Design and Implementation of Parametrized Look-Up Tables for Post-Correction of Oversampling Low-Resolution ADCs

We propose a framework for the design, optimization, and implementation of Look-Up Tables (LUTs) used to recover noisy, oversampled, quantized signals given a parametric input model. The LUTs emulate the spectral effects of pre-quantization dithering through an all-digital solution applied after quantization. This methodology decomposes the intractable LUT design problem into four distinct stages, each of which is addressed analytically using a model-driven approach without reliance on training. Three dithering methods are studied to improve spectral purity metrics. Two novel indexing schemes are proposed to limit the LUT memory overhead shown to compress the LUT size by over four orders of magnitude with marginal performance loss. The LUT design is tested with an oversampled noisy sinusoidal input quantized to 3 bits and shown to improve its Spurious-Free Dynamic Range (SFDR) by over 19 dBc with only 324 bytes of memory while maintaining the same 3-bit fixed-point precision at the digital output. This correction can be implemented using two-level combinational logic ensuring ultra-low latency and, hence, suitable for low-resolution wideband devices.