Thesis Title:
Atomically Engineered, Self-Healing 2D Semiconductor Platforms for Reversible Logic and Ultra-Low Energy Computation
1. Overview & Motivation
As silicon-based logic approaches its physical and thermodynamic limits, the next generation of computation requires radically new materials and device paradigms. This project proposes to explore a novel class of atomically thin, defect-tolerant semiconductors capable of reversible logic switching with minimal energy dissipation, potentially below the Landauer limit. In addition, these materials will be engineered to exhibit self-healing behaviour, a property that could drastically enhance reliability and lifetime in advanced computing systems.
2. Objectives
1. Identify and simulate promising 2D semiconductor materials (e.g., MoS₂, Janus structures, phosphorene) with:
a. Tunable bandgaps
b. Low defect formation energies
c. Reversible electronic or valleytronic behavior
2. Investigate atomic level defect dynamics and healing mechanisms under external stimuli (e.g., thermal, optical, electric field).
3. Design and prototype a reversible logic gate (e.g., Fredkin or Toffoli-type) using the selected material platform.
4. Characterize energy dissipation per logic operation, aiming for sub-kT·ln(2) switching energy.
5. Demonstrate partial or full recovery of logic function after induced damage, confirming self-healing behavior.
3. Methodology
1. Computational Screening: DFT based calculations and AI-guided materials discovery for candidate semiconductors with desired quantum and defect-tolerant properties.
2. Material Synthesis: Fabrication of atomically thin layers using CVD or exfoliation, with dopants or strain to enable dynamic lattice behavior.
3. Device Fabrication: Nanofabrication of test structures (logic elements, transistor analogs).
4. Characterization Tools:
a. In situ TEM and STM for defect tracking
b. Electrical transport + AFM for logic switching and defect recovery
c. Pump-probe spectroscopy for quantum coherence or valley dynamics
5. Theoretical Modeling: Thermodynamic modeling of entropy and information retention in logic operations.
4. Expected Contributions
1. A demonstrable step toward reversible computing using solid-state materials.
2. Experimental validation of self-healing in 2D semiconductors, potentially applicable to next-gen AI hardware, neuromorphic systems, or ultra-resilient space electronics.
3. A scalable framework for ultra-low-power logic that challenges classical limits.
5. Stretch Goals
1. Incorporation of photonic or valleytronic read/write mechanisms
2. Coupling with AI-driven feedback for dynamic material repair prediction
6. Relevance
This topic sits at the convergence of materials innovation, energy-efficient computing, and quantum-adjacent device physics. If successful, it may open new pathways toward computing architectures that are:
1. More sustainable
2. Fundamentally reversible
3. Resilient and adaptive at the atomic level
7. Literature Snapshot
The proposed work is inspired by and builds upon recent advances in the fields of 2D materials, reversible computing, and defect engineering. Below are several cornerstone papers and reviews that provide both theoretical and experimental backing for the key concepts:
Reversible Computing & Sub Landauer Switching
1. Frank, M. P. (2005). Introduction to Reversible Computing: Motivation, Progress, and Challenges. Proceedings of the International Workshop on Physics and Computation.
· Introduces the thermodynamic basis of reversible computing and frames it as the future of ultra-efficient logic.
2. Younis, S. et al. (2021). Adiabatic logic: A step toward energy-efficient computing beyond Moore’s Law. Nature Electronics, 4, 472–479.
· Describes recent physical implementations of low-energy logic switching using adiabatic designs.
2D Semiconductors for Logic Applications
1. Chhowalla, M. et al. (2016). The chemistry of two-dimensional layered transition metal dichalcogenide nanosheets. Nature Chemistry, 8, 191–201.
· A comprehensive overview of the properties, synthesis, and applications of 2D TMDs such as MoS₂, WS₂, etc.
2. Manzeli, S. et al. (2017). 2D transition metal dichalcogenides. Nature Reviews Materials, 2, 17033.
· Focuses on electrical properties, valleytronics potential, and device applications of 2D semiconductors.
Defect Dynamics & Self Healing in Nanomaterials
1. Zhang, X. et al. (2022). Defect-tolerant and self-healing semiconductors: Towards resilient optoelectronics. Nature Materials, 21, 1093–1101.
· Discusses how self-healing occurs in layered materials and how to harness it for durable devices.
2. Komsa, H. P. et al. (2012). Two-dimensional transition metal dichalcogenides under electron irradiation: Defect production and doping. Physical Review Letters, 109, 035503.
· Simulates and observes defect creation and migration in 2D TMDs — vital for understanding healing pathways.
Quantum Coherence & Valleytronics in 2D Materials
1. Mak, K. F., Shan, J. (2016). Photonics and optoelectronics of 2D semiconductor transition metal dichalcogenides. Nature Photonics, 10, 216–226.
· Reviews quantum optical properties of 2D TMDs, critical for understanding potential logic switching methods.
2. Vitale, S. A. et al. (2018). Valleytronics: Opportunities, challenges, and paths forward.
Small, 14(30), 1801483.
· Explores valley degree of freedom in 2D materials and its role in encoding and preserving information.
AI-Driven Materials Discovery
1. Jha, D. et al. (2019). ElemNet: Deep learning the chemistry of materials from only elemental composition. Scientific Reports, 8, 17593.
· Demonstrates how deep learning can rapidly screen materials with target properties — useful for narrowing candidates for this thesis.