Support Vector Machine Aided Semi-intelligent Performance Enhancement of CMOS Gate-voltage Bootstrapped Sampling Switch

As a forefront core functional module, a bootstrapped sampling switch (BSS) built-in an analog-digital converter (ADC) contributes to realize a high-precision signal sampling in bioelectrical sensing systems. A novel algorithm-based automatic approach to guide the optimal design of a high-performance bootstrapped sampling switch is proposed. Within the first manual topology optimization, a complementary sampling transfer-gate is designed to suppress the clock feed-through effect, and a dynamic body bias control module and an improved bootstrapped closed-loop-path are constructed to effectively improve the linearity. In the secondary core algorithm-based performance solving stage, a support vector regression (SVR) machine is adopted to further explore the best design tradeoff between dynamic noise feature and power dissipation according to the model-training-based optimal solution of the design parameters. SMIC 180 nm/1.8 V standard CMOS technology is employed to implement the front/back-end design of the proposed BSS circuit, and pre-/post-layout simulations for feature verification are performed. Final experimental results show that, targeting a test benchmark signal of 100 Hz and 1.2 V p-p in 51.2 kHz sampling frequency, after mixed-optimization-based ENOB and SNR can be reached up to 12.969 bits and 103.046 dB, respectively. Similar to the other two key dynamic performance indexes, SFDR and THD are also improved to 70.905 dBc and -69.508 dB, respectively. In the design case comparison, with a higher power supply voltage of 1.8 V and kHz-order frequency, the average power consumption is only approximately 0.188 μ W. These feature results demonstrated the significant effectiveness of the proposed SVR algorithm aided artificial optimization approach on bootstrapped sampling switch design, and the comprehensive specification of switches can meet the demand of the specific application of highly accurate human bioelectrical signal sampling.