Leakage and Thermal Reliability Optimization of Stacked Nanosheet Field-Effect Transistors with SiC Layers.

In this work, we propose a SiC-NSFET structure that uses a PTS scheme only under the gate, with SiC layers under the source and drain, to improve the leakage current and thermal reliability. Punch-through stopper (PTS) doping is widely used to suppress the leakage current, but aggressively high PTS doping will cause additional band-to-band (BTBT) current. Therefore, the bottom oxide isolation nanosheet field-effect transistor (BOX-NSFET) can further reduce the leakage current and become an alternative to conventional structures with PTS. However, thermal reliability issues, like bias temperature instability (BTI), hot carrier injection (HCI), and time-dependent dielectric breakdown (TDDB), induced by the self-heating effect (SHE) of BOX-NSFET, become more profound due to the lower thermal conductivity of SiO2 than silicon. Moreover, the bottom oxide will reduce the stress along the channel due to the challenges associated with growing high-quality SiGe material on SiO2. Therefore, this method faces difficulties in enhancing the mobility of p-type devices. The comprehensive TCAD simulation results show that SiC-NSFET significantly suppresses the substrate leakage current compared to the conventional structure with PTS. In addition, compared to the BOX-NSFET, the stress reduction caused by the bottom oxide is avoided, and the SHE is mitigated. This work provides significant design guidelines for leakage and thermal reliability optimization of next-generation advanced nodes.

Interaction of Negative Bias Instability and Self-Heating Effect on Threshold Voltage and SRAM (Static Random-Access Memory) Stability of Nanosheet Field-Effect Transistors.

In this paper, we investigate the effects of negative bias instability (NBTI) and self-heating effect (SHE) on threshold voltage in NSFETs. To explore accurately the interaction between SHE and NBTI, we established an NBTI simulation framework based on trap microdynamics and considered the influence of the self-heating effect. The results show that NBTI weakens the SHE effect, while SHE exacerbates the NBTI effect. Since the width of the nanosheet in NSFET has a significant control effect on the electric field distribution, we also studied the effect of the width of the nanosheet on the NBTI and self-heating effect. The results show that increasing the width of the nanosheet will reduce the NBTI effect but will enhance the SHE effect. In addition, we extended our research to the SRAM cell circuit, and the results show that the NBTI effect will reduce the static noise margin (SNM) of the SRAM cell, and the NBTI effect affected by self-heating will make the SNM decrease more significantly. In addition, our research results also indicate that increasing the nanosheet width can help slow down the NBTI effect and the negative impact of NBTI on SRAM performance affected by the self-heating effect.

A High-Accuracy RC Time Constant Auto-Tuning Scheme for Integrated Continuous-Time Filters.

The reliability of the resistor-capacitor (RC) time constant of a continuous-time (CT) filter has long been an obstacle with integrated circuits. Due to process and temperature variations in complementary metal-oxide semiconductor (CMOS) technology, the absolute value of the RC time constant may vary over ±50%, which is a big issue for many integrated continuous-time analog circuits. This study proposes an on-chip RC time constant auto-tuning scheme. The proposed scheme is based on the discrete master-slave auto-tuning concept. Considering the limitations in conventional works, a higher tuning accuracy is achieved by adopting two techniques: firstly, parasitic capacitance cancelation is proposed to eliminate the effects caused by parasitic capacitance; secondly, symmetric comparison is introduced to minimize the influence of the DC offset of the comparator. A successive approximation procedure is applied to improve the tuning speed. The proposed auto-tuning scheme has been validated in 55 nm CMOS technology with a fourth-order active-RC low-pass filter under PVT variations and 60 mV input offset voltage. The average tuning error is 2.21%, and the maximum error is 3.67%. The tuning error of the proposed scheme is considerably lower than the conventional scheme.

Band contour-extraction method based on conformal geometrical algebra for space tumbling targets.

To remove space debris actively, the key step is choosing a suitable capturing method. The size characteristics of space debris are an important factor that affects the capturing method. This paper presents a contour-extracting algorithm for rolling targets, such as space debris. The representation of the point in Euclidean space is converted to a conformal space to obtain candidate contour points through the computational geometric and topological relationships among the points, lines, and surfaces in a high-dimensional space. By combining this conversion with the multiview stitching algorithm, the complete candidate contour point model is obtained. The final contour points are extracted by determining the concave and convex points in conformal space. Simulation results prove that the unified expression of the geometric features in conformal space not only reduces the computational complexity but also increases the speed of the contour point extraction. The method proposed in this paper is superior to other methods in terms of measurement accuracy, speed, and robustness; in particular, the accuracy increased by 60%.

[Research on target identification by multi-spectrum separation algorithm].

In view of problems such as field poor shock resistance, low target identification rate, low real-time and so on in mechanical scanning optical system, a non-scanning target identification remote sensing system was designed using the multi-spectrum separation algorithm. Using the non-scanning M-Z interferometer to provide a space optical path difference, interference fringes were collected by infrared CCD detector. After CUP processing the system obtains the mix spectrum information, achieves target identification by the coordinate system combined with visible light video image, and the coordinate system, which the union visible light video image provides, achieves the target discrimination The genetic algorithm was used to optimize characteristic wavelengths, and then by the rough collection classification the unknown target spectrum's attribute was extracted. Taking first 1/3 confidence level of the corresponding attribute the testing target type was deduced, and compared with the traditional algorithm the amount of computing was reduced by about nine times. Experiment was done under different weather and different background conditions, so detection limits and identification probabilities of the system under different conditions were obtained. The experimental data showed that the genetic algorithm and rough set classification combined with multi-spectral separation algorithm can quickly and efficiently identify the unknown object types.