Digital Twin Foundries and Predictive Nanomanufacturing at the Limit English Introduction and Research Abstract This monograph asks how a digital twin foundry can remain scientifically credible as equipment ages, materials change, sensors drift, and process regimes move. It rejects the idea that a twin is a true factory copy and instead treats every twin as a versioned, decision-conditioned belief with a calibration date, validity domain, competing models, residual sentinels, and reset conditions. Scientific position The candidate objective is J_twin = E[e_pred^2] + lambda_K KL(p||p_ref) + lambda_A Age_cal - V_option. Prediction error, distributional divergence, and calibration age create burden, while preserving credible alternative models has option value. Safety, authorization, evidence custody, irreversible damage, and terminal-function acceptance remain hard vetoes. Research architecture The PDF contains thirty large gold derivations, eighty-two chapters with five single-page research sections each, and two hundred detailed one-page Research Gates. Every derivation links to chapter, experiment, and Gate corridors. Every chapter opens with its governing formula. Fixed quotas for Phoenix Answer Embryos and Easter-Egg Equations are not used. Five research layers Asset state and decision contract: Define twin identity intended decisions observability consequences baselines hard vetoes and validity horizons. Multiscale physics and digital thread: Bind equipment chamber memory wafer genealogy materials sensors unit operations and terminal function. Calibration discrepancy and twin families: Separate parameter inference model inadequacy and measurement error while preserving competing explanations. Residual sentinels and predictive control: Detect drift select active calibration bound control abstain hand over and reset. Portability replay and successor governance: Transfer process intent count maintenance burden preserve failed twins and enable replacement or retirement. Minimum demonstrator Two competing twins fit the same historical deposition or etch data but encode different hidden-state or discrepancy mechanisms. A low-risk, preregistered perturbation is selected for information gain and executed on witness structures. Both twins predict an unseen batch and hidden maintenance transition. Residual sentinels must detect invalidity, calibrated uncertainty must cover errors, and control authority must demote or abstain before unsafe action. A third party then replays calibration, model fission, reset, and the final decision. Central deliverable The Fissionable Foundry Twin binds physical assets, state graphs, wafer and material genealogy, sensors, references, multiscale models, calibration data, discrepancies, competing branches, forecasts, residuals, drift sentinels, authority tiers, controls, handovers, terminal function, failures, resources, resets, and successor choices. Another team must be able to reproduce, refute, fork, reset, simplify, replace, or retire the twin.