The Revolution in Breast Cancer Diagnostics: From Visual Inspection of Histopathology Slides to Using Desktop Tissue Analysers for Automated Nanomechanical Profiling of Tumours

We aim to develop new portable desktop tissue analysers (DTAs) to provide fast, low-cost, and precise test results for fast nanomechanical profiling of tumours. This paper will explain the reasoning for choosing indentation-type atomic force microscopy (IT-AFM) to reveal the functional details of cancer. Determining the subtype, cancer stage, and prognosis will be possible, which aids in choosing the best treatment. DTAs are based on fast IT-AFM at the size of a small box that can be made for a low budget compared to other clinical imaging tools. The DTAs can work in remote areas and all parts of the world. There are a number of direct benefits: First, it is no longer needed to wait a week for the pathology report as the test will only take 10 min. Second, it avoids the complicated steps of making histopathology slides and saves costs of labour. Third, computers and robots are more consistent, more reliable, and more economical than human workers which may result in fewer diagnostic errors. Fourth, the IT-AFM analysis is capable of distinguishing between various cancer subtypes. Fifth, the IT-AFM analysis could reveal new insights about why immunotherapy fails. Sixth, IT-AFM may provide new insights into the neoadjuvant treatment response. Seventh, the healthcare system saves money by reducing diagnostic backlogs. Eighth, the results are stored on a central server and can be accessed to develop strategies to prevent cancer. To bring the IT-AFM technology from the bench to the operation theatre, a fast IT-AFM sensor needs to be developed and integrated into the DTAs.

Research on Pedestrian Indoor Positioning Based on Two-Step Robust Adaptive Cubature Kalman Filter with Smartphone MEMS Sensors

With the development of location-based service (LBS), indoor positioning based on pedestrian dead reckoning (PDR) has become a hot research topic. Smartphones are becoming more popular for indoor positioning. This paper proposes a two-step robust-adaptive-cubature Kalman filter (RACKF) algorithm based on smartphone micro-electro-mechanical-system (MEMS) sensor fusion for indoor positioning. To estimate pedestrian heading, a quaternion-based robust-adaptive-cubature Kalman filter algorithm is proposed. Firstly, the model noise parameters are adaptively corrected based on the fading-memory-weighting method and the limited-memory-weighting method. The memory window of the limited-memory-weighting algorithm is modified based on the characteristics of pedestrian walking. Secondly, an adaptive factor is constructed based on the partial state inconsistency to overcome filtering-model deviation and abnormal disturbances. Finally, to identify and control the measurement outliers, the robust factor based on maximum-likelihood estimation is introduced into the filtering to enhance the robustness of heading estimation and support more robust dynamic-position estimation. In addition, based on the accelerometer information, a nonlinear model is constructed and the empirical model is used to estimate the step length. Combining heading and step length, the two-step robust-adaptive-cubature Kalman filter is proposed to improve the pedestrian-dead-reckoning method, which enhances the adaptability and robustness of the algorithm and further improves the accuracy of the plane-position solution. The adaptive factor based on the prediction residual and the robust factor based on the maximum-likelihood estimation are introduced into the filter to improve the adaptability and robustness of the filter, reduce the positioning error, and improve the accuracy of the pedestrian-dead-reckoning method. Three different smartphones are used to validate the proposed algorithm in an indoor environment. Additionally, the experimental results confirm the algorithm's effectiveness. From the results of the three smartphones, the root mean square error (RMSE) of the indoor-positioning results obtained by the proposed method is about 1.3-1.7 m.

Wearable Multi-Channel Pulse Signal Acquisition System Based on Flexible MEMS Sensor Arrays with TSV Structure

Micro-electro-mechanical system (MEMS) pressure sensors play a significant role in pulse wave acquisition. However, existing MEMS pulse pressure sensors bound with a flexible substrate by gold wire are vulnerable to crush fractures, leading to sensor failure. Additionally, establishing an effective mapping between the array sensor signal and pulse width remains a challenge. To solve the above problems, we propose a 24-channel pulse signal acquisition system based on a novel MEMS pressure sensor with a through-silicon-via (TSV) structure, which connects directly to a flexible substrate without gold wire bonding. Firstly, based on the MEMS sensor, we designed a 24-channel pressure sensor flexible array to collect the pulse waves and static pressure. Secondly, we developed a customized pulse preprocessing chip to process the signals. Finally, we built an algorithm to reconstruct the three-dimensional pulse wave from the array signal and calculate the pulse width. The experiments verify the high sensitivity and effectiveness of the sensor array. In particular, the measurement results of pulse width are highly positively correlated with those obtained via infrared images. The small-size sensor and custom-designed acquisition chip meet the needs of wearability and portability, meaning that it has significant research value and commercial prospects.

MIMU Optimal Redundant Structure and Signal Fusion Algorithm Based on a Non-Orthogonal MEMS Inertial Sensor Array

A micro-inertial measurement unit (MIMU) is usually used to sense the angular rate and acceleration of the flight carrier. In this study, multiple MEMS gyroscopes were used to form a spatial non-orthogonal array to construct a redundant MIMU system, and an optimal Kalman filter (KF) algorithm was established by a steady-state KF gain to combine array signals to improve the MIMU's accuracy. The noise correlation was used to optimize the geometric layout of the non-orthogonal array and reveal the mechanisms of influence of correlation and geometric layout on MIMU's performance improvement. Additionally, two different conical configuration structures of a non-orthogonal array for 4,5,6,8-gyro were designed and analyzed. Finally, a redundant 4-MIMU system was designed to verify the proposed structure and KF algorithm. The results demonstrate that the input signal rate can be accurately estimated and that the gyro's error can also be effectively reduced through fusion of non-orthogonal array. The results for the 4-MIMU system illustrate that the gyro's ARW and RRW noise can be decreased by factors of about 3.5 and 2.5, respectively. In particular, the estimated errors (1σ) on the axes of X_b, Y_b and Z_b were 4.9, 4.6 and 2.9 times lower than that of the single gyroscope.

Reliability Evaluation Based on Mathematical Degradation Model for Vacuum Packaged MEMS Sensor

Vacuum packaging is used extensively in MEMS sensors for improving performance. However, the vacuum in the MEMS chamber gradually degenerates over time, which adversely affects the long-term performance of the MEMS sensor. A mathematical model for vacuum degradation is presented in this article for evaluating the degradation of vacuum packaged MEMS sensors, and a temperature-accelerated test of MEMS gyroscope with different vacuums is performed. A mathematical degradation model is developed to fit the parameters of the degradation of Q-factor over time at three different temperatures. The results indicate that the outgassing rate at 85 °C is the highest, which is 0.0531 cm²/s; the outgassing rate at 105 °C is the lowest, which is 0.0109 cm²/s; and the outgassing rate at 125 °C is in the middle, which is 0.0373 cm²/s. Due to the different mechanisms by which gas was released, the rate of degradation did not follow this rule. It will also be possible to predict the long-term reliability of vacuum packaged MEMS sensors at room temperature based on this model.

Comparative Study of Gravimetric Humidity Sensor Platforms Based on CMUT and QCM

Humidity sensors with comprehensive performance are of great interest for industrial and environmental applications. Most sensors, however, have to compromise on at least one characteristic such as sensitivity, response speed, and linearity. This paper reports a gravimetric humidity sensor based on a capacitive micromachined ultrasonic transducer (CMUT) with exceptional all-around performance, and presents a side-by-side comparative investigation of two types of gravimetric humidity sensors for a better understanding of their characteristics and sensing mechanisms. For these purposes, a circular CMUT and a quartz crystal microbalance (QCM) with a resonance frequency of 10 MHz were designed and fabricated. Poly(vinyl alcohol) (PVA) was employed as the humidity sensing layer for its hydrophilicity and ease of film formation. The electrical properties of the sensors, including the electrical input impedances and quality factors, were characterized by a vector network analyzer. The relative humidity (RH) sensing performance of the sensors was evaluated and compared from RH levels of 11% to 97%. Both sensors exhibited good repeatability and low hysteresis. The unique microscale resonant structure of the CMUT humidity sensor contributed to a high sensitivity of 2.01 kHz/%RH, short response and recovery times of 8 s and 3 s, respectively, and excellent linearity (R² = 0.973), which were far superior to their QCM counterparts. The underlying mechanism was revealed and discussed.

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s² to -1.2646 × 10^-7 m/s², while the variance decreases from 0.0133 m/s² to 1.0975 × 10^-5 m/s². In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.

[Portable Monitoring Device for Motor Rehabilitation Equipment with Rotor Shaft]

A novel and portable device is proposed to monitor motor rehabilitation equipment, which can be mounted on most equipment with rotor shaft. The software of the device, whose main functions include equipment configuration, monitoring and statistical computation, is developed based on available sensor. The data collected by the device serve both department managers to learn the efficiency of the equipment, and physicians and therapists to understand the physical conditions of the patients who perform training exercises with the monitored rehabilitation equipment. About 2000 hours' monitoring has been carried out, and the experimental result indicates that the monitoring device is applicable to many types of motor rehabilitation equipment and achieves good monitoring accuracy. The data aggregated by the device can be used to evaluate the motor functions of the patients and make rehabilitation training plan. Besides, it is agreed by physicians and therapists that the device is easy-to-use, robust and has good real-time performance. The monitoring device thus holds the promise of boosting the development of digitalized rehabilitation medicine.

Thermal Conductivity Gas Sensor with Enhanced Flow-Rate Independence

In this article, novel thermal gas sensors with newly designed diffusion gas channels are proposed to reduce the flow-rate disturbance. Simulation studies suggest that by lowering the gas flow velocity near the hot film, the maximum normalized temperature changes caused by flow-rate variations in the two new designs (Type-H and Type-U) are decreased to only 1.22% and 0.02%, which is much smaller than in the traditional straight design (Type-I) of 20.16%. Experiment results are in agreement with the simulations that the maximum normalized flow-rate interferences in Type-H and Type-U are only 1.51% and 1.65%, compared to 24.91% in Type-I. As the introduced CO₂ flow varied from 1 to 20 sccm, the normalized output deviations in Type-H and Type-U are 0.38% and 0.02%, respectively, which are 2 and 3 orders of magnitude lower than in Type-I of 10.20%. In addition, the recovery time is almost the same in all these sensors. These results indicate that the principle of decreasing the flow velocity near the hot film caused by the two novel diffusion designs can enhance the flow-rate independence and improve the accuracy of the thermal conductivity as well as the gas detection.

Mechanical Energy Sensing and Harvesting in Micromachined Polymer-Based Piezoelectric Transducers for Fully Implanted Hearing Systems: A Review

The paper presents a comprehensive review of mechanical energy harvesters and microphone sensors for totally implanted hearing systems. The studies on hearing mechanisms, hearing losses and hearing solutions are first introduced to bring to light the necessity of creating and integrating the in vivo energy harvester and implantable microphone into a single chip. The in vivo energy harvester can continuously harness energy from the biomechanical motion of the internal organs. The implantable microphone executes mechanoelectrical transduction, and an array of such structures can filter sound frequency directly without an analogue-to-digital converter. The revision of the available transduction mechanisms, device configuration structures and piezoelectric material characteristics reveals the advantage of adopting the polymer-based piezoelectric transducers. A dual function of sensing the sound signal and simultaneously harvesting vibration energy to power up its system can be attained from a single transducer. Advanced process technology incorporates polymers into piezoelectric materials, initiating the invention of a self-powered and flexible transducer that is compatible with the human body, magnetic resonance imaging system (MRI) and the standard complementary metal-oxide-semiconductor (CMOS) processes. The polymer-based piezoelectric is a promising material that satisfies many of the requirements for obtaining high performance implantable microphones and in vivo piezoelectric energy harvesters.

Miniaturized Hybrid Frequency Reader for Contactless Measurement Scenarios Using Resonant Surface Acoustic Wave Sensors

Due to higher automation and predictive maintenance, it becomes more and more important to acquire as many data as possible during industrial processes. However, many scenarios require remote sensing since either moving parts would result in wear and tear of cables or harsh environments prevent a wired connection. In the last few years, resonant surface acoustic wave (SAW) sensors have promised the possibility to be interrogable wirelessly which showed very good results in first studies. Therefore, the sensor's resonance frequency shifts due to a changed measurand and thus has to be determined. However, up to now frequency reader systems showed several drawbacks like high costs or insufficient accuracy that blocked the way for a widespread usage of this approach in the mass market. Hence, this article presents a miniaturized and low cost six-port based frequency reader for SAW resonators in the 2.45 GHz ISM band that does not require an external calculation unit. It is shown that it can be either used to evaluate the scenario or measure the frequency directly with an amplitude or phase measurement, respectively. The performance of the system, including the hardware and embedded software, is finally shown by wired and contactless torque measurements.

Ultra-Sensitive Minute Mass Sensing Using a Microcantilever Virtually Coupled with a Virtual Cantilever

Mass sensors based on the eigenmode shift of coupled cantilevers achieve much higher sensitivity than those based on the single cantilever’s eigenfrequency shift. In the former sensors, two identical cantilevers and a weak coupling stiffness between them are required to achieve high sensitivity. However, conventional coupled cantilevers cannot satisfy these requirements because of machining accuracy. To satisfy both requirements, a virtual coupling between a real macrocantilever and a virtual cantilever, whose dynamics was calculated using a digital computer, was proposed in our previous research. The sensitive mass sensing of mg-order masses was achieved. In the present work, for minute mass sensing, we replace the real macrocantilever with a real microcantilever. The calculation speed of a digital computer is not fast enough to calculate the virtual cantilever’s dynamics because the natural frequency of the microcantilver is much higher than that of the macrocantilever. Therefore, we use an analog circuit instead of a digital computer to achieve virtual coupling with the virtual cantilever. The proposed system enables us to tune the virtual cantilever’s parameters to satisfy both requirements for high sensitivity by changing the analog circuit parameters. We verified experimentally that the proposed system achieved high sensitivity for mass sensing of the order of nanograms.

Pentagram-Shaped Ag@Pt Core-Shell Nanostructures as High-Performance Catalysts for Formaldehyde Detection

High-performance HCHO sensors are of great importance in various application fields such as indoor air quality assessments. Herein, bimetallic Ag-Pt nanoparticles are synthesized as high-performance catalysts for ZnO-based gas sensors. Spherical aberration (Cs)-corrected transmission electron microscopy images with atomic resolution clearly indicate that the prepared nanoparticles exhibit a novel Ag@Pt core-shell nanostructure with a pentagram shape. For high-performance HCHO sensor construction, integrated micro-electrodes are first fabricated with the microelectromechanical system (MEMS) technology. Then, the hydrothermal route is used to self-assemble well-aligned ZnO nanowire arrays onto the sensing microregion. After that, the pentagram-shaped Ag@Pt nanoparticles are loaded onto the surface of ZnO nanowires with the inkjet printing technique to form MEMS sensors with Ag@Pt@ZnO as the sensing material. The thoroughly sensing experiments indicate that the Ag@Pt nanoparticles exhibit satisfied catalytic activation to HCHO molecules. The experimental observed detection limit of our sensor to HCHO reaches the parts per billion level. To elucidate the HCHO-sensing mechanism, the online mass spectrum (online MS) is utilized to analyze the components of exhaust gas stream of HCHO flowing through the Ag@Pt@ZnO material. The online MS indicates that with the Ag@Pt catalyst, HCHO molecules are partially oxidized to HCOOH molecules at low temperatures and are completely oxidized to CO₂ molecules at high temperatures.

Development Trends and Perspectives of Future Sensors and MEMS/NEMS

With the fast development of the fifth-generation cellular network technology (5G), the future sensors and microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS) are presenting a more and more critical role to provide information in our daily life. This review paper introduces the development trends and perspectives of the future sensors and MEMS/NEMS. Starting from the issues of the MEMS fabrication, we introduced typical MEMS sensors for their applications in the Internet of Things (IoTs), such as MEMS physical sensor, MEMS acoustic sensor, and MEMS gas sensor. Toward the trends in intelligence and less power consumption, MEMS components including MEMS/NEMS switch, piezoelectric micromachined ultrasonic transducer (PMUT), and MEMS energy harvesting were investigated to assist the future sensors, such as event-based or almost zero-power. Furthermore, MEMS rigid substrate toward NEMS flexible-based for flexibility and interface was discussed as another important development trend for next-generation wearable or multi-functional sensors. Around the issues about the big data and human-machine realization for human beings' manipulation, artificial intelligence (AI) and virtual reality (VR) technologies were finally realized using sensor nodes and its wave identification as future trends for various scenarios.

Analytical Model and Experimental Evaluation of the Micro-Scale Thermal Property Sensor for Single-Sided Measurement

We report a new analytical model of the MEMS-based thermal property sensor for samples which are difficult to handle and susceptible to damage by thermal stimulus, such as living cells. Many sensor designs had been reported for thermal property measurements, but only a few of them have considered the analytical model of the single-sided measurement in which a measurement sample is placed on the sensor substrate. Even in the few designs that have considered the analytical model, their applicable limits are restricted to more than 1 mm length in practical situations. Our new model considers both the sample and the sensor substrate thermal properties and is applicable to a sensor length less than 1 µm. In order to minimize the influence of the heat stimulus to the sample, the model formulates the required heat dissipating time for different sensor geometries. We propose fast and precise detection circuit architecture to realize our model, and we discuss the sensor performance for a number of different designs.

Wearable Pulse Wave Monitoring System Based on MEMS Sensors

Pulse wave monitoring is critical for the evaluation of human health. In this paper, a wearable multi-sensor pulse wave monitoring system is proposed and demonstrated. The monitoring system consists of a measuring unit and an analog circuit processing unit. The main part of the measuring unit is a flexible printed circuit board (PCB) with a thickness of 0.15 mm, which includes three micro-electromechanical system (MEMS) pressure sensors softly packaged by polydimethylsiloxane (PDMS), a blood oxygen detector and a MEMS three-axis accelerometer. The MEMS pressure sensors，the blood oxygen detector and the accelerometer are fixed on the expected locations of the flexible PCB. The analog circuit processing unit includes a power supply module, a filter and an amplifier. The pulse waves of two volunteers are detected by the monitoring system in this study. The output signals of the analog circuit processing module are processed and analyzed. In the preliminary test, the time delay of the three pressure pulse waves has been detected and the calculated pulse wave velocities (PWVs) are 12.50 and 11.36 m/s, respectively. The K value, related to the area of the pulse wave, can be obtained. Both the PWV and K value meet the health parameter standards.

A technical review and evaluation of implantable sensors for hearing devices

Most commercially available cochlear implants and hearing aids use microphones as sensors for capturing the external sound field. These microphones are in general located in an external element, which is also responsible for processing the sound signal. However, the presence of the external element is the cause of several problems such as discomfort, impossibility of being used during physical activities and sleeping, and social stigma. These limitations have driven studies with the goal of developing totally implantable hearing devices, and the design of an implantable sensor has been one of the main challenges to be overcome. Different designs of implantable sensors can be found in the literature and in some commercial implantable hearing aids, including different transduction mechanisms (capacitive, piezoelectric, electromagnetic, etc), configurations microphones, accelerometers, force sensor, etc) and locations (subcutaneous or middle ear). In this work, a detailed technical review of such designs is presented and a general classification is proposed. The technical characteristics of each sensors are presented and discussed in view of the main requirements for an implantable sensor for hearing devices, including sensitivity, internal noise, frequency bandwidth and energy consumption. The feasibility of implantation of each sensor is also evaluated and compared.

AngleNav: MEMS Tracker to Facilitate CT-Guided Puncture

As a low-cost needle navigation system, AngleNav may be used to improve the accuracy, speed, and ease of CT-guided needle punctures. The AngleNav hardware includes a wireless device with a microelectromechanical (MEMS) tracker that can be attached to any standard needle. The physician defines the target, desired needle path and skin entry point on a CT slice image. The accuracy of AngleNav was first tested in a 3D-printed calibration platform in a benchtop setting. An abdominal phantom study was then performed in a CT scanner to validate the accuracy of the device's angular measurement. Finally, an in vivo swine study was performed to guide the needle towards liver targets (n = 8). CT scans of the targets were used to quantify the angular errors and needle tip-to-targeting distance errors between the planned needle path and the final needle position. The MEMS tracker showed a mean angular error of 0.01° with a standard deviation (SD) of 0.62° in the benchtop setting. The abdominal phantom test showed a mean angular error of 0.87° with an SD of 1.19° and a mean tip-to-target distance error of 4.89 mm with an SD of 1.57 mm. The animal experiment resulted in a mean angular error of 6.6° with an SD of 1.9° and a mean tip-to-target distance error of 8.7 mm with an SD of 3.1 mm. These results demonstrated the feasibility of AngleNav for CT-guided interventional workflow. The angular and distance errors were reduced by 64.4 and 54.8% respectively if using AngleNav instead of freehand insertion, with a limited number of operators. AngleNav assisted the physicians to deliver accurate needle insertion during CT-guided intervention. The device could potentially reduce the learning curve for physicians to perform CT-guided needle targeting.

Experimental Study of Kinematic Activity of Left Ventricular Myocardium

A method of invasive and non-invasive examination of epicardial motion was tested on domestic pigs (n=15). The invasive study employed a contact kinematic sensor of the epicardial wall motion, which determined the amplitude, velocity, and angular parameters of examined epicardial region. A non-invasive assessment of the epicardial kinematics based on echocardiographic 2D Velocity Vector Imaging and ECG recoding used to determine the heart electrical axis. The 4-chamber views were uses to analyze the longitudinal displacements of lateral wall of the left ventricle at the basal, mid, and apical segments. In addition, the radial displacements were determined in short axis projections at basal and apical segments as well as at the level of papillary muscles. The animals with normal or horizontal heart's electrical axis demonstrated similar displacement of the epicardium along X-axis and in longitudinal direction, while displacement along Y-axis corresponded to that in radial direction. The combined invasive and non-invasive approach in measurement of epicardium motion makes it possible to quantify the kinematic epicardial activity.

Accurate characterization of benign and cancerous breast tissues: aspecific patient studies using piezoresistive microcantilevers

Breast cancer is the largest detected cancer amongst women in the US. In this work, our team reports on the development of piezoresistive microcantilevers (PMCs) to investigate their potential use in the accurate detection and characterization of benign and diseased breast tissues by performing indentations on the micro-scale tissue specimens. The PMCs used in these experiments have been fabricated using laboratory-made silicon-on-insulator (SOI) substrate, which significantly reduces the fabrication costs. The PMCs are 260 μm long, 35 μm wide and 2 μm thick with resistivity of order 1.316×10(-3) Ω cm obtained by using boron diffusion technique. For indenting the tissue, we utilized 8 μm thick cylindrical SU-8 tip. The PMC was calibrated against a known AFM probe. Breast tissue cores from seven different specimens were indented using PMC to identify benign and cancerous tissue cores. Furthermore, field emission scanning electron microscopy (FE-SEM) of benign and cancerous specimens showed marked differences in the tissue morphology, which further validates our observed experimental data with the PMCs. While these patient aspecific feasibility studies clearly demonstrate the ability to discriminate between benign and cancerous breast tissues, further investigation is necessary to perform automated mechano-phenotyping (classification) of breast cancer: from onset to disease progression.

Towards an Automated MEMS-based Characterization of Benign and Cancerous Breast Tissue using Bioimpedance Measurements

Micro-Electro-Mechanical-Systems (MEMS) are desirable for use within medical diagnostics because of their capacity to manipulate and analyze biological materials at the microscale. Biosensors can be incorporated into portable lab-on-a-chip devices to quickly and reliably perform diagnostics procedure on laboratory and clinical samples. In this paper, electrical impedance-based measurements were used to distinguish between benign and cancerous breast tissues using microchips in a real-time and label-free manner. Two different microchips having inter-digited electrodes (10 µm width with 10 µm spacing and 10 µm width with 30 µm spacing) were used for measuring the impedance of breast tissues. The system employs Agilent E4980A precision impedance analyzer. The impedance magnitude and phase were collected over a frequency range of 100 Hz to 2 MHz. The benign group and cancer group showed clearly distinguishable impedance properties. At 200 kHz, the difference in impedance of benign and cancerous breast tissue was significantly higher (3110 Ω) in the case of microchips having 10 µm spacing compared to microchip having 30 µm spacing (568 Ω).