diff --git a/pySDC/helpers/spectral_helper.py b/pySDC/helpers/spectral_helper.py
index a801503a7c..25f177daad 100644
--- a/pySDC/helpers/spectral_helper.py
+++ b/pySDC/helpers/spectral_helper.py
@@ -2,6 +2,54 @@
 import scipy
 from pySDC.implementations.datatype_classes.mesh import mesh
 from scipy.special import factorial
+from functools import wraps
+
+
+def cache(func):
+    """
+    Decorator for caching return values of functions.
+    This is very similar to `functools.cache`, but without the memory leaks (see
+    https://docs.astral.sh/ruff/rules/cached-instance-method/).
+
+    Example:
+
+    .. code-block:: python
+
+        num_calls = 0
+
+        @cache
+        def increment(x):
+            num_calls += 1
+            return x + 1
+
+        increment(0)  # returns 1, num_calls = 1
+        increment(1)  # returns 2, num_calls = 2
+        increment(0)  # returns 1, num_calls = 2
+
+
+    Args:
+        func (function): The function you want to cache the return value of
+
+    Returns:
+        return value of func
+    """
+    attr_cache = f"_{func.__name__}_cache"
+
+    @wraps(func)
+    def wrapper(self, *args, **kwargs):
+        if not hasattr(self, attr_cache):
+            setattr(self, attr_cache, {})
+
+        cache = getattr(self, attr_cache)
+
+        key = (args, frozenset(kwargs.items()))
+        if key in cache:
+            return cache[key]
+        result = func(self, *args, **kwargs)
+        cache[key] = result
+        return result
+
+    return wrapper
 
 
 class SpectralHelper1D:
@@ -203,7 +251,6 @@ def __init__(self, *args, transform_type='fft', x0=-1, x1=1, **kwargs):
         if self.transform_type == 'fft':
             self.get_fft_utils()
 
-        self.cache = {}
         self.norm = self.get_norm()
 
     def get_1dgrid(self):
@@ -221,6 +268,7 @@ def get_wavenumbers(self):
         """Get the domain in spectral space"""
         return self.xp.arange(self.N)
 
+    @cache
     def get_conv(self, name, N=None):
         '''
         Get conversion matrix between different kinds of polynomials. The supported kinds are
@@ -238,9 +286,6 @@ def get_conv(self, name, N=None):
         Returns:
             scipy.sparse: Sparse conversion matrix
         '''
-        if name in self.cache.keys() and not N:
-            return self.cache[name]
-
         N = N if N else self.N
         sp = self.sparse_lib
         xp = self.xp
@@ -271,7 +316,6 @@ def get_forward_conv(name):
             except NotImplementedError:
                 raise NotImplementedError from E
 
-        self.cache[name] = mat
         return mat
 
     def get_basis_change_matrix(self, conv='T2T', **kwargs):
diff --git a/pySDC/tests/test_helpers/test_spectral_helper.py b/pySDC/tests/test_helpers/test_spectral_helper.py
index 3a2f9d2136..0e27874822 100644
--- a/pySDC/tests/test_helpers/test_spectral_helper.py
+++ b/pySDC/tests/test_helpers/test_spectral_helper.py
@@ -551,6 +551,30 @@ def test_dealias_MPI(num_procs, axis, bx, bz, nx=32, nz=64, **kwargs):
     run_MPI_test(num_procs=num_procs, axis=axis, nx=nx, nz=nz, bx=bx, bz=bz, test='dealias')
 
 
+@pytest.mark.base
+def test_cache_decorator():
+    from pySDC.helpers.spectral_helper import cache
+    import numpy as np
+
+    class Dummy:
+        num_calls = 0
+
+        @cache
+        def increment(self, x):
+            self.num_calls += 1
+            return x + 1
+
+    dummy = Dummy()
+    values = [0, 1, 1, 0, 3, 1, 2]
+    unique_vals = np.unique(values)
+
+    for x in values:
+        assert dummy.increment(x) == x + 1
+
+    assert dummy.num_calls < len(values)
+    assert dummy.num_calls == len(unique_vals)
+
+
 if __name__ == '__main__':
     str_to_bool = lambda me: False if me == 'False' else True
     str_to_tuple = lambda arg: tuple(int(me) for me in arg.split(','))