A Knowledge-Based Approach to Online Fault Diagnosis of FET Biosensors

Real-time diagnosis of insulator-semiconductor field-effect transistor (ISFET)-based biosensor systems aims at promptly correcting errors caused by insufficient function; insufficiency is judged by the operational behavior of the sensor, i.e., the data that it produces. Ultimately, a complete failure of the system (i.e., a “dead” sensor) should easily be recognized. Much more difficult is the recognition of a gradual malfunction of this complex system, which may be attributed to faults or failures in one or more of its subsystems. Evidently, the identification of the possible fault modes and their symptoms requires in-depth knowledge of sensor´s design and operation, both from the biochemical and electrical/electronic points of view, along with tackling uncertain, incomplete, or imprecise information. In this paper, a novel real-time diagnostic expert scheme for field-effect transistor (FET)-based biosensing is proposed. This paper 1) investigates the causes of sensor misfunction by means of fault tree analysis (FTA) relying on fuzzy reasoning to account for uncertainty and 2) proposes a computer-aided method for diagnosing biosensor failure during operation through an algorithmic procedure that is based on a nested loop mechanism. The tree (dendritic) structure (built using the information provided by the biosensor components and their intrarelations/interrelations on a surface- and a deep-knowledge level) serves as the knowledge base (KB), and the fuzzy-rules-based decision mechanism is the inference engine for fault detection and isolation.