diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..c756d16
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Jason Ho
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
index 6c6867e..d1e9851 100644
--- a/README.md
+++ b/README.md
@@ -2,13 +2,14 @@
 **Authors:** Jason Ho, James. A. Boyle, Linshen Liu, and Andreas Gerstlauer \
 **Affiliation:** The University of Texas at Austin, System-Level Architecture and Modeling Group (SLAM Lab) \
 **Conference:** Machine Learning on Computer-Aided Design (MLCAD) 2025 \
-**Corresponding Contact**: jason_ho@utexas.edu
+**Corresponding Contact**: jason_ho@utexas.edu \
+**Code Ocean DOI**: [https://doi.org/10.24433/CO.1005356.v1](https://doi.org/10.24433/CO.1005356.v1)
 
 
 ### Abstract
 Neuromorphic systems using in-memory or event-driven computing are motivated by the need for more energy-efficient processing of artificial intelligence workloads. Emerging neuromorphic architectures aim to combine traditional digital designs with the computational efficiency of analog computing and novel device technologies. A crucial problem in the rapid exploration and co-design of such architectures is the lack of tools for fast and accurate modeling and simulation. Typical mixed-signal design tools integrate a digital simulator with an analog solver like SPICE, which is prohibitively slow for large systems. By contrast, behavioral modeling of analog components is faster, but existing approaches are fixed to specific architectures with limited energy and performance modeling. In this paper, we propose LASANA, a novel approach that leverages machine learning to derive data-driven surrogate models of analog sub-blocks in a digital backend architecture. LASANA uses SPICE-level simulations of a circuit to train ML models that predict circuit energy, performance, and behavior at analog/digital interfaces. Such models can provide energy and performance annotation on top of existing behavioral models or function as replacements to analog simulation. We apply LASANA to an analog crossbar array and a spiking neuron circuit. Running MNIST and spiking MNIST, LASANA surrogates demonstrate up to three orders of magnitude speedup over SPICE, with energy, latency, and behavioral error less than 7%, 8%, and 2%, respectively.
 
-[[arXiv]](https://arxiv.org/abs/2507.10748) [[Code Ocean]](optional-link)
+[[arXiv]](https://arxiv.org/abs/2507.10748) [[Code Ocean]](https://doi.org/10.24433/CO.1005356.v1)
 
 ---
 ### Overview
@@ -193,4 +194,4 @@ J. Ho, J. A. Boyle, L. Liu, and A. Gerstlauer, “LASANA: Large‑Scale Surrog
   year         = {2025},
   note         = {Accepted to MLCAD 2025},
   doi          = {10.48550/arXiv.2507.10748}
-}
\ No newline at end of file
+}