Electrically integrable, high-sensitivity, and high-reliability magnetic sensors are not yet realized at high temperatures (500 °C). In this study, an integrated on-chip single-crystal diamond (SCD) micro-electromechanical system (MEMS) magnetic transducer is demonstrated by coupling SCD with a large magnetostrictive FeGa film. The FeGa film is multifunctionalized to actuate the resonator, self-sense the external magnetic field, and electrically readout the resonance signal. The on-chip SCD MEMS transducer shows a high sensitivity of 3.2 Hz mT−1 from room temperature to 500 °C and a low noise level of 9.45 nT Hz−1/2 up to 300 °C. The minimum fluctuation of the resonance frequency is 1.9 × 10−6 at room temperature and 2.3 × 10−6 at 300 °C. An SCD MEMS resonator array with parallel electric readout is subsequently achieved, thus providing a basis for the development of magnetic image sensors. The present study facilitates the development of highly integrated on-chip MEMS resonator transducers with high performance and high thermal stability.

Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductors industry1-2. However, most studies in this field have been limited to the fabrication and characterization of isolated large (>1µm2) devices on unfunctional SiO2/Si substrates. Some studies integrated monolayer graphene on silicon microchips as large-area (>500µm2) interconnection3 and as channel of large transistors (~16.5µm2)4-5, but in all cases the integration density was low, no computation was demonstrated, and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here we present the fabrication of high-integration-density 2D/CMOS hybrid microchips for memristive applications — CMOS stands for complementary metal oxide semiconductor. We transfer a sheet of multilayer hexagonal boron nitride (h-BN) onto the back-end-of-line (BEOL) interconnections of silicon microchips containing CMOS transistors of the 180nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the h-BN memristors, which allows us to achieve endurances of ~5 million cycles in memristors as small as ~0.053µm2. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity (STDP) signals that are suitable for the implementation of spiking neural networks (SNN). The high performance and the relatively-high technology readiness level achieved represent a significant advance towards the integration of 2D materials in microelectronic products and memristive applications.

Deep learning algorithms have recently shown to be a successful tool in estimating parameters of statistical models for which simulation is easy, but likelihood computation is challenging. But the success of these approaches depends on simulating parameters that sufficiently reproduce the observed data, and, at present, there is a lack of efficient methods to produce these simulations. We develop new black-box procedures to estimate parameters of statistical models based only on weak parameter structure assumptions. For well-structured likelihoods with frequent occurrences, such as in time series, this is achieved by pre-training a deep neural network on an extensive simulated database that covers a wide range of data sizes. For other types of complex dependencies, an iterative algorithm guides simulations to the correct parameter region in multiple rounds. These approaches can successfully estimate and quantify the uncertainty of parameters from non-Gaussian models with complex spatial and temporal dependencies. The success of our methods is a first step towards a fully flexible automatic black-box estimation framework.