Physical Constraints on Consciousness: Demonstrating the Impossibility of Awareness in Silicon-Based Architectures

This work presents a physics-based analysis of the necessary conditions for awareness and demonstrates the limitations of current silicon-based computational systems. By examining coherence time, clock period, quality factor, and phase closure, the study establishes thresholds that biological systems satisfy but silicon architectures cannot. High-density EEG data from the OpenNeuro ds003505 (VEPCON) dataset were analyzed using phase coherence metrics and anomaly detection across sliding windows to illustrate sustained field-like coherence in biological brains. Simulations, energy dissipation calculations, and toy oscillator models are included to demonstrate why silicon fails to meet these fundamental constraints. All results are framed in a falsifiable and constraint-driven manner: the focus is on physically measurable limits that any system must satisfy to support awareness. No assumptions are made about subjective experience, and the study provides a self-contained methodology suitable for peer review. This dataset and accompanying analyses offer a rigorous, multi-domain perspective on the physical prerequisites for consciousness and provide a benchmark for evaluating claims of artificial awareness in contemporary AI systems.