mpw-008 / CTM1 Contact

the CTM1 shapes of all of mpw-008

40 of 40 dies rendered.

449,550,329 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (155.9 MB in total, largest 9.2 MB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-008_CTM1_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-008_CTM1_reticle.png
the stitched reticle, full resolution
115.0 MB 4a0b9b1c9071d83325ae036d72f15b67c340cd99a45e11af9f87c15236b337a34ea0a6a8 e0ff7e97071dcdbe276905dbd736e37b7b482eaf97d6eb71f53fa69d286a590a
mpw-008_CTM1_reticle_overview.png
the same image scaled to fit 4096 px
1.7 MB 842c991424fea5d67d5ea81c5ccd90a74d9bbbf65edf7051712e2c5b6d59392c4d456231 f37a56321509b8e6f9990f31e86cedf1b1da0f7839cc39a2f3a206fafe9d6ece
mpw-008_CTM1_tiles.json
where every tile belongs, and its checksum
13.5 kB 2786ef6e6dd5376a70331f71f86175cbf6f8adde08f8fd37d57bb160c71a24547db85da6 0b72aaeba46171ca3bd5fe0ce7f9341bf3e0ddd4602b0aff3aaa30841b61f9c0
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 wishbone_pio 7576×107760, 0 3.8 MBb943d33f15624272d16685419d2fdf72dc7c89f47b59ca8099a2afd1314b9fae
A2 iit_indore_mm 7576×107767576, 0 3.0 MBe20f8799678733259f40b44dc2a51e908c2f9ac0b681556fca1bd2f2f8834152
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 2.9 MBc3fcd569501982e5fe238f8d9e9aa216a8ed010cda860bfcec755a93f43bb022
A4 riscduino-dcore_d4_ 7576×1077622728, 0 6.7 MB5f4de12b64d3a0e946953c0a3e9be3bfad4c4991322a70c893c8a53f8085977c
A5 riscduino-qcore_q3_ 7576×1077630304, 0 6.8 MBbe45b8520bdccf561d2a33510e4440739914bbf6fd9234dd4a36bc81e7c53fcb
A6 caravel_mpw8 7576×1077637880, 0 3.0 MBcc2e78b98668ca86901d19cfae0bf73e8544b17e39f655bd6993293c398ea38e
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 3.0 MB46599a9270dd7f1544d7b562de563a43247058f3da212b440d107fdd8a4a0435
B2 sramtestvehicleg4 7576×107767576, 10776 2.9 MBc4b11961500710a210e5b2359710abc77f0f575805a78066d7bce9f095ca4e14
B3 reram_crossbar_rerun 7576×1077615152, 10776 3.2 MB207a202b75a8fb4335fb7ae161e848cceda9fbe573eb5b3017b6e480aa094aed
B4 tinytapeout_skullart 7576×1077622728, 10776 4.2 MBa01080a9e238153834298ea28d665dac4ce413066ba51227f98d29846abfcf78
B5 soomrv__mpw-8_ 7576×1077630304, 10776 9.1 MB688e4690f07dbcde50a36f6afce20c5754a59828bb16c47df7dfc57db61645f7
B6 clock_divide_selector 7576×1077637880, 10776 2.9 MB5cc8294ab02b4c9d5f327dae37553424d73d97f03f3a46157b665efc4d61fa6d
B7 reram_module_mpw8 7976×1077645456, 10776 2.8 MBe6f5502e9318b9f9a2fc6b6adcb46dbc8e3521a184f61b14e4a313541c71f4fe
C1 shapingthewaves_8 7576×107760, 21552 5.3 MBb42fa02f638180ca78ced29bb438a6f55d986039610e68905d030b064cc6f946
C2 leosoc 7576×107767576, 21552 4.4 MBa589d67e6df51236c3e72dac62ef79f317a918f59ec390a4ceef52d4ff00bad8
C3 decrypt_aes128 7576×1077615152, 21552 4.4 MBd1e8656aa9ce96d28babed4b40e1dc7fb7ddfe3a89a94c01abec5fdb650e726c
C4 approximate_adder_axha 7576×1077622728, 21552 2.9 MB2416d6e5a43af1044c836ced8d19a3394c80f8f22385b53c5e15812c4d1a72a7
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 4.2 MB3688831329a55c6a73ebf602e26df06f55d1637547af2df1538bbafdc9e1954e
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 2.8 MB88b903aef0ee96ac6603c7a7c0162b1b7e182c904db550b94d2ae9a996c023fd
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 9.2 MB2ceef53eb553edbf45d886ced5dc190c86a4bb9b1ce75d46ca8b5eaf61703f6e
D2 hyperram_controller_for_sky130 7576×107767576, 32328 2.9 MBf84711ce0c6537ad2089b604e9bc27d32240d624712d0fbd774431569fb3e45d
D3 hardened_alu 7576×1077615152, 32328 3.1 MBa1528627c15170289b63ee6541866573398cc595303696cbcdb001d65bb89eb4
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 4.7 MBbb229eef01d3a38e4768f58d2c3732645b09f6129a14f87e43d085e87a112c92
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 2.7 MBe854481c43bb136866482cf8cc65c6fa9c0c4d6e872f8de1817578d42dd909f1
D6 gf180_vs_sky130 7576×1077637880, 32328 4.1 MB138a166f693b3bc1a98faa15d631cf1b852857a4e1de284777ea05912d21d974
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 3.0 MBb0bfd85ef2a121005667c8d9486cd93db82a88374b4480502fdcd9a3856525d7
E1 iiitb_sd_mpw8 7576×107760, 43104 3.0 MBbaa3b9b7b6601f4fd835eb75de3240f0fc4026d3f8b05cc6015ffee852c3426b
E2 iiitb_sfifo 7576×107767576, 43104 3.0 MB0bcdfc7b8591aa7e9c616e69fdc5fb79e062b293303c10d45e30e270b6920e08
E3 iiitb_alu 7576×1077615152, 43104 3.0 MB3430c7401a61ba32f745b5ae7b13755f12498166bf8092f28db507e0333fe580
E4 ppcpu2 7576×1077622728, 43104 6.7 MB344e8881495f8c1e140e18bae97c0228095b061eb6ea94223128df4ad5290109
E5 mega4_reram 7576×1077630304, 43104 3.9 MB96d67db3cc9d7e4d5daf4a953e696d60cd47d898f6674daf098b621020ee7b11
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 2.9 MB940a1fac2d602e47cb3502c2a2d772099d58f374c09a79c6e5a792864c75c985
E7 sky130cla 7976×1077645456, 43104 2.9 MBda6f4ee3ed823ea81cd381f5923d5a54c3abebfcd49d571517b052dc8e0ccfcc
F1 uetrv_esoc_v2 7576×111760, 53880 3.5 MBf3319feb0b8ee76a0e8a8e7c92693d291c8f3f821ee83dcfe1bfd87e2d676a0f
F2 sequencedetection1010 7576×111767576, 53880 2.9 MB0e3dbfe3f9bbec9ed9a15b6d84b30fea5aa2e15b48e0a2fac3047ab15ed0d5cd
F3 testasynctrimux-mpw8 7576×1117615152, 53880 2.9 MB420783e3abdd35680fb2bbb2325209c5ccf8ccfc124717875c8bcd856dfac02e
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 2.9 MB2e9aa824009448ea6f4dabf42fb01e3ac92ddfa6aa81ed5f7a99b9009e857543
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 3.0 MB854fffab644de31dc5f96692a69a14119cd8e722764767d1071f96ffeacce981
F6 arbiter_puf 7576×1117637880, 53880 2.9 MB978b3702335962cc76ee4c2ffcdd06ecf98eeb335996ca19d1d1343fd582f126
F7 unigate-sky 7976×1117645456, 53880 4.2 MBf66f6f1ab497613dbad02cd9b33cc3e22e57a81a183339b935a83e84d92f3c67

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About CTM1 on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask CTM1

Mask layer
CTM1 — Contact
Process step
107 — Metal contact mask
Drawn layer
67:44
Layer on the mask-layer GDS
35:0
Detail
CTM1 = mcon (li1->met1)
Also
Via 1 (LI/M0→M1)
Used in SKY130
yes

mpw-008 — SKY130 MPW-8

Reticle set
5CS8017AC
Run date
2022-11-19
Submissions closed
2022-12-31
Projects
144
Identification
inferred — Sequence after 8016=MPW-7 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-8 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-008/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.