Autonomous Sequential Thermal Management for Edge AI Accelerators: A Multi-Mechanism Stack with Passive Activation Hierarchy and Falsifiable Validation Criteria

Edge AI accelerators and mobile SoC dies in the 40-60W power envelope face a critical thermal gap. Conventional thermal interface material and substrate stacks produce junction-to-ambient thermal resistance exceeding 1. 20 K/W, yielding junction temperatures of 145°C at 100W — far above silicon reliability limits. Direct liquid cooling, the datacenter solution, is excluded from this form factor by cost, size, and reliability constraints. This paper proposes an eight-mechanism integrated thermal management stack with autonomous sequential activation, explicitly scoped to 40-60W edge AI applications where die cost (200-500) makes a 15-20 thermal solution economically viable. The stack operates across five states: passive baseline conduction, automatic VO2 phase-change conductance switching, active vapor injection, electrocaloric emergency cooling, and self-powered TEG thermal telemetry — each activating in sequence as junction temperature rises, preventing mechanism competition. Three mechanisms are claimed as novel: (1) vanadium dioxide automatic conductance switching requiring no sensors, no software, and no power input — the material phase transition performs the control function; (2) a phononic crystal Kapitza resistance reduction layer at the liquid metal-silicon interface targeting coherent acoustic phonon backscattering suppression; and (3) an autonomous five-tier sequential activation hierarchy encoded in material phase transition temperatures rather than software control algorithms, with sub-millisecond thermal response versus tens-to-hundreds of milliseconds for software thermal management. Finite difference thermal simulation across 16 configurations (8 materials, 2 architectures, 80x80 grid, 10, 000 iterations) demonstrates honeycomb BNNT architecture achieves 56. 20°C peak temperature reduction versus square silicon baseline, with combined peak of 31. 09°C when vapor cooling is added — a 91% reduction in thermal load above ambient. A novel architecture-material interaction was discovered: mica performs 39. 57°C worse than baseline in square grid architecture and 31. 85°C better than baseline in honeycomb architecture. The same material property — extreme thermal anisotropy — causes catastrophic thermal bottlenecking in four-direction square diffusion and enables superior lateral spreading in six-direction honeycomb diffusion. This interaction was not predicted by prior analysis and has not been reported in the literature. Seven falsifiable hypotheses define the research program with specific experimental tests, pass/fail criteria, and honest timelines from 6 to 36 months. A reliability qualification matrix maps each mechanism to JEDEC, MIL-STD, IPC, and ASTM test standards with known failure modes. A techno-economic analysis confirms viability at 16. 50 per die for the target market. Every mechanism is accompanied by a null hypothesis and a removal criterion — what survives peer review is not optimism but evidence. The Presignal subtraction methodology frames the analysis: establish a thermal baseline, introduce each mechanism independently through its activation threshold, measure the residual heat load reduction, combine sequentially. The residual is the signal