(Invited) Hafnia Ferroelectric Device for Semiconductor, Sensor, and Display Applications

2020 ◽  
Vol 98 (7) ◽  
pp. 219-223
Author(s):  
Sanghun Jeon
Keyword(s):  
Semiconductor Sensor

Zinc Oxide Nanorod Electrolyte–Insulator–Semiconductor Sensor for Enhanced 2-methoxyethanol Selectivity

2020 ◽  
pp. 1-1
Author(s):  
Ensaf Mohammed Al-Khalqi ◽  
Muhammad Azmi Abdul Hamid ◽  
Roslinda Shamsudin ◽  
Naif H. Al-Hardan ◽  
Azman Jalar ◽  
...  
Keyword(s):  
Zinc Oxide ◽  
Semiconductor Sensor

Detection of protein kinase using an aptamer on a microchip integrated electrolyte-insulator-semiconductor sensor

2017 ◽  
Vol 248 ◽  
pp. 973-979 ◽  
Author(s):  
Rohit Chand ◽  
Dawoon Han ◽  
Suresh Neethirajan ◽  
Yong-Sang Kim
Keyword(s):  
Protein Kinase ◽  
Semiconductor Sensor

Analysis of Dendrite Images Formed by Electrochemical Migration in a Semiconductor Sensor

2018 ◽  
Vol 916 ◽  
pp. 207-211 ◽  
Author(s):  
Manolo G. Mena
Keyword(s):  
Electrochemical Migration ◽  
Branching Pattern ◽  
Edx Analysis ◽  
Semiconductor Sensor ◽  
Grey Scale ◽  
Sensor Devices ◽  
Silver Dendrites ◽  
Branch Density ◽  
Tin Metal ◽  
Dimension Number

Dendrites were observed in the failure of semiconductor sensor devices. EDX analysis showed that the dendrites grown from bare sensor dice consisted of tin metal. The tin dendrites exhibited massive and dense branches. Dendrites grown from mechanically decapped parts consisted of silver. The silver dendrites exhibited delicate, lace-like structure. Binary and grey scale images of dendrites were analyzed for fractal dimension number and branch density. The tin dendrites had a higher, statistically significant branch density number than silver, due to tin’s more intricate branching pattern. Fractal numbers can be used to differentiate between tin and silver dendrites, even in the absence of EDX analysis equipment.

Adsorption semiconductor sensor for diabetic ketoacidosis diagnosis

1996 ◽  
Vol 36 (1-3) ◽  
pp. 419-421 ◽  
Author(s):  
N. Makisimovich ◽  
V. Vorotyntsev ◽  
N. Nikitina ◽  
O. Kaskevich ◽  
P. Karabun ◽  
...  
Keyword(s):  
Diabetic Ketoacidosis ◽  
Semiconductor Sensor

Metal Oxides Semiconductor Sensors for Odor Classification

2017 ◽  
Vol 6 (3) ◽  
pp. 133
Author(s):  
Nyayu Latifah Husni ◽  
Ade Silvia ◽  
Siti Nurmaini ◽  
Irsyadi Yani
Keyword(s):  
Metal Oxides ◽  
Gas Sensors ◽  
Experimental Test ◽  
Sampling Method ◽  
Performance Test ◽  
Dynamic Experiment ◽  
Semiconductor Sensor ◽  
Surrounding Environment ◽  
Expected Outcome ◽  
Odor Classification

<span>The performance of gas sensor will differ and vary due to the surrounding environment changing, the way of implementation, and the position of the sensors to the source. To reach a good result on gas sensors implementation, a performance test on sensors is needed. The results of the tests are useful for characterizing the properties of the particular material or device. This paper discusses the performances of metal oxides semiconductor (MOS) sensors. The sensors are tested to determine the sensors' time response, sensors' peak duration, sensors' sensitivity, and sensors' stability of the sensor when applied to the various sources at different range. Three sources were used in experimental test, namely: ethanol, methanol, and acetone. The gas sensors characteristics are analyzed in open sampling method in order to see the sensors' sensitivity to the uncertainty disturbances, such as wind. The result shows that metal oxides semiconductor sensor was responsive to the 3 sources not only in static but also dynamic conditions. The expected outcome of this study is to predict the MOS sensors' performance when they are applied in robotic implementation. This performance was considered as the training datasets of the sensor for odor classification in this research. From the experiments, It was got, in dynamic experiment, the senrors has average of precision of 93.8-97%, the accuracy 93.3-96.7%, and the recall 93.3-96.7%. This values indicates that the sensors were selective to the odor they sensed.</span>

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