We revisit the quantitative analysis of the ultrafast magnetoacoustic experiment in a freestanding nickel thin film by Kim and Bigot [J.-W. Kim and J.-Y. Bigot, Phys. Rev. B 95, 144422 (2017)] by applying our recently proposed approach of magnetic and acoustic eigenmode decomposition. We show that the application of our modeling to the analysis of time-resolved reflectivity measurements allows for the determination of amplitudes and lifetimes of standing perpendicular acoustic phonon resonances with unprecedented accuracy. The acoustic damping is found to scale as $\propto {\omega }^2$ for frequencies up to 80 GHz, and the peak amplitudes reach ${10}^{-3}$. The experimentally measured magnetization dynamics for different orientations of an external magnetic field agrees well with numerical solutions of magnetoelastically driven magnon harmonic oscillators. Symmetry-based selection rules for magnon-phonon interactions predicted by our modeling approach allow for the unambiguous discrimination between spatially uniform and nonuniform modes, as confirmed by comparing the resonantly enhanced magnetoelastic dynamics simultaneously measured on opposite sides of the film. Moreover, the separation of timescales for (early) rising and (late) decreasing precession amplitudes provide access to magnetic (Gilbert) and acoustic damping parameters in a single measurement.

Using valid experimental parameters, we quantify the magnitude of resonantly phonon-driven precession of exchange magnons in freestanding ferromagnetic nickel thin films on their thickness L. Analytical solutions of acoustically driven equations for magnon oscillators display a nonmonotonous dependence of the peak magnetization precession on the film thickness. It is explained by different L-dependence of multiple prefactors entering in the expression for the total magnetization dynamics. Depending on the ratio of acoustic and magnetic (Gilbert) damping constants, the magnetization precession is shown to be amplified by a Q-factor of either the phonon or the magnon resonance. The increase in the phonon mode amplitude for thinner membranes is also found to be significant. Focusing on the magnetization dynamics excited by the two first acoustic eigenmodes with $p=1$ and $p=2$, we predict the optimum thicknesses of nickel membranes to achieve large amplitude magnetization precession at multi 100 GHz frequencies at reasonably low values of an external magnetic field. By extending the study to the case of Ni-Si bilayers, we show that these resonances are achievable at even higher frequencies, approaching the THz range.

Using valid experimental parameters we quantify the magnitude of resonantly phonon-driven precession of exchange magnons in freestanding ferromagnetic nickel thin films on their thickness L. Analytical solutions of acoustically driven equations for magnon oscillators display a nonmonotonous dependence of the peak magnetization precession on the film thickness. It is explained by different L-dependence of multiple prefactors entering in the expression for the total magnetization dynamics. Depending on the ratio of acoustic and magnetic (Gilbert) damping constants, the magnetization precession is shown to be amplified by a Q-factor of either the phonon or the magnon resonance. The increase of the phonon mode amplitude for thinner membranes is also found to be significant. Focusing on the magnetization dynamics excited by the two first acoustic eigenmodes with p=1 and p=2 we predict the optimum thicknesses of nickel membranes to achieve large amplitude magnetization precession at multi 100 GHz frequencies at reasonably low values of an external magnetic field.

Processing‐in‐memory architectures mitigate data shuttling bottlenecks in deterministic AI workloads, but still depend on costly external entropy sources for probabilistic models. Here, we present a magnetic‐tunnel‐junction‐based processing‐in‐memory architecture that unifies data storage and massively parallel probabilistic computation for stochastic and deterministic neural networks. By introducing bit‐paired stochastic and stable magnetic tunnel junctions, we realize a multi‐precision stochastic memory cell that transduces stored binary weights into variance‐tunable stochastic bitstreams in situ. We evaluate the architecture's versatility across three progressive regimes of stochastic neural computation. First, by using a hardware‐in‐the‐loop prototype interfacing fabricated stochastic spin‐orbit torque magnetic tunnel junctions with a field‐programmable gate array chip, we validate stochastic matrix‐vector multiplications and achieve near‐floating‐point accuracy without specialized retraining. Second, for networks with stochastic neurons, we demonstrate an Ising machine that utilizes adaptive‐precision sampling and dynamic graph routing to reduce time‐to‐solution. Third, we propose an in‐memory Bayesian computer with independent control of weight means and uncertainties, and apply it to a localization problem under unreliable inputs. By keeping intermediate signals in the stochastic domain, the architecture reduces peripheral overhead associated with random‐number generation and data conversion. This device‐circuit co‐design provides a shared hardware platform for stochastic neural inference, combinatorial optimization, and uncertainty‐aware computing.