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.

Ultraefficient thermophotovoltaic power conversion by band-edge spectral filtering.

Thermophotovoltaic power conversion utilizes thermal radiation from a local heat source to generate electricity in a photovoltaic cell. It was shown in recent years that the addition of a highly reflective rear mirror to a solar cell maximizes the extraction of luminescence. This, in turn, boosts the voltage, enabling the creation of record-breaking solar efficiency. Now we report that the rear mirror can be used to create thermophotovoltaic systems with unprecedented high thermophotovoltaic efficiency. This mirror reflects low-energy infrared photons back into the heat source, recovering their energy. Therefore, the rear mirror serves a dual function; boosting the voltage and reusing infrared thermal photons. This allows the possibility of a practical >50% efficient thermophotovoltaic system. Based on this reflective rear mirror concept, we report a thermophotovoltaic efficiency of 29.1 ± 0.4% at an emitter temperature of 1,207 °C.

Fundamental Efficiency Limit of Lead Iodide Perovskite Solar Cells.

Lead halide materials have seen a recent surge of interest from the photovoltaics community following the observation of surprisingly high photovoltaic performance, with optoelectronic properties similar to GaAs. This begs the question: What is the limit for the efficiency of these materials? It has been known that under 1-sun illumination the efficiency limit of crystalline silicon is ∼29%, despite the Shockley-Queisser (SQ) limit for its bandgap being ∼33%: the discrepancy is due to strong Auger recombination. In this article, we show that methyl ammonium lead iodide (MAPbI3) likewise has a larger than expected Auger coefficient. Auger nonradiative recombination decreases the theoretical external luminescence efficiency to ∼95% at open-circuit conditions. The Auger penalty is much reduced at the operating point where the carrier density is less, producing an oddly high fill factor of ∼90.4%. This compensates the Auger penalty and leads to a power conversion efficiency of 30.5%, close to ideal for the MAPbI3 bandgap.