A CMOS Mixed Mode Non-Linear Processing Unit for Adaptive Sensor Conditioning in Portable Smart Systems

This paper presents the architecture of a novel non-linear digitally programmable analog unit for sensor output conditioning in battery-operated smart systems. Designed in an 180nm 1.8V standard CMOS technology, by properly setting the 6-bit registers in the arithmetic unit, the voltage inputs are weighted before being processed by a non-linear circuit. Thus, a processing system consisting of a set of these devices suitably tuned and interconnected can be applied to condition a non-linear sensor, improving its behavior both in linearity and operating range, while reducing the effects of cross sensitivity. The robustness of the digital weight tuning is tested simulating a chip-on-the-loop training using a Levenberg-Marquardt-based algorithm. Electric simulations of the proposed unit and the results of its application in a complete neural network-based processing system to improve the linear operating range of a thermistor are presented

Microelectronic cmos implementation of a machine learning technique for sensor calibration

An integrated machine-learning based adaptive circuit for sensor calibration implemented in standard 0.18μm CMOS technology with 1.8V power supply is presented in this paper. In addition to linearizing the device response, the proposed system is also capable to correct offset and gain errors. The building blocks conforming the adaptive system are designed and experimentally characterized to generate numerical high-level models which are used to verify the proper performance of each analog block within a defined multilayer perceptron architecture. The network weights, obtained from the learning phase, are stored in a microcontroller EEPROM memory, and then loaded into each of the registers of the proposed integrated prototype. In order to verify the proposed system performance, the non-linear characteristic of a thermistor is compensated as an application example, achieving a relative error er below 3% within an input span of 130°C, which is almost 6 times less than the uncorrected response. The power consumption of the whole system is 1.4mW and it has an active area of 0.86mm 2 . The digital programmability of the network weights provides flexibility when a sensor change is required