User:Dvd8719/sandbox

Reinforcement learning

$$h_\theta(x) = \theta_0 + \sum_{i=1}^d \theta_i x_i = \sum_{i=0}^d \theta_i x_i$$

$$D = \Big[C \times \text{VAF}(V_g, V_d) \exp \big(-\frac{E_a}{k_b T}\big) t \Big]^n$$$$\log(D) = n \Big[\log(C) + \log[\text{VAF}(V_g, V_d)] -\frac{E_a}{k_b T} + \log(t) \Big]$$$$\frac{\log(D)}{n} = \log(C) + \log[\text{VAF}(V_g, V_d)] -\frac{E_a}{k_b T} + \log(t)$$

Assume


 * $$\text{VAF}(V_g, V_d) = \exp(-\lambda V_g)$$
 * Two measurements $$(D_0, V_{d_0}, V_{g_0}, T_0, t_0)$$ and $$(D_1, V_{d_1}, V_{g_1}, T_1, t_1)$$

Therefore we have$$\lambda V_g = \log(C) -\frac{E_a}{k_b T} + \log(t) - \frac{\log(D)}{n}$$$$\lambda = \frac{1}{V_{g_0} - V_{g_1}} \Bigg[ \frac{E_a}{k_b}\Big(\frac{1}{T_0}-\frac{1}{T_1}\Big) + \Big[\log(t_0)-\log(t_1)\Big] + \frac{\log(D_1)-\log(D_0)}{n} \Bigg]$$$$\lambda_{\text{deg space}} = \frac{1}{n} \times \frac{\log(D_1)-\log(D_0)}{V_{g_0}-V_{g_1}}$$

$$\lambda_{\text{TTF space}} = \frac{\log(t_0)-\log(t_1)}{V_{g_0}-V_{g_1}}$$