Nanoelectromechanical systems (NEMS) as integrated components for ultrasensitive sensing, time keeping, or radio frequency applications have driven the search for scalable nanomechanical transduction on-chip. Here, we present a hybrid silicon-on-insulator platform for building NEM oscillators in which fin field effect transistors (FinFETs) are integrated into nanomechanical silicon resonators. We demonstrate transistor amplification and signal mixing, coupled with mechanical motion at very high frequencies (25-80 MHz). By operating the transistor in the subthreshold region, the power consumption of resonators can be reduced to record-low nW levels, opening the way for the parallel operation of hundreds of thousands of NEM oscillators. The electromechanical charge modulation due to the field effect in a resonant transistor body constitutes a scalable nanomechanical motion detection all-on-chip and at room temperature. The new class of tunable NEMS represents a major step toward their integration in resonator arrays for applications in sensing and signal processing.

In this paper we analyze and discuss the characteristics and expected benefits of some emerging device categories for ultra low power integrated circuits. First, we focus on two categories of sub-thermal subthreshold swing switches Tunnel FETs and Negative Capacitance (NC) FETs and evaluate their potential advantages for digital and analog design, compared to CMOS. Second, we investigate the combined low power and novel integrated functionality in some hybrid Nano-Electro-Mechanical (NEM) devices: the Resonant Body (RB) Fin FET for nW time reference ICs and dense arrays of Suspended Body (SB) Double Gate (DG) Carbon Nanotube (CNT) FET for low power analog/RF and integrated sensor arrays.

The reduction of wafer thickness requires an improved quality control of the wafer strength, which is significantly influenced by cracks. We introduce a machine learning framework to establish photoluminescence (PL) imaging as an optical inspection technique for the detection of cracks in multi-crystalline silicon wafers. The specially derived algorithm enables reliable crack detection in spite of similar background structures in the PL image from grain boundaries and dislocations. Within an experiment on thin wafers with artificially induced cracks we evaluate our approach by comparing the PL detection results to the findings of an infrared-transmission system and fractographical reference data. Based on the optical detection result, we derive a description of the crack structure. Since wafer strength may change after etching and thermal processes, wafer strength is analyzed during cell production and correlated to the optical detection results

Microcracks in silicon wafers reduce the strength of the wafers and can lead to critical failure within the solar-cell production. Both detection of the microcracks and their impact on fracture strength of the wafers are addressed within this study. To improve the accuracy of the crack detection in photoluminescence (PL) and infrared transmission (IR) images of as-cut wafers, we introduce a pattern recognition approach based on local descriptors and support-vector classification. The learning model requires a set of labeled data generated by an artificial insertion of cracks. Within this evaluation, the algorithm detects 81% of the cracks for PL-images and 98% for IR-images at precision rates above 98% in each case, which outperforms the quality of pure IR-intensitybased crack-detection systems with a hit-rate of 65% at a precision of 59%. The proposed algorithm may be combined with the images of the grain structure to avoid the confusion of cracks and grain boundaries. Moreover, the comprehensive set of wafers allows the impact of crack morphology on wafer strength to be investigated. Despite complex crack morphologies, the theoretically expected dependence between crack length and fracture strength is confirmed. Therefore, sorting criteria are derived to rate the cracks with respect to the expected fracture strength of the wafer based on the measured crack length only.

Nanoelectromechanical systems (NEMS) offer the potential to revolutionize fundamental methods employed for signal processing in today's telecommunication systems, owing to their spectral purity and the prospect of integration with existing technology. In this work we present a novel, front-end receiver topology based on a single device silicon nanoelectromechanical mixer-filter. The operation is demonstrated by using the signal amplification in a field effect transistor (FET) merged into a tuning fork resonator. The combination of both a transistor and a mechanical element into a hybrid unit enables on-chip functionality and performance previously unachievable in silicon. Signal mixing, filtering and demodulation are experimentally demonstrated at very high frequencies (>100 MHz), maintaining a high quality factor of Q = 800 and stable operation at near ambient pressure (0.1 atm) and room temperature (T = 300 K). The results show that, ultimately miniaturized, silicon NEMS can be utilized to realize multi-band, single-chip receiver systems based on NEMS mixer-filter arrays with reduced system complexity and power consumption.