Xiaofang Pan

Patterned dual-layer achromatic micro-quarter-wave-retarder array for active polarization imaging

In this paper, we present a liquid-crystal-polymer (LCP)-based dual-layer micro-quarter-wave-retarder (MQWR) array for active polarization image sensors. The proposed MQWRs, for the first time, enable the extraction of the incident lights circularly polarized components in the whole visible regime, which correspond to the fourth parameter of Stokes vector. Compared with the previous implementations, our proposed MQWRs feature high achromaticity, making their applications no longer limited to monochromatic illumination. In addition, the presented thin structure exhibits an overall thickness of 2.43μm, leading to greatly alleviated optical cross-talk between adjacent photo-sensing pixels. Moreover, the reported superior optical performance (e.g. minor transmittance, extinction ratio) validates our optical design and optimization of the proposed MQWRs. Furthermore, the demonstrated simple fabrication recipe offers a cost-effective solution for the monolithic integration between the proposed MQWR array and the commercial solid-state image sensors, which makes the multi-spectral full Stokes polarization imaging system on a single chip feasible.

Ultra-High Sensitivity Zinc Oxide Nanocombs for On-Chip Room Temperature Carbon Monoxide Sensing

In this paper, we report an on-chip gas sensor based on novel zinc oxide (ZnO) nanocombs for carbon monoxide (CO) sensing. With ZnO gas sensing nanocombs fully integrated on a single silicon chip, the concept of low cost complementary-metal-oxide-semiconductor (CMOS) microsensor capable of on-chip gas sensing and processing is enabled. Compared with all previous implementations, the proposed ZnO nanocombs feature much larger effective sensing area and exhibit ultra-high sensitivity even at the room temperature. Specifically, at room temperature, we demonstrate peak sensitivities as high as 7.22 and 8.93 for CO concentrations of 250 ppm and 500 ppm, respectively. As a result, by operating the proposed ZnO-nanocomb-based gas sensor at the room temperature, the widely adopted power consuming heating components are completely removed. This leads to not only great power saving, but also full compatibility between the gas sensor and the on-chip circuitry in term of acceptable operating temperature. In addition, the reported fast response/recovery time of ~200 s/~50 s (250 ppm CO) makes it well suited to real-life applications.

A hierarchical ZnO nanostructure gas sensor for human breath-level acetone detection

Analyzing the concentration of acetone in human breath constitutes a promising non-invasive means to diagnose the onset of diabetes, with acetone levels of at least 1.8ppm typically associated to individuals suffering from diabetes. In this paper, we report the performance of a hierarchical ZnO nanostructure gas sensor for acetone detection. The fabricated gas sensor can detect concentrations as low as 1ppm while operating at a comparatively lower temperature of 200°C. In addition, the proposed gas sensor can be fabricated on a silicon wafer using a MEMS process, making it thereby possible to fully integrate gas sensing and electronic circuitry on a single silicon chip.

An Analog Gamma Correction Scheme for High Dynamic Range CMOS Logarithmic Image Sensors

In this paper, a novel analog gamma correction scheme with a logarithmic image sensor dedicated to minimize the quantization noise of the high dynamic applications is presented. The proposed implementation exploits a non-linear voltage-controlled-oscillator (VCO) based analog-to-digital converter (ADC) to perform the gamma correction during the analog-to-digital conversion. As a result, the quantization noise does not increase while the same high dynamic range of logarithmic image sensor is preserved. Moreover, by combining the gamma correction with the analog-to-digital conversion, the silicon area and overall power consumption can be greatly reduced. The proposed gamma correction scheme is validated by the reported simulation results and the experimental results measured for our designed test structure, which is fabricated with 0.35 μm standard complementary-metal-oxide-semiconductor (CMOS) process.

Gas Classification Using Deep Convolutional Neural Networks

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

A Compact and Low Power RO PUF with High Resilience to the EM Side-Channel Attack and the SVM Modelling Attack of Wireless Sensor Networks

Authentication is a crucial security service for the wireless sensor networks (WSNs) in versatile domains. The deployment of WSN devices in the untrusted open environment and the resource-constrained nature make the on-chip authentication an open challenge. The strong physical unclonable function (PUF) came in handy as light-weight authentication security primitive. In this paper, we present the first ring oscillator (RO) based strong physical unclonable function (PUF) with high resilience to both the electromagnetic (EM) side-channel attack and the support vector machine (SVM) modelling attack. By employing an RO based PUF architecture with the current starved inverter as the delay cell, the oscillation power is significantly reduced to minimize the emitted EM signal, leading to greatly enhanced immunity to the EM side-channel analysis attack. In addition, featuring superior reconfigurability due to the conspicuously simplified circuitries, the proposed implementation is capable of withstanding the SVM modelling attack by generating and comparing a large number of RO frequency pairs. The reported experimental results validate the prototype of a 9-stage RO PUF fabricated using standard 65 nm complementary-metal-oxide-semiconductor (CMOS) process. Operating at the supply voltage of 1.2 V and the frequency of 100 KHz, the fabricated RO PUF occupies a compact silicon area of 250 μm2 and consumes a power as low as 5.16 μW per challenge-response pair (CRP). Furthermore, the uniqueness and the worst-case reliability are measured to be 50.17% and 98.30% for the working temperature range of −40∼120 ∘C and the supply voltage variation of ±2%, respectively. Thus, the proposed PUF is applicable for the low power, low cost and secure WSN communications.

Ultra-Low Power Smart Electronic Nose System Based on Three-Dimensional Tin-Oxide Nanotube Arrays

In this work, we present a high-performance smart electronic nose (E-nose) system consisting of a multiplexed tin oxide (SnO2) nanotube sensor array, read-out circuit, wireless data transmission unit, mobile phone receiver, and data processing application (App). Using the designed nanotube sensor device structure in conjunction with multiple electrode materials, high-sensitivity gas detection and discrimination have been achieved at room temperature, enabling a 1000 times reduction of the sensors power consumption as compared to a conventional device using thin film SnO2. The experimental results demonstrate that the developed E-nose can identify indoor target gases using a simple vector-matching gas recognition algorithm. In addition, the fabricated E-nose has achieved state-of-the-art sensitivity for H2 and benzene detection at room temperature with metal oxide sensors. Such a smart E-nose system can address the imperative needs for distributed environmental monitoring in smart homes, smart buildings, and smart cities.

Classification of Data from Electronic Nose Using Gradient Tree Boosting Algorithm

In this paper, an approach that can fast classify the data from the electronic nose is presented. In this approach the gradient tree boosting algorithm is used to classify the gas data and the experiment results show that the proposed gradient tree boosting algorithm achieved high performance on this classification problem, outperforming other algorithms as comparison. In addition, electronic nose we used only requires a few seconds of data after the gas reaction begins. Therefore, the proposed approach can realize a fast recognition of gas, as it does not need to wait for the gas reaction to reach steady state.

Fabrication of a low power CMOS-compatible ZnO nanocomb-based gas sensor

In this paper, a novel CMOS-compatible ZnO nanocomb-based gas sensor is presented. Compared with previously reported implementations, the proposed ZnO nanocombs feature multiple conducting channels and much larger effective sensing area, both of which result in dramatically improved sensitivity (6.54 for 250 ppm CO), response time (3.4 min) and recovery time (0.24 min). In addition, by operating the gas sensor at room temperature, additional power-hungry heating components inevitable in traditional implementations are completely removed. This not only leads to low power consumption, but also avoids the high-temperature-caused reliability degradation when integrated with CMOS circuitry.