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Strategies of miniaturised reference electrodes integrated in a silicon-based „one chip“ pH sensor
(2003)
Miniaturised reference electrodes for field-effect sensors compatible to silicon chip technology
(2005)
In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem.
Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ.
Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible.
In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production.
Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.
Macroporous silicon has been etched from n-type Si, using a vertical etching cell where no rear side contact on the silicon wafer is necessary. The resulting macropores have been characterised by means of Scanning Electron Microscopy (SEM). After etching, SiO₂ was thermally grown on the top of the porous silicon as an insulating layer and Si₃N₄ was deposited by means of Low Pressure Chemical Vapour Deposition (LPCVD) as transducer material to fabricate a capacitive pH sensor. In order to prepare porous biosensors, the enzyme penicillinase has been additionally immobilised inside the porous structure. Electrochemical measurements of the pH sensor and the biosensor with an Electrolyte/Insulator/Semiconductor (EIS) structure have been performed in the Capacitance/Voltage (C/V) and Constant capacitance (ConCap) mode.