Attosecond delays in X-ray molecular ionization.

The photoelectric effect is not truly instantaneous but exhibits attosecond delays that can reveal complex molecular dynamics1-7. Sub-femtosecond-duration light pulses provide the requisite tools to resolve the dynamics of photoionization8-12. Accordingly, the past decade has produced a large volume of work on photoionization delays following single-photon absorption of an extreme ultraviolet photon. However, the measurement of time-resolved core-level photoionization remained out of reach. The required X-ray photon energies needed for core-level photoionization were not available with attosecond tabletop sources. Here we report measurements of the X-ray photoemission delay of core-level electrons, with unexpectedly large delays, ranging up to 700 as in NO near the oxygen K-shell threshold. These measurements exploit attosecond soft X-ray pulses from a free-electron laser to scan across the entire region near the K-shell threshold. Furthermore, we find that the delay spectrum is richly modulated, suggesting several contributions, including transient trapping of the photoelectron owing to shape resonances, collisions with the Auger-Meitner electron that is emitted in the rapid non-radiative relaxation of the molecule and multi-electron scattering effects. The results demonstrate how X-ray attosecond experiments, supported by comprehensive theoretical modelling, can unravel the complex correlated dynamics of core-level photoionization.

In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory.

In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network's synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.

Effect of dimensionality on the optical absorption properties of CsPbI3 perovskite nanocrystals.

The bandgaps of CsPbI3 perovskite nanocrystals are measured by absorption spectroscopy at cryogenic temperatures. Anomalous bandgap shifts are observed in CsPbI3 nanocubes and nanoplatelets, which are modeled accurately by bandgap renormalization due to lattice vibrational modes. We find that decreasing dimensionality of the CsPbI3 lattice in nanoplatelets greatly reduces electron-phonon coupling, and dominant out-of-plane quantum confinement results in a homogeneously broadened absorption line shape down to cryogenic temperatures. An absorption tail forms at low-temperatures in CsPbI3 nanocubes, which we attribute to shallow defect states positioned near the valence band edge.