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In this study, a conjugate radiation/conduction multimode heat transfer analysis of cryogenic focused ion beam (FIB) milling steps necessary for producing ex situ lift out specimens under cryogenic conditions (cryo-EXLO) is performed. Using finite volume for transient heat conduction and enclosure theory for radiation heat transfer, the analysis shows that as long as the specimen is attached or touching the FIB side wall trenches, the specimen will remain vitreous indefinitely, while actively cooled at liquid nitrogen (LN2) temperatures. To simulate the time needed to perform a transfer step to move the bulk sample containing the FIB-thinned specimen from the cryo-FIB to the cryo-EXLO cryostat, the LN2 temperature active cooling is turned off after steady-state conditions are reached and the specimen is monitored over time until the critical devitrification temperature is reached. Under these conditions, the sample will remain vitreous for >3 min, which is more than enough time needed to perform the cryo-transfer step from the FIB to the cryostat, which takes only ∼10 s. Cryo-transmission electron microscopy images of a manipulated cryo-EXLO yeast specimen prepared with cryo-FIB corroborates the heat transfer analysis.

Multifunctional metamaterials (MMM) bear promise as next‐generation material platforms supporting miniaturization and customization. Despite many proof‐of‐concept demonstrations and the proliferation of deep learning assisted design, grand challenges of inverse design for MMM, especially those involving heterogeneous fields possibly subject to either mutual meta‐atom coupling or long‐range interactions, remain largely under‐explored. To this end, a data‐driven design framework is presented, which streamlines the inverse design of MMMs involving heterogeneous fields. A core enabler is implicit Fourier neural operator (IFNO), which predicts heterogeneous fields distributed across a metamaterial array, thus in general at odds with homogenization assumptions. Additionally, a standard formulation of inverse problem covering a broad class of MMMs is presented, together with gradient‐based multitask concurrent optimization identifying a set of Pareto‐optimal architecture‐stimulus (A‐S) pairs. Fourier multiclass blending is proposed to synthesize inter‐class meta‐atoms anchored on a set of geometric motifs, while enjoying training‐free dimension reduction and built‐it reconstruction. Interlocking the three pillars, the framework is validated for light‐by‐light programmable nanoantenna, whose design involves vast space jointly spanned by quasi‐freeform supercells, maneuverable incident phase distributions, and conflicting figure‐of‐merits (FoM) involving on‐demand localization patterns. Accommodating all the challenges, the framework can propel future advancements of MMM.