Artificial neural network electronic nose for volatile organic compounds

Advanced microsystems that include, sensors, interface-circuits, and pattern-recognition integrated monolithically or in a hybrid module are needed for civilian, military, and space applications. These include: automotive, medical applications, environmental engineering, and manufacturing automation. ASICs with Artificial Neural Networks (ANN) are considered in this paper, with the objective of recognizing air-borne volatile organic compounds, especially alcohols, ethers, esters, halocarbons, NH₃, NO₂, and other warfare agent simulants. The ASIC inputs are connected to the outputs from array-distributed sensors which measure three-features for identifying each of four chemicals. A Specialized Reinforcement Neural Network (RNN) learning approach is chosen for the chemicals classification problem. Hardware implementation of the RNN is presented for 2 μm CMOS process, MOSIS chip. Design implementation and evaluation are also presented