Design of atomically-thin-body field-effect sensors and pattern recognition neural networks for ultra-sensitive and intelligent trace explosive detection 
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The significance of detecting trace concentrations of chemical molecules in particular low-volatile explosives and explosive-related compounds has intensively increased in recent years for national security [1], environmental monitoring [2], and military applications [3]. With the large amount of energy stored in their chemical bonds, explosives such as trinitrotoluene (TNT), octogen (HMX), RDX (1,3,5-Trinitro-1,3,5-triazacyclohexane), pentaerythritol tetranitrate (PETN) usually result in great release of heat and light when detonated. The accurate detection of these explosive chemicals has become a multi-decade long challenge, in large part due to the inherently low vapor pressures of these
analytes. For instance, TNT exhibits a low vapor pressure of only 5.8 × 10−6 Torr at 25 °C (less than 10 parts-per-billion, that is, only a few TNT molecules in 1 billion molecules of air), while some non-volatile explosives such as HMX have extremely low vapor pressure down to a few parts-per-trillion (ppt) level (5 × 10−9 Torr) [4]. Great strides have been made on exploring trace explosives detection strategies to date. Existing trace detection methods include ion mobility spectrometry [5, 6], mass spectrometry [7, 8], surface acoustic wave detectors [9, 10], chemiluminescence [11, 12], electron capture detection [13, 14], and surface enhanced Raman spectroscopy [15, 16]. While being highly sensitive, these methods usually require bulky machines and swipe collection of particulate substance which are very expensive. Due to these
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Design of atomically-thin-body field-effect sensors and pattern recognition neural networks for ultra-sensitive and intelligent trace explosive detection
Yi Qiang1,5 , Ao Ren1,5, Xianzhe Zhang1, Preyaa Patel4, Xun Han1, Kyung Jin Seo1, Zhan Shi1, Yanzhi Wang1 and Hui Fang1,2,3,6
1 Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, United States of America 2 Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, United States of America 3 Department of Bioengineering, Northeastern University, Boston, MA 02115, United States of America 4 Department of Electrical Engineering, Indian Institute of Technology, Simrol, Madhya Pradesh, 453552 India 5 These authors contributed equally to this work. 6 Author to whom any correspondence should be addressed.
E-mail: h.fang@northeastern.edu
Keywords: trace explosive sensor, atomically-thin body semiconductors, pattern recognition, deep neural network
Supplementary material for this article is available online
Abstract There has been enormous demand for detecting trace concentrations of chemical molecules and in particular low-volatile explosives through electronic instrumentation, which however, still faces significant shortcomings in both detectability and selectivity to date. In this work, we propose a novel sensor strategy that incorporates arrays of atomically-thin-body field-effect sensors for highly-scalable, ultra-sensitive trace explosive sensors with fast response to ultra-low analyte concentrations. Sensor performance and functionalization are theoretically simulated through system-level considerations using various kinetic, electrostatic, quantum mechanics, and drift-diffusion models. Moreover, by implementing custom-built neural network models for pattern recognition, we successfully achieved automatic, accurate detection of four different types of analytes with concentrations down to 0.02 part per trillion. The intelligent sensors have the capability to differentiate analyte types with 100% accuracy and predict the concentration values with ~10% of relative error simultaneously. We envision the proposed sensor platform, design metrics, deep learning methods and the combination of these innovations will be a promising yet practical solution towards ultra-sensitive trace explosive detection and can be extended to a wide range of molecular sensing applications.
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limitations, trained dogs are still one of the most widely deployed mobile detection systems because they offer incomparable sensitivity in addition to their mobility and directionality. However, canine sensing effectiveness is a function of its training, mood, physical activity, etc. These challenges motivate researchers to develop systems that mimic dog nose.
Indeed, there have been enormous interests in the detection of trace concentrations of chemical mol- ecules by means of electronic instrumentation during the past thirty years, which has led to the development and commercialization of so-called ‘electronic noses’, typically comprising an array of partially selective sen- sors with analog outputs and suitable pattern recogni- tion algorithm to infer the type and concentration of volatile chemicals. In an ideal scenario, to achieve suc- cessful trace explosive detection using electronic sys- tems, the sensors need to possess the following charac- teristics: (1) sensitive to detect and quantify the trace amounts of airborne explosive molecules, to the ppt or sub-ppt level, within minutes or even seconds; (2) able to differentiate and provide orthogonality at large throughput to form multiplexed arrays required for selective sensing; (3) scalable to large-scale implemen- tation and manufacturing; (4) robust and stable with time and environmental changes; (5) miniaturized and low power consumption to be compatible with small form factor, handheld devices. Despite more than three decades of research on electronic noses around the world, there is currently no proven system that offers all of these properties.
Most existing electronic noses have utilized arrays of bulk complementary metal–oxide–semiconductor (CMOS)/microelectromechanical systems (MEMS) sensors which absorb volatile molecules on the sensor surface resulting in a change of physical state of the sen- sor [17–19]. Such electronic noses have already been adopted in many different industries, in areas such as food and beverage, pharmaceutical, cosmetic and per- fumes, flavor, and biomedical diagnostics [20–22]. However, currently there is no related technologies with detection limit down to even sub-ppb level. For example, the Cyranose® 320 electronic nose integrates 32 nanocomposite sensor arrays with online pattern recognition and memory, but with only low ppm level detection limit. Recently, sensors from amino-termi- nated, chemical-vapor-deposition (CVD) deposited Si nanowire field-effect sensors demonstrated one of the lowest detection limits for several explosive chemicals in aqueous solutions down to ppq concentration range [23]. This result encouragingly shows that miniaturized semiconducting channels are ultra-sensitive to external electrostatic perturbations and allow for sub-ppt-level chemical sensing. However, it may eventually pose sig- nificant technical challenges in scaling up these bottom- up Si nanowires towards both large-scale arrays and sys- tems in both reliable and uniform fashion.
Herein, we propose atomically-thin-body (ATB) semiconductor based, field-effect sensors to achieve
ultra-sensitive and fast-responsive explosive detec- tion with high sensor scalability. The surface of ATB semiconductors will be chemically functionalized with silane derivatives, to which the explosives molecules can strongly bind due to the interactions between elec- tron-deficient aromatic rings from the explosives and electron-rich amino groups from the silane deriva- tives [4]. This charge transfer process will form dipoles close to the ATB sensor surface, eventually resulting in the field-gating effect and explosive detection. Sen- sors with different functionalization will subsequently form partially orthogonal arrays yielding unique response pattern associated with different analytes which subsequently contribute to high selectivity. Due to their extreme level of miniaturization, ATB semi- conductors could achieve extreme level of field-effect sensitivity from each sensor. Physical implementa- tion of these sensors can take advantage of the exist- ing commercial ultra-thin body (UTB) silicon (Si) fabrication platform, or the emerging manufacturing of many other 2-dimensional (2D) materials such as molybdenum disulfide (MoS2) and graphene, which are both highly scalable.
In this work, we performed theoretical studies of the ATB field-effect sensors through systematic model-based simulation to predict individual sensor properties and neural networks (a 3-layer neural net- work is only a shallow network, not a deep network) based modelling to evaluate sensor array performance. The influence of key sensor parameters including channel length, width, thickness and doping concen- trations and impact of different functionalization are holistically studied to optimize the sensor design. Syn- thesized data from arrays of differentially functional- ized sensors and multiple analytes was then used to train the neural network, which generated distinctive pattern for each explosive analyte. As a proof of con- cept, the developed pattern recognition algorithm suc- cessfully achieved accurate recognition of four differ- ent analytes with a range of concentrations between 20 ppq to 1 ppt and were able to readout the precise analyte concentrations simultaneously. These results reveal the great potential of the ATB field-effect sen- sors to achieve ultra-sensitive and selective explosive detection with high scalability. We expect the proposed sensor design metrics and simulation approaches as well as neural network models to be a practical guide- line for developing highly scalable, ultra-sensitive and intelligent trace explosive sensors targeting at both detection and discrimination of ultra-low analyte con- centrations.
2. Results and discussion
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electro-mechanical systems (MEMS), photonic crystals and fluorescence polymers, etc. CMOS- compatible gas sensors developed in recent years with various detection methods are summarized in SI table 1 [23–36]. The sensing mechanism of the proposed ATB field-effect sensors is the field-gating effect originating from the charge transfer between analytes molecules and surface functionalization layers, eventually forming dipoles close to the sensor surface (figure 1(a)) [37]. In this work, we adopted ATB Si as the sensing material, but the same design concept can be applied to many other 2D semiconductors such as MoS2 with the same compatibility with neural network algorithms. In the simulation model using Synopsys Sentaurus TCAD platform, we defined the surface dipole as 2 fixed elementary charges (that is, each charge carries 1.602 × 10−19 C) with reversed signs (positive and negative) located above the surface of an atomically-thin (less than 5 nm thick) n-type Si layer (SI figure 1(a) (stacks.iop.org/TDM/6/044002/ mmedia)). The molecular dipoles were placed in air ambient (εr = 1) with the positive charges positioned closer to the sensor surface to mimic that the surface functionalization layer usually have electron-rich amino groups, which forms positive charges during the charge transfer process [4]. The simulation model does not include a gate oxide layer, but the threshold voltage of the Si channel can still be tuned by changing the doping concentration of the body. The source and drain contacts are set to be ideal Ohmic contact. Key parameters that are tuned in the present work are listed in SI figure 1(b). In this simulation, Poisson equations coupled with electron, hole and electron temperature continuity equations are self-consistently solved. We also adopted quantum confinement model considering the sub-5 nm semiconductor thickness. The formed surface dipoles induce a change of electrostatic potential inside the 2D semiconductor channel, which is visualized through the simulation software (figure 1(b)). With the positive charges formed closer to the channel, the electrostatic potential is increased throughout the channel as plotted in figure 1(c), resulting in the modulation of electron density inside the channel accordingly (figure 1(d)). Eventually, the increased electron density contributes to a larger output current of the sensor compared to the original state without surface dipoles (figure 1(e)). The trend of Id-Vd curve is not linear in this simulation due to the HighFieldSat model used, which refers to the saturation of carrier velocity when electric field increases to certain value. The results demonstrated in figure 1 are from representative simulation data to display the general trend while with qualitative relevance. Meanwhile, we propose a fabrication scheme to achieve the ATB Si field-effect sensor. Realizing the atomically thin Si layer is the most significant step in the fabrication process. Here we propose to adopt the thermal oxidation process of silicon on insulator (SOI) wafer using a dry-wet-dry process. The ultra-thin top
Si layer can then be achieved after the etching of the grown oxide layer. Similar fabrication process has been validated by literature to achieve 3.5 nm-thick silicon layer [33]. SI figure 2 illustrated the key fabrication steps of this process.
2.2. Sensor response time and chemical binding kinetics When detecting analytes with extremely low trace concentrations, the surface binding process on the sensors usually saturate slowly due to the low capture rate. To enable fast sensor responses, we considered the transient sensor output by simulating the dynamic molecule binding process. The binding kinetics between the functionalization layer and analyte molecules forming the so-called Meisenheimer complex [38, 39] is related to a number of parameters including the dimensions of the sensing layers, analyte concentrations and analyte types which determine the thermal velocity at a given temperature. The analyte capture rate, which is the number of analyte molecules bounded to the surface functionalization layer for a given time, is assumed to be linearly proportional to the analyte concentration. From molecular capturing perspective, the capture rate R can be expressed as R = σ · C · v, where σ is the capture cross section (L·W, where L and W stands for the channel length and width, respectively), C is the analyte concentration, and v is the thermal velocity. A dynamic molecular binding process is illustrated in figure 2(a) as the number of dipoles increase with the change of time. When the analyte concentrations become extremely low (a few ppt or lower), it usually takes more than a few minutes to reach the maximum amounts of molecules that could be captured to certain functionalization layers (saturation) [23]. Thus, to develop ultra-sensitive sensors with fast responses (within seconds level), the transient behaviour before the molecules binding saturation needs to be considered. As a matter of fact, before reaching the molecules binding saturation, there should be a continuous change of drain current as the number of dipoles generated on the sensor surface increases with time, which could be used for sensing. To understand the general trend and derive average values, we assumed that formed dipoles evenly distributed on the sensor surface. The ATB Si layer used for this simulation is 120 nm long, 100 nm wide and 2 nm thick. The n-type doping concentration adopted here is 1 × 1016 cm−3. The increase of drain current as a function of time was then observed by comparing the Id − Vd curves with different time (figure 2(b)). Interestingly, a highly close-to-linear increase of the drain current before saturation was observed, which motivated us to adopt metrics associated with the average current increase rate (δI′) as the alternative standard to characterize the sensitivities (figure 2(c)). Detailed electrostatic potential and electron density changes as a function of time (0–5 s) are available in SI figures 3 and 4, respectively. The design
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metric regarding the transient sensor output change, instead of considering the sensor status at saturation, will significantly shorten the sensor response time particularly when detecting ultra-low analyte concentrations. Notably, the time-window of the close-to-linear current increase is related to the analyte concentrations. Here we observed a window of linear- increasing of ~25 s for 1 ppt concentration, however,
with a much higher concentration such as 1 ppb, the time -window will be shortened to ~25 ms. Here, we focus on small sub-ppb level concentrations since they are currently beyond the detection limit of conventional technologies. For higher concentrations, one can use sensing metrics associated with the saturation current or combine with existing technologies, which will be the subject of subsequent studies.
Figure 1. Concept of ATB field-effect sensor. (a) Illustration of ATB field-effect sensor. (b) Electrostatic potential distribution of ATB field-effect sensor. Upper: without surface dipoles; bottom: with surface dipoles; dotted box: ATB channel area. (c) Electrostatic potential at the surface of an ATB channel. Black: without surface dipoles; red: with surface dipoles. (d) Electron density at the surface of the ATB channel. Black: without surface dipoles; red: with surface dipoles. (e) Drain current of ATB field effect sensor. Black: without surface dipoles; red: with surface dipoles. Note: figures (c) and (d) do not have scales and units to just show the general trend of field gating effect while with qualitative relevance.
Figure 2. Sensor response time and chemical binding kinetics. (a) Illustration of the dynamic molecules binding process. (b) Drain current (Id) versus drain voltage (Vd) curves with time = 0, 1, 2, 3, 4, 5 s. Inset: zoomed-in view of the multiple Id − Vd curves. (c) Drain current of ATB field-effect sensor as a function of time (Vd = 0.1 V). Inset: electrostatic potential distribution at time = 1 and 2 s.
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Generally, a higher δI′ for a given analyte of a given concentration should usually correspond to a larger detectability. However, using δI′ as a standalone metric will misguide the sensor design. For example, a large W of the ATB channel in the sensor can always guarantee a high δI′ without true improvement of the field effect caused by the dipole formation upon the channel, while W might be strictly controlled for footprint and power consumption requirements. Moreover, using δI′ alone does not capture the noise level in the initial current (I0), which is highly relevant for the detection of δI′ itself. It has been reported that the drain thermal noise current constitutes a major noise source of field- effect devices [40], and this noise is proportional to the drain current, which is dominated by I0 at the begin- ning of the molecules binding process if assuming the δI′ to be fairly small. We therefore propose to use the dimensionless, relative current change per second (δI′/I0) when characterizing the detectability. Using this metric will allow us to reflect the true field effect to the channel and embody the noise level.
2.3. Detectability considerations and design There are many important parameters substantially affecting the sensor detectability, which should be investigated for rational sensor designs. The sensor detectability could vary with different lengths, widths, thicknesses as well as doping concentrations of the ATB semiconductor channel. Here we specifically characterized δI′/I0 by simulating the dynamic molecules binding process from 0 to 5 s and averaging the current increase in the first 5 s. From the perspective of control experiments, we maintained the other parameters unchanged while tuning one of the selected ones. The positive charges in the dipoles were positioned at 2 nm high from Si surface where the nitro groups in the analyte molecules could be located as described in literature [4]. The distance between the 2 fixed single charges was set to be 0.3 nm, derived from the reported intermolecular length between analyte molecules and amino groups [41]. As discussed before, the increasing rate of drain current is positive with introduced dipoles, which is because the n-type Si channel works in the accumulation mode while the surface dipoles increase the major carrier densities (electrons) in the channel. Dimensions used for different simulations are labeled in each figure in figure 3 for a clear view.
We first examined the effect of different channel widths. As expected, the initial drain current I0 is lin- early proportional to the channel width (figure 3(a)). Meanwhile, as discussed in the previous section, the molecule capture rate also increases linearly with the channel width, which results in a direct increase of the δI′. The net result is that we observed minimal change of the δI′/I0 while tuning the channel width from 100 to 400 nm (figure 3(b)). The result also indi- cates that the channel width can be a scale factor for sensor designs as the output current can be tuned by
the channel width without compromising the device detectability.
We then studied the dependence of the channel thickness and length. To conclude the simulation results generally, smaller thicknesses lead to higher δI′/I0, which is expected due to a smaller initial cur- rent but with the same molecule capture rate. This trend is universal as we clearly observed from the δI′/I0 values as a function of thicknesses with different channel lengths and doping concentrations (figures 3(c) and (d)). Unlike the effects from channel width and thickness, the impact of channel length on δI′/I0 is more complicated and intriguing. We observed that with an increase of channel length, there is an optimal channel length resulting in maximum δI′/I0 per given doping concentration. This phenomenon can be explained by the trade-off between the single- dipole response and the number of dipoles generated with the change of channel lengths. Each single dipole formed on the sensor surface induces the change of electrostatic potential throughout the channel (SI figure 5(a)). We found that for different chan- nel length, the induced potential changes by a single dipole are similar (SI figure 5(b)). However, with a longer channel, the same gating volt age contributes to less current, indicating a lower response to a single dipole when having longer channels. Meanwhile, the molecule capture rate increases due to a larger sur- face area (W × L), resulting in a larger number of formed dipoles per given time. This trade-off along with the variation of initial current makes the trend of δI′/I0 unpredictable without systematic simula- tions. Taking advantage of the simulation approach, we observed that the optimal channel length varies with different doping concentrations (figure 3(e)). For example, the maximum δI′/I0 appears at 180 nm length for 1 × 1016 cm−3 doping concentration while at 100 nm length for 5 × 1016 cm−3 doping concen- tration.
Lastly, we studied the dependence of doping con- centrations. Generally, the sensors benefit from a lower doping concentration, but not the lower the better (figure 3(f)). In other words, there’s an optimal dop- ing concentration for each channel length and thick- ness, though the value is usually extremely low. For example, the optimal concentration for a 100 nm-long device is 5 × 1014 cm−3, which is not realistic for sen- sor deigns due to an ultra-low initial current compara- ble to the environmental noises in terms of amplitude. We simulated and listed the optimal channel lengths for different doping concentrations in the table shown in SI table 2. Besides the initial current, proper sen- sor size is also necessary for rational sensor design. As mentioned previously, the channel width can be tuned without compromising the δI′/I0. Here we also show the channel width needed for achieving an initial cur- rent of 1 µA with the optimal channel length selected, as a guideline for optimizing sensor designs within the boundary conditions.
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When the dipoles are formed further from the sensor surface, the field-gating effect becomes weaker as the induced electrostatic change are smaller. This expectation has been validated by simulation results. Figure 4(a) shows the time δI′/I0 as a function of dipoles heights ranging from 2 to 10 nm which are realistic molecule heights from literature [4]. The sim- ulation with dipoles having different inter-molecular length from 0.2 to 0.6 nm (which leads to different dipole moments) also verified our intuition that the Si sensor is less sensitive to dipoles with smaller dipole moment, resulting from a closer to neutral com- bined interaction from the positive and negative fixed charges (figure 4(b)). So far, we have simulated differ- ent dipole conditions where the dipoles are vertically aligned, however, the dipoles can form different angles in real settings. Here we simulated dipoles with 0° (ver- tically aligned), ±45°, ±90° to show the δI′/I0 varia- tion induced by different dipole angles (figure 4(c)),
with how the dipoles are paired illustrated in the insets using simulation visualization results. Dipoles verti- cally aligned lead to the largest δI′/I0 compared to other angles while dipoles with −90° even resulted in a slight decrease of drain current as the positive and negative charges are now placed at the same height from the sensor surface. Because we placed the dipoles equally distributed on the top of channel, placing them with positive and negative 90° (reversed charge orientation) caused the relative change of each dipole’s position, eventually resulting in the difference sensor responses.
Besides the dipole positions, analytes with different molecular weights and concentrations have consider- able influence on the sensor detectability as well. To probe into the details, we first calculated the capture rate under different analyte concentrations from 0.2 to 5 ppt and placed different number of dipoles on the sensor surface accordingly. Consistent with the exper- imental results from references, higher concentrations
Figure 3. Detectability dependence. (a) Left: drain current increase rate (δI′), Right: initial drain current (I0) as a function of channel widths. (b) Relative drain current increase rate (δI′/I0) as a function of channel widths. (c) Relative drain current increase rate (δI′/I0) as a function of channel thickness with different doping concentrations. (d) Relative drain current increase rate (δI′/I0) as a function of channel thickness with different channel length. (e) Relative drain current increase rate (δI′/I0) as a function of channel length with different doping concentrations. (f) Relative drain current increase rate (δI′/I0) as a function of doping concentrations with channel length. Labels in figures: device parameters used for different simulation.
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contribute to stronger sensor responses [23]. Notably, with 0.2 ppt analyte concentration, proposed field- effect sensor still possesses δI′/I0 of ~2.38% s−1 from simulation results, revealing an ultra-low detection limit (below ppt level). From simulation, it is difficult to determine the formed dipole conditions strictly fol- lowing the realistic cases. However, the different cap- ture rates can still be differentiated from molecular weights. For example, the molecular weights of com- mon explosive molecules like TNT, HMX, RDX are 227.13, 296.155, 222.12 g mol−1 respectively. We show a considerable change in δI′/I0 only resulting from the molecular weight differences as an inset of figure 4(d). When different types of analytes bind to the surface functionalization layers, the variation of dipole con- ditions and capture rate will result in the δI′/I0 differ- ence, subsequently leading to the selectivity of sensors. The choice of different set of functionalization layers according to the analyte chemistry can therefore form the sensor array to achieve selective sensing. While we assume a single layer of dipoles for all the simulations, the chemical molecules are actually complex with mul- tiple layers of charges which can induce channel mod- ulation. Here we simulated the effect of multiple layers of charges by stacking 1–3 layers of dipoles on top of the silicon channel. Distance between each dipole layer was set to be 1 nm. With more layers of dipoles placed, the sensor response increases with a nearly linear trend (SI figure 6). Further analysis will include more com- plicated dipoles arrangement.
Notably, the formation of native oxide at the sur- face could be present for Si. Here we added a 1 nm- thick SiO2 layer on top of the Si channel and adjusted the dipole distance from the device surface accordingly to study the influence of the oxide layer. Following the strategies mentioned above, we studied the detectabil- ity dependence on different dipole heights, channel lengths and doping concentrations. While we observed decreased sensitivities due to the capacitive oxide layer applied, the general trend of dependences on channel length and doping concentrations remains the same, indicating that the existence of the native oxide layer does not change the detectability dependence (SI fig- ure 7).
As stated before, the proposed sensor design met- rics and the simulation approach can be applied to any other channel materials. Here within the capabil- ity of Sentaurus TCAD software, we demonstrated the simulation results using Gallium Nitride (GaN) as the channel material. Sensitivities of GaN sensors with dif- ferent dipole positions were derived. With a higher car- rier mobility, ATB GaN sensor has better detectability performance than Si with the same dimensions and physical conditions (SI figure 8).
2.4. Pattern recognition algorithm based on neural networks To enable fast, accurate, and automatic recognition of explosive analytes, machine learning can greatly enhance the efficiency in analyte recognition. Machine
Figure 4. Selectivity considerations. (a) Relative drain current increase rate (δI′/I0) as a function of dipoles distance from the sensor surface h. Inset: illustrations of h. (b) Relative drain current increase rate (δI′/I0) as a function of intermolecular length d. Inset: illustrations of d. (c) Relative drain current increase rate (δI′/I0) as a function of angles of dipoles. Inset: simulation results of different dipole angles. (d) Relative drain current increase rate (δI′/I0) as a function of analyte concentrations. Inset: δI′/I0 versus analyte molecule weight.
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learning based pattern recognition algorithms, have shown excellent performance in many application domains [42–49] (SI table 3). There are already many great efforts on combining gas sensors with machine learning algorithms to enhance the sensor performance. SI table 4 shows recently developed gas sensors utilizing state-of-the art machine learning methods [50–56]. More existing work can be found in the [57].
As a branch of machine learning, neural networks have achieved great success in various fields, such as computer vision [56], speech recognition [58] and natural language processing [59], due to its remarkable ability in feature extraction and recognition. Since the explosive detection task is also based on the recogni- tion to the patterns formed by the sensor measurement results, we believe that neural network is a promising candidate for discovering the intrinsic features of the formed patterns.
For the neural network training purpose, we demonstrate an ATB field-effect sensor array consist- ing of six different sensors each with a different type of functionalization layer formed on the sensor sur- face (figure 5(a)). Sensors all include a 120 nm long, 100 nm wide and 2 nm thick Si layer to ensure good detectability results. We generated four different types of analytes with a concentration range from 0.02 ppt to 1 ppt. Functionalization layers and analytes used in this work are artificially designed with different mol- ecules heights (1.5–5 nm), molecules angles (0–180°), molecule weights (192, 227, 296, 316 g mol−1) as well as the intermolecular length (0.1–0.5 nm). All the numbers used for different sensor-analyte interfaces
were derived from literature to simulate the interac- tion between surface functionalization and explosive analytes reasonably to mimic the real-world com- plexity. Specifically, the parameters for analytes were based on properties of explosive chemicals including TNT, PETN, HMX, RDX. The surface functionaliza- tions designed in this work were based on properties of silane derivatives with electron-rich amino groups (i.e. APTES, APDMES, en-APTAS, etc) [4, 23]. The distance between the positive and negative charges were selected using the reported intermolecular length between nitroaromatic explosives and amine com- plexes [41]. Generated δI′/I0 data forms distinctive pattern for each analyte. Generally, the shapes of the generated patterns are independent of analyte con- centrations, although patterns generated from ultra- low concentrations will have severe variations and even negative values due to small numbers of formed dipoles (figure 5(b)). To better mimic the application environment in the real world, the fluctuation of input data (that is, the variance of molecules heights, dipole moments, analyte concentrations) was created by changing the input data to 80% ~120% of their origi- nal value. Having this range of fluctuation is reasona- ble in the experimental conditions and is also required for the neural network training purpose. We will train the neural network with experimental data in the future studies. Besides, a large amount of simulation data was generated, because neural networks generally require rich data for training to achieve the state-of- the-art recognition performance.
The neural networks built in this work were then trained and tested with the synthesized data generated
Figure 5. Pattern recognition algorithm based on neural networks. (a) Demonstration of the intelligent ATB field-effect sensor array. (b) Fingerprinting patterns generated by simulation data (δI′/I0) for 4 different analytes with 6 sensors. Upper row: analyte concentration = 1 ppt; Bottom row: analyte concentration = 0.02 ppt. (c) Relative prediction error of 0.1 ppt data with 2, 3, 4 analytes using 3, 4, 5, 6 sensors, respectively. Red dotted line: 10% relative error bar. (d) Relative prediction error of 0.1, 0.4, 0.8 ppt data using 3, 4, 5, 6 sensors, respectively. (e) Analyte type and concentration recognition results by training 0.02–1 ppt data.
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as described before. Detailed information about the self-built neural network model is available in the Methods section. Each set of data was composed of input data, which was sent to the inputs of the neural networks, and label data, which was used to compare with the outputs of the neural networks to evaluate their performance. Input data were composed of the sensors measurement results. For example, if 6 sen- sors are utilized to perform the detection, then the input data will contain 6 numbers while the corre- sponding output will consist of two numbers, one of which indicates the type of the analyte, and the other one presents the predicted concentration of the ana- lyte. In this work, 4 types of analytes and their concen- trations are detected and quantized with the neural networks. The neural networks are trained with com- binations of data of 4 analyte types and multiple con- centrations (0.02 ppt–1 ppt) to introduce high diversi- ties to the training data and thus enable the models to accurately differentiate different types of analytes and predict their concentrations. And the trained mod- els are tested with data of concentrations that are not included in the training data, to evaluate the generality of the models. For the prediction of analyte concen- trations, we investigated several approaches including (1) training lower concentrations data for predicting higher ones, (2) training higher concentrations data for predicting lower ones and (3) training certain con- centration nodes for predicting values in the range. The training and testing results validated that only the third approach could be achieved with low prediction errors and the range needs to be carefully determined (SI table 5). As it can be observed from SI table 6, the analyte types detection accuracy can reach 100% and the concentration prediction errors are remarkably low (<7% relative error), if the neural networks are trained with the combined 0.05, 0.2, 0.6, 1 ppt data sets and tested with individual 0.1, 0.4, 0.8 ppt data. Nota- bly, with the more numbers of analytes included, the relative detection error increases simultaneously (fig- ure 5(c)). One way to control the detection error is to increase the numbers of sensors. With 6 sensors used, the relative error appears to be the smallest compared to others (figure 5(d)), indicating that we can deter- mine the minimum numbers of sensors required for detecting certain numbers of analytes by simply setting a maximum error bar. For instance, if we set the largest error accepted to be ~10%, as shown in figure 5(c), the numbers of sensors needed for detecting 4 different analytes are 5 with the given error. Detecting 3 ana- lytes within the same error needs 4 different sensors and only 3 sensors are required for recognizing 2 ana- lytes. When using the combined 0.02, 0.2, 0.6, 0.1 ppt data sets for training, the type detection accuracy can still reach 100% with more than four sensors used for measurements and the relative error increased but still lower than ~13% with 6 sensors (figure 5(e)). The acc- uracy degradation of the latter combination is incurred by the low differentiability of 0.02 ppt as the patterns
become severely distorted at this low concentration, but it can be mitigated with more sensors being used. As a proof-of-concept, we successfully achieved the recognition and concentration prediction of 4 types of analytes with 0.02–1 ppt range. The results have dem- onstrated detection limit down to 20 ppq, superior to the state-of-the-art sensor performance. While highly promising, however this conclusion is only prelimi- nary currently as we have not conducted experimental validations of the device. Our future work will include the validation and optimization of our ATB field-effect sensor designs through fabricating and characterizing the real devices. By using this neural network model, with more sensors included and appropriate concen- tration nodes selected, we envision that we can achieve the detection of a large quantity of analytes with a con- tinuous range of concentrations down to tens of ppq level, to form intelligent explosive sensors with the capability of recognizing analyte types and quantizing the trace concentrations in real time.
3. Conclusion
In this work, we proposed highly scalable ATB field- effect sensors for fast-responsive, ultra-sensitive, and intelligent explosives detection. We validated the concept by demonstrating the theoretical results through system-level, model-based simulation based on the field-gating effect. The proposed sensor design benefits from the atomic-layer semiconductor materials and has the potential to achieve ultra- low detection limit (down to tens of ppq level) as well as fast response (within a few seconds). Through systematic simulation with different sensor dimensions and doping concentrations, we proposed the basic design rules for optimizing the ATB field- effect sensors with tunable initial current and sensor sizes. We also demonstrated the compatibility of the proposed sensor designs with the dedicated neural network algorithms and successfully achieved the recognition of various types of analytes with a large, continuous concentration range. We envision that the sensors design metrics, simulation approaches and neural network models can be applicable to other 2D semiconductor materials, facilitating the development of fast-response, ultra-sensitive trace explosive sensors for a wide range of molecules sensing applications. Future work should focus on the large- scale implementation of the ATB field-effect sensor system using nano-fabrication technologies and its validation in the real-world environment.
4. Methods
4.1. Synopsys TCAD simulation All the simulation results are computed using Synopsis Sentaurus TCAD (Version M-2016.12). Simulation models were defined using Sentaurus Structure Editor and device simulation was
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implemented using Sentaurus Device in this work. While mesh size of non-important region of air ambient was set to be around 10 to 20 nm, that of the Si and region with dipoles was set to be smaller than 0.1 nm. This meshing approach is to manage a balance between computation time and accuracy of the results. Carrier transport in devices was simulated by solving Poisson continuity equations coupled with electrons, holes and electrons temperature continuity equations self-consistently (Hydrodynamic model). Quantum confinement effects related to nano-scale devices were considered using the density-gradient quantization model. The Slotboom and Graaff bandgap narrowing model was used during the simulation to be consistent with the density-gradient model. Additionally, doping-dependent Shockley– Reed–Hall recombination model was included with the band-band tunneling model. The simulation models used are similar to previous studies of ultra- thin silicon devices [60]. Data analysis including the plotting of drain current, electrostatic potential and electrons densities was enabled by the Sentaurus SVisual software.
4.2. Neural network training Each layer of a fully-connected neural network calculates xi+1 = h(W i × xi + bi), where xi and xi+1 are the input and output vector of the ith layer, respectively, W i and bi are the weight parameter matrix and bias vector of the ith layer, and h(·) denotes the activation function, which introduces nonlinearities into the neural network. In the training phase, the outputs of the output layer are compared with labels (i.e. the expected correct outputs) to generate losses, then the losses are back-propagated to update the weight and bias parameters through stochastic gradient descent [61]. In the inference phase, the weight and bias parameters are fixed, and the outputs at the output layer are the predictions of the model.
In this work, 3-layer fully-connected neu- ral networks are built with the configuration of (input_size, 70)− (70, 70)− (70, output_size), where the first number in a pair (·, ·) denotes the input size of that layer, and the second number denotes the number of neurons in that layer. input_size is deter- mined by the data sent to the first layer, which is the number of sensors that are used to perform the detec- tion task. And output_size = analyte types + 1, the added ‘1’ is due to the capability of our model not only recognizing the types of the explosive analytes, but also detecting their concentrations. The activation func- tion used in the neural networks is the rectified linear unit (ReLU), which calculates f (x) = max(x, 0) [62] (SI figure 9).
Acknowledgments
Author contributions
YQ and AR contributed equally to this work. YQ, AR, YW, and HF designed the research; YQ, XZ, PP performed the Sentaurus TCAD simulation; AR, YQ, carried out the neural network training and pattern recognition studies; YQ, AR, XH, KJS performed the data analysis. YQ, AR, YW, and HF co-wrote the manuscript.
ORCID iDs
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Abstract
2.3. Detectability considerations and design
2.4. Pattern recognition algorithm based on neural networks
3. Conclusion
4. Methods