AI Driven Autonomous Synthesis of Sustainable Semiconductors for 3D-HI for High Performance Computing
Project Description
Over the past 50 years, on-chip power densities have significantly increased due to transistor miniaturization and increased integration, following the Moore’s Law. However, continuing growth of compact 3D heterogeneously integrated (3D-HI) microsystems is limited by inadequate thermal management, leading to hotspots that reduce the performance and lifespan of these devices. State-of-the-art cooling technologies such as forced air cooling, immersion cooling or on-chip jet impingement cooling, have a significant footprint that constrains the size, weight, and power (SWaP) of microsystems in high performance computing, including in artificial intelligence and machine learning applications. On-chip, solid-state cooling methods that can be topologically fabricated on the hottest dies (e.g. logic dies in a multi-tier stack) can dynamically manage hotspots and enhance system performance. Solid-state cooling can be achieved by Peltier-effect based thermoelectric coolers or thermo-photonic coolers. Conventional semiconductors such as GaAs exhibit high quantum yield, however, suffer from weak electron-phonon interactions. The unique electron-phonon interactions and high radiative recombination rates in halide perovskites show promise of slow relaxation of hot carriers as well as anti-Stokes photoluminescence, resulting in excellent thermo-photonic cooling potential compared to conventional inorganic semiconductors. This project will use AI driven autonomous synthesis for high-throughput screening of perovskite semiconductors via dimensionality and compositional engineering, with a goal to discover semiconductors with ~100% quantum yield and optimal electron-phonon coupling.
Start Date
July 2026
Postdoc Qualifications
• Ph.D. in Materials Science, Chemistry, Chemical Engineering, Physics, Electrical Engineering, or a closely related field. The degree must be completed by the appointment start date.
• Demonstrated hands-on experimental experience in the areas of perovskite fabrication, Autonomous experimentation, and Opto-electronic characterization including PL, Raman, LT-PL, JV, EQE, EL, etc.
• Analytical Skills including XRD, SEM, UV-Vis, etc.
• Ability to work independently, design experiments, analyze complex data, troubleshoot technical challenges, and rigorously document research findings.
• Excellent written and verbal communication skills, as demonstrated by a strong publication record and the ability to present research clearly at conferences.
• Ability to mentor graduate and undergraduate students.
Co-advisors
Shubhra Bansal, Associate Professor, School of Mechanical Engineering, School of Materials Engineering, Purdue University, West Lafayette IN
Arun Mannodi Kanakkithodi, Assistant Professor, School of Materials Engineering, Purdue University, West Lafayette IN
Bibliography
1. Z. Li et al., “Comprehensive review and future prospects on chip-scale thermal management: Core of data center’s thermal management,” Appl Therm Eng, vol. 251, p. 123612, Aug. 2024, doi: 10.1016/J.APPLTHERMALENG.2024.123612.
2. W. Y. Chen, X. L. Shi, J. Zou, and Z. G. Chen, “Thermoelectric coolers for on-chip thermal management: Materials, design, and optimization,” Materials Science and Engineering: R: Reports, vol. 151, p. 100700, Oct. 2022, doi: 10.1016/J.MSER.2022.100700.
3. C. Li et al., “The on-chip thermoelectric cooler: advances, applications and challenges,” Jun. 01, 2024, Elsevier B.V. doi: 10.1016/j.chip.2024.100096.
4. Y. Park and S. Fan, “Multijunction Electroluminescent Cooling,” PRX Energy, vol. 3, no. 3, Jul. 2024, doi: 10.1103/prxenergy.3.033002.
5. Yamada, Y., Kanemitsu, Y. Electron-phonon interactions in halide perovskites. NPG Asia Mater 14, 48 (2022). https://doi.org/10.1038/s41427-022-00394-4.