sin波输入计算ADC的INLDNL的脚本

Sin波信号用于模拟实际信号的典型特性，在分析模数转换器(ADC)的信噪比(INL，Integral Non-Linearity)和 differential non-linearity (DNL)时非常常见。INL通常是指连续量化误差的累计效应，而DNL则是指单次转换的量化误差。

编写一个用于计算ADC INL和DNL的脚本通常涉及以下步骤，这里我会给出一个Python伪代码示例：

import numpy as np

# 定义理想sin波函数和采样频率
def ideal_sin_wave(freq, sample_rate, duration):
    time = np.linspace(0, duration, sample_rate)
    return np.sin(2 * np.pi * freq * time)

# 模拟ADC转换
def adc_conversion(sin_signal, resolution):
    quantized_values = np.round(sin_signal / (2**resolution - 1)) * (2**resolution - 1)
    return quantized_values

# 计算INL
def inl(adc_samples, ref_voltage):
    continuous_error = adc_samples - ref_voltage
    integrated_error = np.cumsum(continuous_error)
    return integrated_error

# 计算DNL
def dnl(adc_samples, max_value):
    single_shot_errors = adc_samples - max_value
    return single_shot_errors

# 使用示例参数
freq = 1kHz
sample_rate = 100ksps
duration = 1s
resolution = 12bit
ideal_signal = ideal_sin_wave(freq, sample_rate, duration)
adc_samples = adc_conversion(ideal_signal, resolution)

inl_result = inl(adc_samples, ideal_signal.max())
dnl_result = dnl(adc_samples, 2**resolution - 1)

print(f"INL: {inl_result}")
print(f"DNL: {dnl_result}")