Combined foreground calibration#

This experiment places both errors in the same conversion path and compares uncorrected output, each correction alone, and their frozen cascade. It reuses existing public error/model functions rather than introducing a new cubic model. It is foreground behavioral validation, not a measured-chip reproduction or a background adaptive algorithm.

Run and replay#

From the copied examples directory:

python 12_foreground/exp_cal11_combined_foreground.py --output output/combined_foreground
python 12_foreground/audit_combined_foreground.py --directory output/combined_foreground --output output/combined_audit.json

Defaults: binary and redundant SAR, seeds 11/22/33, 64 training records, eight validation records, 8192 samples per record, fitting stride eight, and at most 1000 iterations for each MLP. A small functional run can use --records 8 --validation-records 2 --size 2048 --iterations 3 --seeds 11. Such a run is not an acceptance-performance result. Nonempty output directories are rejected.

Physical path and reused interfaces#

normal acquisition:
input x -> smooth analog front end f(x) -> mismatched SAR decisions b
        -> nominal or calibrated digital reconstruction -> optional inverse map

dedicated bit calibration:
calibration sine -> injection at SAR input (front end bypassed) -> decisions b

Block	Repository implementation and settings
Smooth front end	`ADC_Signal_Generator.apply_static_nonlinearity(input_signal=x, k2=.015, k3=.04)`, DC=0: u=x+.015x²+.04x³
SAR conversion	Public `sar_convert`, range [-1,1], actual MSB weight 1% above nominal
Capacitor structure	`sar_ideal_weights(14)` or `sar_ideal_weights(14, redundant_bit=2)`; the latter repeats the third largest capacitor, yielding 15 decisions
Nominal output	Public `sar_reconstruct` plus half a nominal LSB
Reference monitor	Independent synchronized 16-bit monitor, 2e-4 RMS noise; existing `apply_quantization_noise` plus half-LSB midpoint; known-frequency projection
Sample noise	Deterministic independent RNG, 1e-5 RMS at SAR input

The repeated-weight normalization is sum(weights)+one LSB; the two architectures therefore have different normalized LSBs. The same deterministic +1% MSB error is used for every seed. Seeds vary stimulus, noise and training initialization, not physical mismatch. No comparator noise, memory, reference ripple, timing skew or PVT drift is included. Unlike exp_cal03, this experiment has unit linear front-end gain because it directly uses the original static-error API.

All normal training, validation and test captures pass through both the smooth front end and the mismatched SAR. Only the separately recorded bit-calibration capture bypasses the smooth front end. True input and actual weights enter only the simulated plant and evaluator, never the fitters.

Identification sequence and observation cost#

Acquire a separate coherent 0.9-peak sine at the SAR input, bypassing the nonlinear front end. Its frequency and noise seed are separate from end-to-end training, validation and test captures.
Call public calibrate_weight_sine with raw decisions, known frequency, force_search=False, harmonic_order=1. Scale its unit-sine output using the measured reference-monitor sine amplitude, not simulator truth. Include the monitor’s fitted DC. Save and freeze the resulting weights and offset.
Acquire mixed single/two-tone records through the complete device. Construct both nominal output y and frozen bit-corrected output z from the same decisions.
Fit separate scalar inverse models y→r and z→r using the same aligned external reference r, rows, degree/network size and optimizer budget. Validation selects each MLP’s iterate using only reference MSE. No test-driven selection occurs.

The primary bit stage implements z=offset+sum(w_i*b_i). The polynomial then evaluates x_hat=P5(z). The MLP evaluates a frozen affine baseline plus a learned residual, exactly as the public fitter specifies. This is sequential calibration and cascaded correction, not joint gradient optimization of weights and network.

The bit paths require one extra full record (8192 points at defaults) and access to internal decisions plus a SAR-input calibration injection/bypass path. The direct polynomial/MLP paths receive the nominal scalar output only. All nonlinear fitters share the same end-to-end records, but this is not an equal total-observation or equal hardware-access comparison.

The bypass is a material applicability condition: it must leave the normal SAR weights/reference behavior representative. An ADC without this calibration access cannot be assumed to implement the protocol. If the nonlinearity resides inside the converter or residue amplifier, stage-specific observations/calibration modes or a joint identifiable model may be needed instead.

Methods and controls#

Saved method	Frozen operation
`raw`	Nominal SAR reconstruction
`bits_only`	Dedicated-calibration weights, no smooth inverse
`poly_only`	Fifth-degree polynomial of nominal scalar output
`mlp_only`	MLP of nominal scalar output
`bits_poly`	Dedicated bit weights, then fifth-degree polynomial
`bits_mlp`	Dedicated bit weights, then MLP
`end_to_end_bits`	Control: affine fit of current decisions directly to complete-chain reference, without bypass calibration
`end_to_end_bits_poly`	Control: freeze those end-to-end weights, then fit a polynomial
`known_weights_inverse`	Evaluator-only actual-weight reconstruction followed by exact inverse of the known cubic

The no-bypass control tests whether independently fitting both stages to total error is equivalent to isolating bit error first. Its weights can absorb smooth distortion and need not represent physical capacitor weights. It is not the same calibration protocol as the primary cascade.

The known-weight inverse is a diagnostic control, not an optimal statistical bound. Strict binary conversion gaps and quantization remain. The exact cubic coefficients are used only in this labeled evaluator path.

Model size, training and measurements#

This device has static errors, so polynomial and MLP use one current scalar input, no history. Each polynomial has six coefficients. Each MLP has 16 tanh hidden units, 49 network parameters plus two frozen affine coefficients. This differs from the earlier four-input history MLP; do not interpret the comparison as an isolated change to training iterations on the previous network.

Training has 65,536 selected rows at defaults; validation has 8192. Peak amplitude budgets span 0.05–0.90 and tone frequencies approximately 0.02–0.45 fs. Half the records are single-tone and half two-tone. All feature standardization uses training statistics. Polynomial ridge=1e-10; MLP normalized-residual L2=1e-6. Report optimizer status; reaching the iteration limit does not mean convergence.

Every case evaluates the same 15 single-tone amplitude/frequency combinations as exp_cal10, plus two-, three- and five-tone records. The single-tone set has amplitudes 0.1/0.5/0.85 and frequencies approximately 0.031/0.113/0.239/0.371/0.467 fs; the last frequency is outside the training band. All test predictions are from JSON-reloaded frozen models. No test gain, phase or offset adjustment is made.

Single-tone SNDR includes all unwanted non-DC power, including harmonics; SFDR uses the largest unwanted spectral bin. Coherent rectangular-window FFTs span the full Nyquist band. Public analyze_spectrum cross-checks single-tone results. Multi-tone ratios follow FOREGROUND_BITWEIGHTS.md and are labeled separately, with single-tone SNDR/SFDR fields empty. Absolute error SNR remains available. Summaries average conditions within each seed, then report seed mean/sample SD.

Evidence and limits#

Joint treatment of capacitor/gain error and smooth nonlinearity has measured precedents. Verma and Razavi’s JSSC 2009 paper, sections II–III and V, combines programmable stage coefficients with third-order inverse correction in a 90-nm, 10-bit, 500-MS/s prototype. Its architecture and calibration sequence differ from this SAR-input bypass experiment.

The publisher’s public abstract/highlights for Li et al., Integration 2026, describe joint PGA second-harmonic and SAR bit-weight calibration on a 180-nm chip. Only the publicly available description was checked; its full algorithm was not reproduced here. Neither source validates this repository’s exact model.

Applicability is limited to a stable, invertible smooth front end, a SAR with observable decisions and suitable calibration access, characterized aligned reference measurements, and sufficient conversion redundancy/headroom. This experiment does not show recovery from clipping or arbitrary missing-code gaps, nor does it cover long memory, random jitter, drift or uncharacterized reference errors. It provides no fixed-point, area/power or real-time throughput result.

Artifacts include complete model dictionaries, capture manifests, training histories, per-condition metrics, source snapshots and hashes. The audit replays all test captures, verifies split separation and validation selection, confirms that models remain frozen, and independently recomputes ratios with a full FFT.