• Biotechnology and Bioprocess Engineering:BBE
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• v.8 no.4
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• pp.252-256
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• 2003

In this paper we describe the status of a silicon-based microelectrode for neural recording and an advanced neural interface. We have developed a silicon neural probe, using a combination of plasma and wet etching techniques. This process enables the probe thickness to be controlled precisely. To enhance the CMOS compatibility in the fabrication process, we investigated the feasibility of the site material of the doped polycrystalline silicon with small grains of around 50 nm in size. This silicon electrode demonstrated a favorable performance with respect to impedance spectra, surface topography and acute neural recording. These results showed that the silicon neural probe can be used as an advanced microelectrode for neurological applications.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2002.11a
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• pp.366-369
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• 2002

Two artificial neural networks (ANN) are used to model the electric coupling probe in the combline resonators. One is used to analyze and synthesize the electric probe, and the other is used to correct errors between the results of the analysis and the synthesis ANNs and the fabrication results. The ANNs for the analysis and the synthesis of the electric probe are trained using the physical dimensions of the electric probe and the corresponding coupling bandwidth which is obtained using the finite element method. The ANNs for the error correction are trained using a very small set of the measurement results. Once trained, the ANN models provide the correct result approaching the accuracy of the measurement. The results from the ANN models show fairly good agreement with those of the measurement and they can be used as good initial design values.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1138-1143
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• 2015

Effects of fabrication process variations on impedance of microelectrodes integrated on a neural probe were examined through equivalent circuit modeling and SPICE simulation. Process variation and the corresponding range were estimated based on experimental data. The modeling results illustrate that the process variation induced by metal etching process was the dominant factor in impedance variation. We also demonstrate that the effect of process variation is frequency dependent. Another process variation that was examined in this work was the thickness variation induced by deposition process. The modeling results indicate that the effect of thickness variation on impedance is negligible. This work provides a means to predict the variations in impedance values of microelectrodes on neural probe due to different process variations.

• Journal of Sensor Science and Technology
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• v.20 no.1
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• pp.19-24
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• 2011

Brain disorders such as epilepsy is a condition that affects an estimated 2.7 million Americans, 50,000,000 worldwide, approximately 200,000 new cases of epilepsy are diagnosed each year. Of the major chronic medical conditions, epilepsy is among the least understood. Scientists are conducting research to determine appropriate treatments, such as the use of drugs, vagus nerve stimulation, brain stimulation, and Peltier chip-based focal cooling. However, brain stimulation and Peltier chip-based stimulation processes cannot effectively stop seizures. This paper presents simulation of a novel heat enchanger network(HEN) technique designed to stop seizures by using a neural cooling probe to stop focal and spontaneous seizures by cooling the brain. The designed probe was composed of a U-shaped tube through which cold fluid flowed in order to reduce the temperature of the brain. The simulation results demonstrated that the neural probe could cool a 7 $mm^2$ area of the brain when the fluid was flowing atb a velocity of 0.55 m/s. It also showed that the neural cooling probe required 23 % less energy to produce cooling when compared to the Peltier chip-based cooling system.

• Journal of the Microelectronics and Packaging Society
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• v.16 no.2
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• pp.33-42
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• 2009

A polymer-based flexible neural probe was fabricated for monitoring of neural activities from a brain. To improve the insertion stiffness, a 5 ${\mu}m$ thick biocompatible Au layer was electroplated between the top and bottom polymer layers. The developed neural probe penetrated a gel whose elastic modulus is similar to that of a live brain tissue without any fracture, To minimize mechanical residual stress and bending from the probe, two new methods were employed: (1) use of a thermal annealing process after completing the device and (2) incorporation of multiple different layers to compensate the residual stress between top and bottom layers. Mechanical bending around the probe tip was clearly removed after employing the two processes. In electrical test, the developed probe showed a proper impedance value to record neural signals from a brain and the result remained the same for 72 hours. In simple in-vitro probe characterization, the probe showed a great removal of residual stress and an excellent recording performance. The in-vitro recording results did not change even after 1 week, suggesting that this electrode has the potential for great recording from neuron firing and long-term implant performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.11
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• pp.1998-2003
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• 2007

A flexible but implantable polyimide-based neural implant was fabricated for reliable and stable long-term monitoring of neural activities from brain. The developed neural implant provides 3-dimensional (3D) $3{\times}3$ structure, avoids any hand handling, and makes the insertion more efficient and reliable. Any film curvature caused by residual stress was not observed in the electrode. The 3D flexible polyimide electrode penetrated a dense gel whose stiffness is close to live brain tissue, because a ${\sim}1{\mu}m$ thick nickel was electroplated along the edge of the shank in order to improve the stiffness. The recording sites were positioned at both side of the shank to increase the probability of recording neural signals from a target volume of tissue. Impedance remained stable over 72 hours because of extremely low moisture uptake in the polyimide dielectric layers. At electrical recording test in vitro, the fabricated electrode showed excellent recording performance, suggesting that this electrode has the potential for great recording from neuron firing and long-term implant performance.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.64-73
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• 1998

This paper is concerned with defects detection and evaluation of heat affected zone (HAZ) in austenitic stainless steel type 304 by ultrasonic wave and neural network. In experiment, the reflected ultrasonic defect signals from artificial defects (side hole, vertical hole, notch) of HAZ appears as beam distance of prove-defect, distance of probe-surface, depth of defect-surface on CRT. For defect classification simulation, neural network system was organized using total results of ultrasonic experiment. The organized neural network system was learned with the accuracy of 99%. Also it could be classified with the accuracy of 80% in side hole, and 100% in vertical hole, 90% in notch about ultrasonic pattern recognition. Simulation results of neural network agree fairly well with results of ultrasonic experiment. Thus were think that the constructed system (ultrasonic wave - neural network) in this work is useful for defects dection and classification such as holes and notches in HAZ of austenitic stainless steel 304.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1175-1182
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• 2000

Diffraction systematically causes error in acoustic measurements. Most probes are designed to reduce this phenomenon. On the contrary, this paper proposes a spherical probe a] lowing acoustic inten sity measurements in three dimensions to be made, which creates a diffracted field that is well-defined, thanks to analytic solution of diffraction phenomena. Six microphones are distributed on the surface of the sphere along three rectangular axes. Its measurement technique is not based on finite difference approximation, as is the case for the ID probe but on the analytic solution of diffraction phenomena. In fact, the success of sound source identification depends on the inverse models used to estimate inverse diffraction phenomena, which has nonlinear properties. In this paper, we propose the concept of nonlinear inverse diffraction modeling using a neural network and the idea of 3 dimensional sound source identification with better performances. A number of computer simulations are carried out in order to demonstrate the diffraction phenomena under various angles. Simulations for the inverse modeling of diffraction phenomena have been successfully conducted in showing the superiority of the neural network.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.147-148
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• 1998

In this paper, we developed the process for depth-probe type silicon microelectrode arrays. The process consists of four mask steps only. The steps are for defining sites, windows, and for shaping probe using plasma etch from above, and for shaping using wet etch from below, respectively. The probe thickness is controlled by dry etching, not by impurity diffusion. We used gold electrodes with a triple dielectric system consisting of oxide/nitride/oxide. The shank of the probe taper from 200um to tens of urn tip and has 30 um thickness.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.209-210
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• 1998

Noise characteristics of preamplifier, the most essential part of on-chip signal processing circuitry for the active semiconductor neural probe, is the important factor determining the overall signal-to-noise-ratio (SNR). We present a systematic design method for the optimization of SNR, based on the spectral characteristics of the electrode, circuit noise and extracelluar action potential. Analytical expression is derived to calculate total output noise power. Output SNR of 2-stage CMOS preamplifier is tailored to meet the given specification while the layout area is minimized.