An integrated machine-learning based adaptive circuit for sensor calibration implemented in standard 0.18 μm0.18~\mu \text{m} 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 ere_{r} below 3% within an input span of 130∘C130^\circ 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 0.86mm2. The digital programmability of the network weights provides flexibility when a sensor change is required.

中文翻译：

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用于传感器校准的机器学习技术的微电子 CMOS 实现


本文提出了一种基于机器学习的集成自适应传感器校准电路，采用标准 0.18 μm0.18~\mu \text{m} CMOS 技术和 1.8V 电源实现。除了线性化器件响应之外，所提出的系统还能够纠正偏移和增益误差。符合自适应系统的构建块经过设计和实验表征，以生成数值高级模型，该模型用于验证定义的多层感知器架构中每个模拟块的正确性能。从学习阶段获得的网络权重存储在微控制器 EEPROM 存储器中，然后加载到所提出的集成原型的每个寄存器中。为了验证所提出的系统性能，以补偿热敏电阻的非线性特性作为应用示例，在 130∘C130^\circ C 的输入范围内实现相对误差 ere_{r} 低于 3%，即几乎比未校正响应少 6 倍。整个系统的功耗为1.4mW，有效面积为0.86mm2。当需要更换传感器时，网络权重的数字可编程性提供了灵活性。