[step:Define the oscillatory kernel distribution by cutoff regularization]
Choose a cutoff function $\rho \in C_c^\infty(\mathbb{R}^n)$ such that $\rho(\xi)=1$ for $|\xi|\le 1$. For $\varepsilon \in (0,1]$, define
\begin{align*}
K_{a,\varepsilon}: \mathbb{R}^n \times \mathbb{R}^n &\to \mathbb{C}
\end{align*}
by
\begin{align*}
K_{a,\varepsilon}(x,y;h) := \frac{1}{(2\pi h)^n}\int_{\mathbb{R}^n} e^{i(x-y)\cdot \xi/h} \rho(\varepsilon \xi)a(x,\xi;h)\, d\mathcal{L}^n(\xi).
\end{align*}
Since $\rho(\varepsilon \xi)a(x,\xi;h)$ is compactly supported in $\xi$ and smooth in $(x,\xi)$, this integral defines a smooth function of $(x,y)$.
For $\Phi \in \mathcal{S}(\mathbb{R}^n_x \times \mathbb{R}^n_y)$, define
\begin{align*}
K_{a,\varepsilon}(\Phi) := \int_{\mathbb{R}^n}\int_{\mathbb{R}^n} K_{a,\varepsilon}(x,y;h)\Phi(x,y)\, d\mathcal{L}^n(y)\, d\mathcal{L}^n(x).
\end{align*}
We next prove that the cutoff limit exists. For $\Phi \in \mathcal{S}(\mathbb{R}^n_x \times \mathbb{R}^n_y)$, define
\begin{align*}
B_\Phi: \mathbb{R}^n_\xi &\to \mathbb{C}
\end{align*}
by
\begin{align*}
B_\Phi(\xi;h) := \int_{\mathbb{R}^n}\int_{\mathbb{R}^n} e^{i(x-y)\cdot \xi/h}a(x,\xi;h)\Phi(x,y)\,d\mathcal{L}^n(y)\,d\mathcal{L}^n(x).
\end{align*}
For each integer $q \geq 0$, integrate by parts in the $x$ variable using
\begin{align*}
e^{i(x-y)\cdot \xi/h} = (1 + |\xi|^2h^{-2})^{-q}(1 - \Delta_x)^q e^{i(x-y)\cdot \xi/h}.
\end{align*}
The transpose of $(1 - \Delta_x)^q$ with respect to $d\mathcal{L}^n(x)$ is itself, and all boundary terms vanish because $\Phi$ is Schwartz in $x$. Thus $B_\Phi(\xi;h)$ is bounded by a finite sum of integrals of derivatives $\partial_x^\mu a(x,\xi;h)$ multiplied by Schwartz functions in $(x,y)$. Since $a \in S^m(T^*\mathbb{R}^n)$, for every $q$ there is a constant $C_{q,\Phi,a,h_0}>0$ such that
\begin{align*}
|B_\Phi(\xi;h)| \leq C_{q,\Phi,a,h_0}(1 + |\xi|^2h^{-2})^{-q}\langle \xi\rangle^m
\end{align*}
for all $\xi \in \mathbb{R}^n$ and all $h \in (0,h_0]$. Choosing $q$ with $2q>m+n$ gives $B_\Phi(\cdot;h) \in L^1(\mathbb{R}^n,d\mathcal{L}^n)$. Therefore dominated convergence gives
\begin{align*}
\lim_{\varepsilon \downarrow 0}K_{a,\varepsilon}(\Phi)=\frac{1}{(2\pi h)^n}\int_{\mathbb{R}^n}B_\Phi(\xi;h)\,d\mathcal{L}^n(\xi).
\end{align*}
This defines a continuous linear functional $K_a \in \mathcal{S}'(\mathbb{R}^n_x \times \mathbb{R}^n_y)$, and this distribution is precisely the oscillatory integral denoted by
\begin{align*}
K_a(x,y;h)=\frac{1}{(2\pi h)^n}\int_{\mathbb{R}^n}e^{i(x-y)\cdot \xi/h}a(x,\xi;h)\,d\mathcal{L}^n(\xi).
\end{align*}
[/step]