processes. The development of “statistical metrology” methods encompassed characterization and modeling of semiconductor variation, with layout pattern dependent models to identify “hot spots” in planarization, dishing, and erosion for a given design, as well as to guide dummy fill generation. An evolution from statistical to ML/AI approaches, particularly Bayesian methods, enabled design for manufacturability (DFM) for rapid MOSFET characterization, and then rapid fabrication process tuning, as well as AI-enabled anomaly detection. These and other paths bring us to an exciting next stage of the journey: by harnessing advances in sensing and data collection, AI methods, and computational power not possible at the beginning, the community is poised to create and deploy digital twins for semiconductor manufacturing.
Principal Investigator Marc Baldo