A range of medical issues are treated by simple biomechanical implants to regulate fluid pressure and flow (e.g. valves, shunts). With moving parts in fluid, these implants are vulnerable to biological failures (infection, migration), mechanical failures (clogging, cracking), and parametric failures (change in flow resistance, cracking pressure). Existing biomechanical implants only show failure by clinical symptoms, which may be catastrophic. A means to better observe device behavior and predict failure is necessary. We explore merging biomechanical implants with low-footprint passive electronics, creating bio-electro-mechanical (BEM) devices and thereby allowing external monitoring. Passive feedback signals (RF backscatter) may be interpreted by a model to extract flow parameters and predict failure. A model may be trained by benchtop testing, to correlate direct measurements (flow, pressure) with passive device signals. Benchtop failure simulation (accelerated aging, simulated biofouling) may better train the model for failure prediction. This paper uses long-term pressure/flow testing data from a simple biomechanical device (hydrogel valve for hydrocephalus) as a test case for extracting predictive signals of imminent device failure.

CMOS-based Ion-Sensitive Field Effect Transistors (ISFETs) are used to measure a given media’s pH. CMOS-based ISFETs, in contrast to traditional glass electrode-based pH meters, are compact and consume lower power. However, ISFETs suffer from a mismatch in their output current due to the variations in CMOS fabrication and post-fabrication insulation steps. This mismatch can significantly impact the accuracy of the pH measurements. This work presents a Floating-Gate (FG) to reduce the mismatch in the ISFETs. By employing FG-based ISFET, the study effectively tunes its threshold voltage to calibrate against the mismatch. Fowler-Nordheim tunneling removes the charge from the floating node, which effectively increases the threshold voltage. In contrast, hot-electron injection is used to program the charge onto the FG node, decreasing the threshold voltage. The work experimentally demonstrates the programming of FG-based ISFETs by fabricating them in 0.5μm CMOS process. Moreover, the work characterizes different biasing schemes to program the FG-based ISFETs efficiently.

Machine learning and Artificial Intelligence (AI) are national priority areas for research, education and workforce development. This work in progress paper describes a Research Experiences for Teachers program in sensors and machine learning launched in the summer of 2020. Motivated by national AI workforce needs, we designed a program that engaged high school teachers from STEM fields in machine learning research. In 2020, the program focused on AI algorithms for solar energy systems. Because of the COVID-19 conditions, the research experience was virtual and ran with a smaller teacher group than originally planned. The program included development of training content, algorithm and software training, research in solar energy monitoring, development of research reports and lesson plans, research presentations, and assessment. The assessment of the program included surveys, interviews, presentation observations, and follow-up in high school content delivery.

This article focuses on designing an energy-efficient image recognition system for marine monitoring. One of the main challenges of an underwater imaging system is the strict power consumption constraints due to the limited on-site resources. Considering the need for continuous operation in different water turbidity levels and background illumination conditions, an energy-efficient approach is needed for the effective utilization of the resources. In this work, we propose a recognition framework that will adaptively adjust the system parameters, such as camera frame rate and LED illumination level, based on the environmental conditions to optimize the energy consumption while ensuring a high recognition accuracy. The first part of the proposed decision system contains the convolutional neural network (CNN)-based animal recognition block which is used for obtaining the confidence level for a single frame. The second part is the adaptive decision block that dynamically changes the system parameters and combines the results of the recognition block for multiple frames based on the environmental conditions. In our experiments, we have used nearly 8000 underwater images for training and testing the single frame recognition block and used nearly 200 different video sequences for training and testing the adaptive decision block. Based on measurements of a hardware framework composed of a Raspberry Pi 3 Model B, a Pi NoIR Camera v2.1, and 850 nm LEDs, the proposed system achieves up to 92.7% energy savings with a comparable recognition performance by dynamically changing the frame rate and emitted light intensity based on water turbidity and background illumination level.

While Volatile Organic Compounds (VOC) and ammonia have a place in our daily lives, their leakage into the environment is harmful to human health. In order to prevent and detect gaseous leaks of harmful VOCs, a cyber-physical system (CPS) comprised of ordinary people or first responders is proposed. This CPS uses small, low-cost sensors coupled to smart phones or mobile devices with the necessary computation and communication capabilities. The efficacy of such a CPS hinges on its ability to address technical challenges stemming from the fact that identically produced sensors may produce different results under the same conditions due to sensor drift, noise, or resolution errors. The proposed system makes use of time-varying signals produced by sensors to detect gas leaks. Sensors sample the gas vapor level in a continuous manner and time-varying sensor data is processed using deep neural networks. One of the neural networks (NN) is an energy efficient Additive Neural Network (AddNet) which can be implemented in host devices. The second NN is the discriminator of a GAN and the third a regular convolutional NN. AddNet produces comparable VOC gas leak detection results to regular convolutional networks while reducing area requirements by two thirds.

Arizona State University (ASU) established an NSF Research Experiences for Undergraduates (REU) site to embed students in research projects related to integrated sensor and signal processing systems. The program includes both sensor hardware and algorithm/software design for a variety of applications including health monitoring. The site was funded in February 2017 and the Co-PIs recruited nine students from different universities and community colleges to spend the summer of 2017 in research laboratories at ASU. The program included structured training with modules in sensor design, signal processing, and machine learning. Cross-cutting training included research ethics, IEEE manuscript development, and building presentation skills. Nine undergraduate research projects were launched and the program went through an assessment by an independent evaluator. This paper describes the REU activities, modules, training, projects, and their assessment.

This Live Demonstration shows a compact, low-cost, disposable fluorescence detection platform, using an LFA test strip or sample cartridge, with up to a 1000-fold enhancement in sensitivity over visual colorimetric readouts. The system achieves a high sensitivity by combining low-cost discrete electronic components with optical interference filters. A sample measurement takes less than a minute, trading time for accuracy by using a charge integration amplifier. This system is capable of detecting HPV in human sera with a sensitivity of 10 pg/mL [1].

We present a low-cost, portable electrochemical analysis system using impedimetric measurements rather than more commonly used potentiometric techniques. The presented impedance spectroscopy (impedimetric) technique uses a small perturbation to obtain a linearized response without affecting the composition of the sample as opposed to cyclic voltammetry (potentiometric) which repeatedly reduces and oxidizes the sample. The presented system consists of an Arduino UNO microcontroller, a CMOS digital-to-analog converter (DAC) chip, a potentiostat, and data acquisition (DAQ) hardware. The Arduino UNO is loaded with a program we have written to generate sinusoidal excitation signals in conjunction with the CMOS DAC chip. The CMOS chip fabricated in a standard 0.5 micron CMOS process contains a DAC with a unity gain buffer circuit. The potentiostat is capable of carrying out impedance measurements with both the Randles model and a potassium chloride (KCl) solution. We describe a behavioral model of the op-amp used in the potentiostat using Verilog-A hardware description language. Verilog-A simulations reduced the error relative to standard Cadence simulations and compare well with known Randles RC values giving an error less than 1%. The impedance measurements for a KCl electrochemical solution were performed with our system and a commercial electrochemical workstation (Gamry) giving a relative error up to 16% for lower frequencies (below 37 Hz) and up to 31% at higher frequencies.

Spike timing dependent plasticity (STDP) is an important neural process that enables biological neural networks to learn by strengthening or weakening synaptic connections between neurons. This work presents simulation results and post-silicon experimental data that demonstrate for the first time the possibility of tuning the on state resistance of a type of emerging resistive memory device known as conductive bridge random access memory (CBRAM) in accordance with the biological STDP rule for neuromorphic applications. STDP behavior is demonstrated for CBRAM devices integrated with CMOS spiking neuron circuitry through back end of line post-processing for different initial resistance values and spike durations.

We demonstrate a sun tracking sensor fabricated in the MultiMEMS SensoNor process. The sensor uses a quadrant photodetector to determine the position of a projected shadow. Differential measurement of pairs of photodiodes allows simultaneous dual-axis readings. The sensing structure was manufactured in the commercial MultiMEMS process, so it can be easily reproduced in large quantities. The device was tested from -45° to +45° to verify performance. Results show a linear correlation between a mapped output and angle of incident light with an R2value of 0.995. The small size, robust structure, and minimal post-processing make it well-suited for sun tracking applications.

This paper presents a floating gate Ion Sensitive Field Effect Transistor (ISFET) to monitor the activity of breast cancer cells. We use an ISFET to monitor the change in pH of the cell culture media and to observe the apoptosis of the breast cancer cells when treated with staurosporine. Since ISFETs suffer from inherent mismatch and drift in the threshold voltage, predominantly caused due to accumulation of ions on the surface of the gate, we have integrated a floating gate ISFET to calibrate the device. Floating gate ISFETs have been used to program the threshold voltage of the device either by hot electron injection, Fowler-Nordheim tunneling, and UV to remove charges. In this work we use hot electron injection to precisely program the device and tunneling as a global erase. This enables us to precisely record the changes in pH. These floating gate devices have been fabricated in 0.5 µm CMOS process.

People rarely put in their papers the things that didn't work, the mistakes they made, and how they found out what went wrong. Such confessions can help others learn how to avoid similar mistakes. Twenty-six confessions were collected to form the bulk of this paper. Themes that arise are errors that result from not understanding the limitations of simulation tools in modeling physical reality, chip verification errors that result from lack of clear communication between designers, and projects that are considered in their own isolated environment of technical challenges rather than the broader context of their environment or application.

In this work, we utilize a CMOS active pixel sensor in a fluorescence imaging setup. The ability to sense small light intensity changes on top of a large baseline with spatial resolution at the subcellular scale is required in fluorescence imaging. The CMOS imager presented in [1] is perfect for this application with the ability to resolve fine features coupled with high dynamic range. By using a custom imager with a relay lens we are able to realize a dramatic decrease in device size, cost and complexity of the whole system.

We describe a fully-adjustable dynamic range capacitance sensor circuit implemented with switched capacitors in a 3D process. The dynamic range and sampling frequency are set by the frequency of two clock inputs that control an on-chip four phase non-overlapping clock generation circuit. The chip also contains a bandgap reference, increasing the accuracy of the capacitance measurements. It also has full temperature control capabilities via the PTAT circuit and resistive heating element. The circuits were fully simulated using the Cadence IC 6 simulation tool. The 3D chips were provided by Lincoln Lab at Massachusetts Institute of Technology (MITLL). MITLL fabricate a 3D wafer by bonding 3 stacked wafers each from the IBM silicon over insulator (SOI) 0.15 μm process.

We present work towards expanding the capabilities of microfluidic cell culture devices through the incorporation of microelectronic sensing systems. A system has been fabricated that includes four sensing regions each consisting of a pH sensor, capacitance sensor, and photodiode. We have demonstrated the incorporation of feedback control systems using computer controlled microfluidics for a pH sensor. Preliminary data for photodiode characterization, pH detection using ISFETs, and capacitance sensing of cell density is presented. Results from this work will be used towards creating a single system that can function as a cell culture platform with programmable control over all of these variables.

We have designed and tested two amplification circuits for measuring HL-1 cardiac action potentials, single-ended and differential. We describe the improvements in performance resulting from the differential configuration. These improvements are especially important considering the cell culture's aqueous environment that experiences DC drift. In addition we have designed and fabricated a 4×4 gold microelectrode array and cultured HL-1 cells on the electrodes. The array is connected to our circuit through a custom made Pogo pin jig to allow for easy measurements from the electrodes while cells are being cultured. This setup allowed us to obtain initial results for modeling the transfer function of the cell culture as a passive device network. Testing has confirmed the circuits are capable of filtering low frequency signals including DC drift and frequencies above 5 kHz. The amplification was achieved through two gain stages with a total gain of 60 dB for the single-ended and 46 dB for the differential circuits.

We present a system comprised of a PDMS microfluidic device and an injection system for dilution of DNA derived from whole blood to a single strand. We discuss two micro fluidic architectures for ultra-high ratio dilution (1:1000 or more) as well as the necessity to move beyond previously reported dilution architectures. We describe a custom injection system used both for fabrication and operation of the system. The system is operated by placing syringes, one filled with water and one filled with DNA extracted from whole blood, into the injection system. This system fixes and plunges the syringes simultaneously to introduce both solutions into the microfluidic channels that produce the ultra-high ratio dilutions. The most significant constraint on the design is that the system is designed for fabrication, characterization and use in a clinical setting.

We report on the design fabrication and testing of a wide range transconductance amplifier fabricated in the 0.18μm MIT Lincoln Labs 3D SOI-CMOS process. The amplifier is designed to operate in subthreshold and employs self-biased cascode transistors to minimize the bias lines transversing the 3 tiers in the technology.

While there has been a growing emergence of circuit designs for the life sciences, there is an important issue that remains largely unaddressed. Although advanced design techniques have been applied to circuits capable of measuring very small amplitude, high signal to noise ratio signals, with many custom circuit designs for these applications (Harrison and Charles, 2003), there remains a proverbial elephant in the room. The systems used to measure these signals provide no means of accurate thermal control. This is especially surprising considering the huge dependance in the behavior of both biological and electrical systems upon temperature. In fact, the vast majority of electrical cellular assays are performed on dying cells! We present a systematic method of incorporating a high-accuracy, closed-loop thermal feedback system into hybrid systems for the life sciences. We introduce a thermal stabilization chip containing a heater and PTAT temperature sensor including the heater control circuit. We then provide a description of the PID control loop. This is followed by both computational data via finite element analysis and empirical data that assesses the thermal performance of the chip. Finally, we demonstrate the advantages of the system with comparative results of cell culture.

We present hybrid silicon/silicone microsystem for closed-loop temperature control with applications in the life sciences. The system architecture includes an integrated CMOS die for localized thermal cycling and a disposable PDMS microfluidic structure for aseptic fluidic manipulation. Chip design, experimental results and finite element analysis are presented. This work is based on criteria for a clinical diagnostics system comprised of disposable microfluidics for use in tandem with CMOS electronics.

We discuss the design, fabrication and testing of a hybrid microsystem for stand-alone cell culture and incubation. The micro-incubator is engineered through the integration of silicon CMOS die for the heater and temperature sensor, with multilayer silicone PDMS (polydimethylsiloxane) structures namely, fluidic channels and a 4 mm diameter, 30muL, culture well. A 25 micron thick PDMS membrane covers the top of the culture well, acting as barrier to contaminants while allowing the cells to exchange gases with the ambient environment. The packaging for the microsystem includes a flexible polyimide electronic ribbon cable and four fluidic ports that provide external interfaces to electrical energy, closed loop sensing and electronic control as well as solid and liquid supplies. The complete structure has a size of (2.5 times 2.5 times 0.6 cm3). We have employed the device to successfully culture BHK-21 cells autonomously over a sixty hour period in ambient environment